diff --git a/results/denoising/data/dataset_info.json b/results/denoising/data/dataset_info.json
index dc288024..df7d4e60 100644
--- a/results/denoising/data/dataset_info.json
+++ b/results/denoising/data/dataset_info.json
@@ -1,38 +1,172 @@
 [
-    {
-        "dataset_name": "Pancreas (inDrop)",
-        "image": "openproblems-python-pytorch",
-        "data_url": "https://ndownloader.figshare.com/files/36086813",
-        "data_reference": "luecken2022benchmarking",
-        "dataset_summary": "Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Here we just use the inDrop1 batch, which includes1937 cells \u00d7 15502 genes.",
-        "task_id": "denoising",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "pancreas",
-        "source_dataset_id": "openproblems_v1/pancreas",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/denoising/datasets/pancreas.py"
-    },
-    {
-        "dataset_name": "1k Peripheral blood mononuclear cells",
-        "image": "openproblems-python-pytorch",
-        "data_url": "https://ndownloader.figshare.com/files/36088667",
-        "data_reference": "10x2018pbmc",
-        "dataset_summary": "1k Peripheral Blood Mononuclear Cells (PBMCs) from a healthy donor. Sequenced on 10X v3 chemistry in November 2018 by 10X Genomics.",
-        "task_id": "denoising",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "pbmc",
-        "source_dataset_id": "openproblems_v1/tenx_1k_pbmc",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/denoising/datasets/pbmc.py"
-    },
-    {
-        "dataset_name": "Tabula Muris Senis Lung",
-        "image": "openproblems-python-pytorch",
-        "data_url": "https://tabula-muris-senis.ds.czbiohub.org/",
-        "data_reference": "tabula2020single",
-        "dataset_summary": "All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 organs and tissues across the mouse lifespan. Here we use just 10x data from lung. 24540 cells \u00d7 16160 genes across 3 time points.",
-        "task_id": "denoising",
-        "commit_sha": "9d1665076cf6215a31f89ed2be8be20a02502887",
-        "dataset_id": "tabula_muris_senis_lung_random",
-        "source_dataset_id": "openproblems_v1/tabula_muris_senis_droplet_lung",
-        "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/main/openproblems/tasks/denoising/datasets/tabula_muris_senis.py"
-    }
-]
\ No newline at end of file
+  {
+    "dataset_id": "openproblems_v1/pancreas",
+    "dataset_name": "Human pancreas",
+    "dataset_summary": "Human pancreas cells dataset from the scIB benchmarks",
+    "dataset_description": "Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq).",
+    "data_reference": "luecken2022benchmarking",
+    "data_url": "https://theislab.github.io/scib-reproducibility/dataset_pancreas.html",
+    "date_created": "19-12-2024",
+    "file_size": 14734440
+  },
+  {
+    "dataset_id": "cellxgene_census/mouse_pancreas_atlas",
+    "dataset_name": "Mouse Pancreatic Islet Atlas",
+    "dataset_summary": "Mouse pancreatic islet scRNA-seq atlas across sexes, ages, and stress conditions including diabetes",
+    "dataset_description": "To better understand pancreatic β-cell heterogeneity we generated a mouse pancreatic islet atlas capturing a wide range of biological conditions. The atlas contains scRNA-seq datasets of over 300,000 mouse pancreatic islet cells, of which more than 100,000 are β-cells, from nine datasets with 56 samples, including two previously unpublished datasets. The samples vary in sex, age (ranging from embryonic to aged), chemical stress, and disease status (including T1D NOD model development and two T2D models, mSTZ and db/db) together with different diabetes treatments. Additional information about data fields is available in anndata uns field 'field_descriptions' and on https://github.com/theislab/mm_pancreas_atlas_rep/blob/main/resources/cellxgene.md.",
+    "data_reference": "hrovatin2023delineating",
+    "data_url": "https://cellxgene.cziscience.com/collections/296237e2-393d-4e31-b590-b03f74ac5070",
+    "date_created": "19-12-2024",
+    "file_size": 58395968
+  },
+  {
+    "dataset_id": "openproblems_v1/cengen",
+    "dataset_name": "CeNGEN",
+    "dataset_summary": "Complete Gene Expression Map of an Entire Nervous System",
+    "dataset_description": "100k FACS-isolated C. elegans neurons from 17 experiments sequenced on 10x Genomics.",
+    "data_reference": "hammarlund2018cengen",
+    "data_url": "https://www.cengen.org",
+    "date_created": "19-12-2024",
+    "file_size": 12760976
+  },
+  {
+    "dataset_id": "openproblems_v1/tenx_1k_pbmc",
+    "dataset_name": "1k PBMCs",
+    "dataset_summary": "1k peripheral blood mononuclear cells from a healthy donor",
+    "dataset_description": "1k Peripheral Blood Mononuclear Cells (PBMCs) from a healthy donor. Sequenced on 10X v3 chemistry in November 2018 by 10X Genomics.",
+    "data_reference": "10x2018pbmc",
+    "data_url": "https://www.10xgenomics.com/resources/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0",
+    "date_created": "19-12-2024",
+    "file_size": 6217680
+  },
+  {
+    "dataset_id": "openproblems_v1/tnbc_wu2021",
+    "dataset_name": "Triple-Negative Breast Cancer",
+    "dataset_summary": "1535 cells from six fresh triple-negative breast cancer tumors.",
+    "dataset_description": "1535 cells from six TNBC donors by (Wu et al., 2021). This dataset includes cytokine activities, inferred using a multivariate linear model with cytokine-focused signatures, as assumed true cell-cell communication (Dimitrov et al., 2022).",
+    "data_reference": "wu2021single",
+    "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE118389",
+    "date_created": "19-12-2024",
+    "file_size": 46471680
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "dataset_name": "Diabetic Kidney Disease",
+    "dataset_summary": "Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression",
+    "dataset_description": "Multimodal single cell sequencing is a powerful tool for interrogating cell-specific changes in transcription and chromatin accessibility. We performed single nucleus RNA (snRNA-seq) and assay for transposase accessible chromatin sequencing (snATAC-seq) on human kidney cortex from donors with and without diabetic kidney disease (DKD) to identify altered signaling pathways and transcription factors associated with DKD. Both snRNA-seq and snATAC-seq had an increased proportion of VCAM1+ injured proximal tubule cells (PT_VCAM1) in DKD samples. PT_VCAM1 has a pro-inflammatory expression signature and transcription factor motif enrichment implicated NFkB signaling. We used stratified linkage disequilibrium score regression to partition heritability of kidney-function-related traits using publicly-available GWAS summary statistics. Cell-specific PT_VCAM1 peaks were enriched for heritability of chronic kidney disease (CKD), suggesting that genetic background may regulate chromatin accessibility and DKD progression. snATAC-seq found cell-specific differentially accessible regions (DAR) throughout the nephron that change accessibility in DKD and these regions were enriched for glucocorticoid receptor (GR) motifs. Changes in chromatin accessibility were associated with decreased expression of insulin receptor, increased gluconeogenesis, and decreased expression of the GR cytosolic chaperone, FKBP5, in the diabetic proximal tubule. Cleavage under targets and release using nuclease (CUT&RUN) profiling of GR binding in bulk kidney cortex and an in vitro model of the proximal tubule (RPTEC) showed that DAR co-localize with GR binding sites. CRISPRi silencing of GR response elements (GRE) in the FKBP5 gene body reduced FKBP5 expression in RPTEC, suggesting that reduced FKBP5 chromatin accessibility in DKD may alter cellular response to GR. We developed an open-source tool for single cell allele specific analysis (SALSA) to model the effect of genetic background on gene expression. Heterozygous germline single nucleotide variants (SNV) in proximal tubule ATAC peaks were associated with allele-specific chromatin accessibility and differential expression of target genes within cis-coaccessibility networks. Partitioned heritability of proximal tubule ATAC peaks with a predicted allele-specific effect was enriched for eGFR, suggesting that genetic background may modify DKD progression in a cell-specific manner.",
+    "data_reference": "wilson2022multimodal",
+    "data_url": "https://cellxgene.cziscience.com/collections/b3e2c6e3-9b05-4da9-8f42-da38a664b45b",
+    "date_created": "19-12-2024",
+    "file_size": 50805456
+  },
+  {
+    "dataset_id": "cellxgene_census/hcla",
+    "dataset_name": "Human Lung Cell Atlas",
+    "dataset_summary": "An integrated cell atlas of the human lung in health and disease (core)",
+    "dataset_description": "The integrated Human Lung Cell Atlas (HLCA) represents the first large-scale, integrated single-cell reference atlas of the human lung. It consists of over 2 million cells from the respiratory tract of 486 individuals, and includes 49 different datasets. It is split into the HLCA core, and the extended or full HLCA. The HLCA core includes data of healthy lung tissue from 107 individuals, and includes manual cell type annotations based on consensus across 6 independent experts, as well as demographic, biological and technical metadata.",
+    "data_reference": "sikkema2023integrated",
+    "data_url": "https://cellxgene.cziscience.com/collections/6f6d381a-7701-4781-935c-db10d30de293",
+    "date_created": "19-12-2024",
+    "file_size": 77918688
+  },
+  {
+    "dataset_id": "openproblems_v1/tenx_5k_pbmc",
+    "dataset_name": "5k PBMCs",
+    "dataset_summary": "5k peripheral blood mononuclear cells from a healthy donor",
+    "dataset_description": "5k Peripheral Blood Mononuclear Cells (PBMCs) from a healthy donor. Sequenced on 10X v3 chemistry in July 2019 by 10X Genomics.",
+    "data_reference": "10x2019pbmc",
+    "data_url": "https://www.10xgenomics.com/resources/datasets/5-k-peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-with-cell-surface-proteins-v-3-chemistry-3-1-standard-3-1-0",
+    "date_created": "19-12-2024",
+    "file_size": 22471584
+  },
+  {
+    "dataset_id": "openproblems_v1/mouse_hspc_nestorowa2016",
+    "dataset_name": "Mouse HSPC",
+    "dataset_summary": "Haematopoeitic stem and progenitor cells from mouse bone marrow",
+    "dataset_description": "1656 hematopoietic stem and progenitor cells from mouse bone marrow. Sequenced by Smart-seq2.",
+    "data_reference": "nestorowa2016single",
+    "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE81682",
+    "date_created": "19-12-2024",
+    "file_size": 140202724
+  },
+  {
+    "dataset_id": "cellxgene_census/gtex_v9",
+    "dataset_name": "GTEX v9",
+    "dataset_summary": "Single-nucleus cross-tissue molecular reference maps to decipher disease gene function",
+    "dataset_description": "Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.",
+    "data_reference": "eraslan2022singlenucleus",
+    "data_url": "https://cellxgene.cziscience.com/collections/a3ffde6c-7ad2-498a-903c-d58e732f7470",
+    "date_created": "19-12-2024",
+    "file_size": 15792224
+  },
+  {
+    "dataset_id": "openproblems_v1/mouse_blood_olsson_labelled",
+    "dataset_name": "Mouse myeloid",
+    "dataset_summary": "Myeloid lineage differentiation from mouse blood",
+    "dataset_description": "660 FACS-isolated myeloid cells from 9 experiments sequenced using C1 Fluidigm and SMARTseq in 2016 by Olsson et al.",
+    "data_reference": "olsson2016single",
+    "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70245",
+    "date_created": "19-12-2024",
+    "file_size": 19666052
+  },
+  {
+    "dataset_id": "cellxgene_census/hypomap",
+    "dataset_name": "HypoMap",
+    "dataset_summary": "A unified single cell gene expression atlas of the murine hypothalamus",
+    "dataset_description": "The hypothalamus plays a key role in coordinating fundamental body functions. Despite recent progress in single-cell technologies, a unified catalogue and molecular characterization of the heterogeneous cell types and, specifically, neuronal subtypes in this brain region are still lacking. Here we present an integrated reference atlas “HypoMap” of the murine hypothalamus consisting of 384,925 cells, with the ability to incorporate new additional experiments. We validate HypoMap by comparing data collected from SmartSeq2 and bulk RNA sequencing of selected neuronal cell types with different degrees of cellular heterogeneity.",
+    "data_reference": "steuernagel2022hypomap",
+    "data_url": "https://cellxgene.cziscience.com/collections/d86517f0-fa7e-4266-b82e-a521350d6d36",
+    "date_created": "19-12-2024",
+    "file_size": 46841520
+  },
+  {
+    "dataset_id": "cellxgene_census/immune_cell_atlas",
+    "dataset_name": "Immune Cell Atlas",
+    "dataset_summary": "Cross-tissue immune cell analysis reveals tissue-specific features in humans",
+    "dataset_description": "Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis, and antigen receptor sequencing.",
+    "data_reference": "dominguez2022crosstissue",
+    "data_url": "https://cellxgene.cziscience.com/collections/62ef75e4-cbea-454e-a0ce-998ec40223d3",
+    "date_created": "19-12-2024",
+    "file_size": 59557448
+  },
+  {
+    "dataset_id": "openproblems_v1/allen_brain_atlas",
+    "dataset_name": "Mouse Brain Atlas",
+    "dataset_summary": "Adult mouse primary visual cortex",
+    "dataset_description": "A murine brain atlas with adjacent cell types as assumed benchmark truth, inferred from deconvolution proportion correlations using matching 10x Visium slides (see Dimitrov et al., 2022).",
+    "data_reference": "tasic2016adult",
+    "data_url": "http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71585",
+    "date_created": "19-12-2024",
+    "file_size": 851626584
+  },
+  {
+    "dataset_id": "openproblems_v1/immune_cells",
+    "dataset_name": "Human immune",
+    "dataset_summary": "Human immune cells dataset from the scIB benchmarks",
+    "dataset_description": "Human immune cells from peripheral blood and bone marrow taken from 5 datasets comprising 10 batches across technologies (10X, Smart-seq2).",
+    "data_reference": "luecken2022benchmarking",
+    "data_url": "https://theislab.github.io/scib-reproducibility/dataset_immune_cell_hum.html",
+    "date_created": "19-12-2024",
+    "file_size": 47439616
+  },
+  {
+    "dataset_id": "cellxgene_census/tabula_sapiens",
+    "dataset_name": "Tabula Sapiens",
+    "dataset_summary": "A multiple-organ, single-cell transcriptomic atlas of humans",
+    "dataset_description": "Tabula Sapiens is a benchmark, first-draft human cell atlas of nearly 500,000 cells from 24 organs of 15 normal human subjects. This work is the product of the Tabula Sapiens Consortium. Taking the organs from the same individual controls for genetic background, age, environment, and epigenetic effects and allows detailed analysis and comparison of cell types that are shared between tissues. Our work creates a detailed portrait of cell types as well as their distribution and variation in gene expression across tissues and within the endothelial, epithelial, stromal and immune compartments.",
+    "data_reference": "consortium2022tabula",
+    "data_url": "https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5",
+    "date_created": "19-12-2024",
+    "file_size": 72851720
+  },
+  {
+    "dataset_id": "openproblems_v1/zebrafish",
+    "dataset_name": "Zebrafish embryonic cells",
+    "dataset_summary": "Single-cell mRNA sequencing of zebrafish embryonic cells.",
+    "dataset_description": "90k cells from zebrafish embryos throughout the first day of development, with and without a knockout of chordin, an important developmental gene.",
+    "data_reference": "wagner2018single",
+    "data_url": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112294",
+    "date_created": "19-12-2024",
+    "file_size": 51152176
+  }
+]
diff --git a/results/denoising/data/method_info.json b/results/denoising/data/method_info.json
index c5961134..443d2852 100644
--- a/results/denoising/data/method_info.json
+++ b/results/denoising/data/method_info.json
@@ -1,197 +1,114 @@
 [
   {
-    "method_name": "ALRA (log norm)",
-    "method_summary": "ALRA (Adaptively-thresholded Low Rank Approximation) is a method for imputation of missing values in single cell RNA-sequencing data. Given a normalised scRNA-seq expression matrix, it first imputes values using rank-k approximation, using singular value decomposition. Next, a symmetric distribution is fitted to the near-zero imputed values for each gene (row) of the matrix. The right \u201ctail\u201d of this distribution is then used to threshold the accepted nonzero entries. This same threshold is then used to rescale the matrix, once the \u201cbiological zeros\u201d have been removed.",
-    "paper_name": "Zero-preserving imputation of scRNA-seq data using low-rank approximation",
-    "paper_reference": "linderman2018zero",
-    "paper_year": 2018,
-    "code_url": "https://github.com/KlugerLab/ALRA/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "alra_log",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/alra.py"
-  },
-  {
-    "method_name": "ALRA (log norm, reversed normalization)",
-    "method_summary": "ALRA (Adaptively-thresholded Low Rank Approximation) is a method for imputation of missing values in single cell RNA-sequencing data. Given a normalised scRNA-seq expression matrix, it first imputes values using rank-k approximation, using singular value decomposition. Next, a symmetric distribution is fitted to the near-zero imputed values for each gene (row) of the matrix. The right \u201ctail\u201d of this distribution is then used to threshold the accepted nonzero entries. This same threshold is then used to rescale the matrix, once the \u201cbiological zeros\u201d have been removed.",
-    "paper_name": "Zero-preserving imputation of scRNA-seq data using low-rank approximation",
-    "paper_reference": "linderman2018zero",
-    "paper_year": 2018,
-    "code_url": "https://github.com/KlugerLab/ALRA/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "alra_log_reversenorm",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/alra.py"
+    "task_id": "control_methods",
+    "method_id": "no_denoising",
+    "method_name": "No Denoising",
+    "method_summary": "negative control by copying train counts",
+    "method_description": "This method serves as a negative control, where the denoised data is a copy of the unaltered training data. This represents the scoring threshold if denoising was not performed on the data.",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_denoising",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_denoising/control_methods/no_denoising:1.0.0",
+    "implementation_url": "https://github.com/openproblems-bio/task_denoising/blob/252731bc7276eb8a6a3398dc4bea026ae70eca80/src/control_methods/no_denoising",
+    "code_version": "1.0.0",
+    "commit_sha": "252731bc7276eb8a6a3398dc4bea026ae70eca80"
   },
   {
-    "method_name": "ALRA (sqrt norm)",
-    "method_summary": "ALRA (Adaptively-thresholded Low Rank Approximation) is a method for imputation of missing values in single cell RNA-sequencing data. Given a normalised scRNA-seq expression matrix, it first imputes values using rank-k approximation, using singular value decomposition. Next, a symmetric distribution is fitted to the near-zero imputed values for each gene (row) of the matrix. The right \u201ctail\u201d of this distribution is then used to threshold the accepted nonzero entries. This same threshold is then used to rescale the matrix, once the \u201cbiological zeros\u201d have been removed.",
-    "paper_name": "Zero-preserving imputation of scRNA-seq data using low-rank approximation",
-    "paper_reference": "linderman2018zero",
-    "paper_year": 2018,
-    "code_url": "https://github.com/KlugerLab/ALRA/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "alra_sqrt",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/alra.py"
+    "task_id": "control_methods",
+    "method_id": "perfect_denoising",
+    "method_name": "Perfect Denoising",
+    "method_summary": "Positive control by copying the test counts",
+    "method_description": "This method serves as a positive control, where the test data is copied 1-to-1 to the denoised data. This makes it seem as if the data is perfectly denoised as it will be compared to the test data in the metrics.",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_denoising",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_denoising/control_methods/perfect_denoising:1.0.0",
+    "implementation_url": "https://github.com/openproblems-bio/task_denoising/blob/252731bc7276eb8a6a3398dc4bea026ae70eca80/src/control_methods/perfect_denoising",
+    "code_version": "1.0.0",
+    "commit_sha": "252731bc7276eb8a6a3398dc4bea026ae70eca80"
   },
   {
-    "method_name": "ALRA (sqrt norm, reversed normalization)",
-    "method_summary": "ALRA (Adaptively-thresholded Low Rank Approximation) is a method for imputation of missing values in single cell RNA-sequencing data. Given a normalised scRNA-seq expression matrix, it first imputes values using rank-k approximation, using singular value decomposition. Next, a symmetric distribution is fitted to the near-zero imputed values for each gene (row) of the matrix. The right \u201ctail\u201d of this distribution is then used to threshold the accepted nonzero entries. This same threshold is then used to rescale the matrix, once the \u201cbiological zeros\u201d have been removed.",
-    "paper_name": "Zero-preserving imputation of scRNA-seq data using low-rank approximation",
-    "paper_reference": "linderman2018zero",
-    "paper_year": 2018,
-    "code_url": "https://github.com/KlugerLab/ALRA/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-r-extras",
+    "task_id": "methods",
+    "method_id": "alra",
+    "method_name": "ALRA",
+    "method_summary": "ALRA imputes missing values in scRNA-seq data by computing rank-k approximation, thresholding by gene, and rescaling the matrix.",
+    "method_description": "Adaptively-thresholded Low Rank Approximation (ALRA). \n\nALRA is a method for imputation of missing values in single cell RNA-sequencing data, \ndescribed in the preprint, \"Zero-preserving imputation of scRNA-seq data using low-rank approximation\" \navailable [here](https://www.biorxiv.org/content/early/2018/08/22/397588). Given a \nscRNA-seq expression matrix, ALRA first computes its rank-k approximation using randomized SVD. \nNext, each row (gene) is thresholded by the magnitude of the most negative value of that gene. \nFinally, the matrix is rescaled.\n",
     "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "alra_sqrt_reversenorm",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/alra.py"
+    "references_doi": "10.1101/397588",
+    "references_bibtex": null,
+    "code_url": "https://github.com/KlugerLab/ALRA",
+    "documentation_url": "https://github.com/KlugerLab/ALRA/blob/master/README.md",
+    "image": "https://ghcr.io/openproblems-bio/task_denoising/methods/alra:1.0.0",
+    "implementation_url": "https://github.com/openproblems-bio/task_denoising/blob/252731bc7276eb8a6a3398dc4bea026ae70eca80/src/methods/alra",
+    "code_version": "1.0.0",
+    "commit_sha": "252731bc7276eb8a6a3398dc4bea026ae70eca80"
   },
   {
-    "method_name": "DCA",
-    "method_summary": "DCA (Deep Count Autoencoder) is a method to remove the effect of dropout in scRNA-seq data. DCA takes into account the count structure, overdispersed nature and sparsity of scRNA-seq datatypes using a deep autoencoder with a zero-inflated negative binomial (ZINB) loss. The autoencoder is then applied to the dataset, where the mean of the fitted negative binomial distributions is used to fill each entry of the imputed matrix.",
-    "paper_name": "Single-cell RNA-seq denoising using a deep count autoencoder",
-    "paper_reference": "eraslan2019single",
-    "paper_year": 2019,
-    "code_url": "https://github.com/theislab/dca/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-tensorflow",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
+    "task_id": "methods",
     "method_id": "dca",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/dca.py"
-  },
-  {
-    "method_name": "KNN smoothing",
-    "method_summary": "KNN-smoothing is a method for denoising data based on the k-nearest neighbours. Given a normalised scRNA-seq matrix, KNN-smoothing calculates a k-nearest neighbour matrix using Euclidean distances between cell pairs. Each cell\u2019s denoised expression is then defined as the average expression of each of its neighbours.",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras",
+    "method_name": "DCA",
+    "method_summary": "A deep autoencoder with ZINB loss function to address the dropout effect in count data",
+    "method_description": "\"Deep Count Autoencoder\n\nRemoves the dropout effect by taking the count structure, overdispersed nature and sparsity of the data into account \nusing a deep autoencoder with zero-inflated negative binomial (ZINB) loss function.\"\n",
     "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "knn_naive",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/magic.py"
+    "references_doi": "10.1038/s41467-018-07931-2",
+    "references_bibtex": null,
+    "code_url": "https://github.com/theislab/dca",
+    "documentation_url": "https://github.com/theislab/dca#readme",
+    "image": "https://ghcr.io/openproblems-bio/task_denoising/methods/dca:1.0.0",
+    "implementation_url": "https://github.com/openproblems-bio/task_denoising/blob/252731bc7276eb8a6a3398dc4bea026ae70eca80/src/methods/dca",
+    "code_version": "1.0.0",
+    "commit_sha": "252731bc7276eb8a6a3398dc4bea026ae70eca80"
   },
   {
-    "method_name": "Iterative KNN smoothing",
-    "method_summary": "Iterative kNN-smoothing is a method to repair or denoise noisy scRNA-seq expression matrices. Given a scRNA-seq expression matrix, KNN-smoothing first applies initial normalisation and smoothing. Then, a chosen number of principal components is used to calculate Euclidean distances between cells. Minimally sized neighbourhoods are initially determined from these Euclidean distances, and expression profiles are shared between neighbouring cells. Then, the resultant smoothed matrix is used as input to the next step of smoothing, where the size (k) of the considered neighbourhoods is increased, leading to greater smoothing. This process continues until a chosen maximum k value has been reached, at which point the iteratively smoothed object is then optionally scaled to yield a final result.",
-    "paper_name": "K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data",
-    "paper_reference": "wagner2018knearest",
-    "paper_year": 2018,
-    "code_url": "https://github.com/yanailab/knn-smoothing/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
+    "task_id": "methods",
     "method_id": "knn_smoothing",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/knn_smoothing.py"
-  },
-  {
-    "method_name": "MAGIC",
-    "method_summary": "MAGIC (Markov Affinity-based Graph Imputation of Cells) is a method for imputation and denoising of noisy or dropout-prone single cell RNA-sequencing data. Given a normalised scRNA-seq expression matrix, it first calculates Euclidean distances between each pair of cells in the dataset, which is then augmented using a Gaussian kernel (function) and row-normalised to give a normalised affinity matrix. A t-step markov process is then calculated, by powering this affinity matrix t times. Finally, the powered affinity matrix is right-multiplied by the normalised data, causing the final imputed values to take the value of a per-gene average weighted by the affinities of cells. The resultant imputed matrix is then rescaled, to more closely match the magnitude of measurements in the normalised (input) matrix.",
-    "paper_name": "Recovering Gene Interactions from Single-Cell Data Using Data Diffusion",
-    "paper_reference": "van2018recovering",
-    "paper_year": 2018,
-    "code_url": "https://github.com/KrishnaswamyLab/MAGIC/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras",
-    "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "magic",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/magic.py"
-  },
-  {
-    "method_name": "MAGIC (approximate)",
-    "method_summary": "MAGIC (Markov Affinity-based Graph Imputation of Cells) is a method for imputation and denoising of noisy or dropout-prone single cell RNA-sequencing data. Given a normalised scRNA-seq expression matrix, it first calculates Euclidean distances between each pair of cells in the dataset, which is then augmented using a Gaussian kernel (function) and row-normalised to give a normalised affinity matrix. A t-step markov process is then calculated, by powering this affinity matrix t times. Finally, the powered affinity matrix is right-multiplied by the normalised data, causing the final imputed values to take the value of a per-gene average weighted by the affinities of cells. The resultant imputed matrix is then rescaled, to more closely match the magnitude of measurements in the normalised (input) matrix.",
-    "paper_name": "Recovering Gene Interactions from Single-Cell Data Using Data Diffusion",
-    "paper_reference": "van2018recovering",
-    "paper_year": 2018,
-    "code_url": "https://github.com/KrishnaswamyLab/MAGIC/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras",
+    "method_name": "KNN Smoothing",
+    "method_summary": "Iterative kNN-smoothing denoises scRNA-seq data by iteratively increasing the size of neighbourhoods for smoothing until a maximum k value is reached.",
+    "method_description": "Iterative kNN-smoothing is a method to repair or denoise noisy scRNA-seq expression matrices. Given a scRNA-seq expression matrix, KNN-smoothing first applies initial normalisation and smoothing. Then, a chosen number of principal components is used to calculate Euclidean distances between cells. Minimally sized neighbourhoods are initially determined from these Euclidean distances, and expression profiles are shared between neighbouring cells. Then, the resultant smoothed matrix is used as input to the next step of smoothing, where the size (k) of the considered neighbourhoods is increased, leading to greater smoothing. This process continues until a chosen maximum k value has been reached, at which point the iteratively smoothed object is then optionally scaled to yield a final result.",
     "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "magic_approx",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/magic.py"
+    "references_doi": "10.1101/217737",
+    "references_bibtex": null,
+    "code_url": "https://github.com/yanailab/knn-smoothing",
+    "documentation_url": "https://github.com/yanailab/knn-smoothing#readme",
+    "image": "https://ghcr.io/openproblems-bio/task_denoising/methods/knn_smoothing:1.0.0",
+    "implementation_url": "https://github.com/openproblems-bio/task_denoising/blob/252731bc7276eb8a6a3398dc4bea026ae70eca80/src/methods/knn_smoothing",
+    "code_version": "1.0.0",
+    "commit_sha": "252731bc7276eb8a6a3398dc4bea026ae70eca80"
   },
   {
-    "method_name": "MAGIC (approximate, reversed normalization)",
-    "method_summary": "MAGIC (Markov Affinity-based Graph Imputation of Cells) is a method for imputation and denoising of noisy or dropout-prone single cell RNA-sequencing data. Given a normalised scRNA-seq expression matrix, it first calculates Euclidean distances between each pair of cells in the dataset, which is then augmented using a Gaussian kernel (function) and row-normalised to give a normalised affinity matrix. A t-step markov process is then calculated, by powering this affinity matrix t times. Finally, the powered affinity matrix is right-multiplied by the normalised data, causing the final imputed values to take the value of a per-gene average weighted by the affinities of cells. The resultant imputed matrix is then rescaled, to more closely match the magnitude of measurements in the normalised (input) matrix.",
-    "paper_name": "Recovering Gene Interactions from Single-Cell Data Using Data Diffusion",
-    "paper_reference": "van2018recovering",
-    "paper_year": 2018,
-    "code_url": "https://github.com/KrishnaswamyLab/MAGIC/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras",
+    "task_id": "methods",
+    "method_id": "magic",
+    "method_name": "MAGIC",
+    "method_summary": "MAGIC imputes and denoises scRNA-seq data that is noisy or dropout-prone.",
+    "method_description": "MAGIC (Markov Affinity-based Graph Imputation of Cells) is a method for imputation and denoising of noisy or dropout-prone single cell RNA-sequencing data. Given a normalised scRNA-seq expression matrix, it first calculates Euclidean distances between each pair of cells in the dataset, which is then augmented using a Gaussian kernel (function) and row-normalised to give a normalised affinity matrix. A t-step markov process is then calculated, by powering this affinity matrix t times. Finally, the powered affinity matrix is right-multiplied by the normalised data, causing the final imputed values to take the value of a per-gene average weighted by the affinities of cells. The resultant imputed matrix is then rescaled, to more closely match the magnitude of measurements in the normalised (input) matrix.",
     "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "magic_approx_reverse_norm",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/magic.py"
+    "references_doi": "10.1016/j.cell.2018.05.061",
+    "references_bibtex": null,
+    "code_url": "https://github.com/KrishnaswamyLab/MAGIC",
+    "documentation_url": "https://github.com/KrishnaswamyLab/MAGIC#readme",
+    "image": "https://ghcr.io/openproblems-bio/task_denoising/methods/magic:1.0.0",
+    "implementation_url": "https://github.com/openproblems-bio/task_denoising/blob/252731bc7276eb8a6a3398dc4bea026ae70eca80/src/methods/magic",
+    "code_version": "1.0.0",
+    "commit_sha": "252731bc7276eb8a6a3398dc4bea026ae70eca80"
   },
   {
-    "method_name": "MAGIC (reversed normalization)",
-    "method_summary": "MAGIC (Markov Affinity-based Graph Imputation of Cells) is a method for imputation and denoising of noisy or dropout-prone single cell RNA-sequencing data. Given a normalised scRNA-seq expression matrix, it first calculates Euclidean distances between each pair of cells in the dataset, which is then augmented using a Gaussian kernel (function) and row-normalised to give a normalised affinity matrix. A t-step markov process is then calculated, by powering this affinity matrix t times. Finally, the powered affinity matrix is right-multiplied by the normalised data, causing the final imputed values to take the value of a per-gene average weighted by the affinities of cells. The resultant imputed matrix is then rescaled, to more closely match the magnitude of measurements in the normalised (input) matrix.",
-    "paper_name": "Recovering Gene Interactions from Single-Cell Data Using Data Diffusion",
-    "paper_reference": "van2018recovering",
-    "paper_year": 2018,
-    "code_url": "https://github.com/KrishnaswamyLab/MAGIC/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-extras",
+    "task_id": "methods",
+    "method_id": "scprint",
+    "method_name": "scPRINT",
+    "method_summary": "scPRINT is a large transformer model built for the inference of gene networks",
+    "method_description": "scPRINT is a large transformer model built for the inference of gene networks\n(connections between genes explaining the cell's expression profile) from\nscRNAseq data.\n\nIt uses novel encoding and decoding of the cell expression profile and new\npre-training methodologies to learn a cell model.\n\nscPRINT can be used to perform the following analyses:\n\n- expression denoising: increase the resolution of your scRNAseq data\n- cell embedding: generate a low-dimensional representation of your dataset\n- label prediction: predict the cell type, disease, sequencer, sex, and\n  ethnicity of your cells\n- gene network inference: generate a gene network from any cell or cell\n  cluster in your scRNAseq dataset\n",
     "is_baseline": false,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "magic_reverse_norm",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/magic.py"
-  },
-  {
-    "method_name": "No denoising",
-    "method_summary": "Denoised outputs are defined from the unmodified input data.",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "no_denoising",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/baseline.py"
-  },
-  {
-    "method_name": "Perfect denoising",
-    "method_summary": "Denoised outputs are defined from the target data.",
-    "paper_name": "Open Problems for Single Cell Analysis",
-    "paper_reference": "openproblems",
-    "paper_year": 2022,
-    "code_url": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks",
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "is_baseline": true,
-    "code_version": "v1.0.0",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
-    "method_id": "perfect_denoising",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/methods/baseline.py"
+    "references_doi": "10.1101/2024.07.29.605556",
+    "references_bibtex": null,
+    "code_url": "https://github.com/cantinilab/scPRINT",
+    "documentation_url": "https://cantinilab.github.io/scPRINT/",
+    "image": "https://ghcr.io/openproblems-bio/task_denoising/methods/scprint:1.0.0",
+    "implementation_url": "https://github.com/openproblems-bio/task_denoising/blob/252731bc7276eb8a6a3398dc4bea026ae70eca80/src/methods/scprint",
+    "code_version": "1.0.0",
+    "commit_sha": "252731bc7276eb8a6a3398dc4bea026ae70eca80"
   }
-]
\ No newline at end of file
+]
diff --git a/results/denoising/data/metric_execution_info.json b/results/denoising/data/metric_execution_info.json
new file mode 100644
index 00000000..eeb3f3ec
--- /dev/null
+++ b/results/denoising/data/metric_execution_info.json
@@ -0,0 +1,2858 @@
+[
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "alra",
+    "metric_component_name": "mse",
+    "resources": {
+      "submit": "2024-12-19 18:51:54",
+      "exit_code": 0,
+      "duration_sec": 17.7,
+      "cpu_pct": 119.9,
+      "peak_memory_mb": 7885,
+      "disk_read_mb": 867,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "alra",
+    "metric_component_name": "poisson",
+    "resources": {
+      "submit": "2024-12-19 18:51:54",
+      "exit_code": 0,
+      "duration_sec": 18.1,
+      "cpu_pct": 158.5,
+      "peak_memory_mb": 14848,
+      "disk_read_mb": 850,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "dca",
+    "metric_component_name": "mse",
+    "resources": {
+      "submit": "2024-12-19 18:44:55",
+      "exit_code": 0,
+      "duration_sec": 20.7,
+      "cpu_pct": 143.6,
+      "peak_memory_mb": 12493,
+      "disk_read_mb": 990,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "dca",
+    "metric_component_name": "poisson",
+    "resources": {
+      "submit": "2024-12-19 18:44:55",
+      "exit_code": 0,
+      "duration_sec": 11.1,
+      "cpu_pct": 163.3,
+      "peak_memory_mb": 14029,
+      "disk_read_mb": 974,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "knn_smoothing",
+    "metric_component_name": "mse",
+    "resources": {
+      "submit": "2024-12-19 18:45:25",
+      "exit_code": 0,
+      "duration_sec": 19.7,
+      "cpu_pct": 136,
+      "peak_memory_mb": 6554,
+      "disk_read_mb": 234,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "knn_smoothing",
+    "metric_component_name": "poisson",
+    "resources": {
+      "submit": "2024-12-19 18:45:25",
+      "exit_code": 0,
+      "duration_sec": 17.7,
+      "cpu_pct": 144.5,
+      "peak_memory_mb": 13517,
+      "disk_read_mb": 218,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "magic",
+    "metric_component_name": "mse",
+    "resources": {
+      "submit": "2024-12-19 18:44:24",
+      "exit_code": 0,
+      "duration_sec": 22.4,
+      "cpu_pct": 99,
+      "peak_memory_mb": 7885,
+      "disk_read_mb": 1536,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "magic",
+    "metric_component_name": "poisson",
+    "resources": {
+      "submit": "2024-12-19 18:44:24",
+      "exit_code": 0,
+      "duration_sec": 23.6,
+      "cpu_pct": 133.9,
+      "peak_memory_mb": 14848,
+      "disk_read_mb": 1536,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "no_denoising",
+    "metric_component_name": "mse",
+    "resources": {
+      "submit": "2024-12-19 18:41:35",
+      "exit_code": 0,
+      "duration_sec": 8.6,
+      "cpu_pct": 175.1,
+      "peak_memory_mb": 12186,
+      "disk_read_mb": 198,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "cellxgene_census/dkd",
+    "method_id": "no_denoising",
+    "metric_component_name": "poisson",
+    "resources": {
+      "submit": "2024-12-19 18:41:34",
+      "exit_code": 0,
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+      "exit_code": 0,
+      "duration_sec": 29.1,
+      "cpu_pct": 116.2,
+      "peak_memory_mb": 7988,
+      "disk_read_mb": 1741,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/zebrafish",
+    "method_id": "magic",
+    "metric_component_name": "poisson",
+    "resources": {
+      "submit": "2024-12-19 18:57:55",
+      "exit_code": 0,
+      "duration_sec": 16.1,
+      "cpu_pct": 132,
+      "peak_memory_mb": 15258,
+      "disk_read_mb": 1741,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/zebrafish",
+    "method_id": "no_denoising",
+    "metric_component_name": "mse",
+    "resources": {
+      "submit": "2024-12-19 18:56:24",
+      "exit_code": 0,
+      "duration_sec": 10.9,
+      "cpu_pct": 165.5,
+      "peak_memory_mb": 11060,
+      "disk_read_mb": 186,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/zebrafish",
+    "method_id": "no_denoising",
+    "metric_component_name": "poisson",
+    "resources": {
+      "submit": "2024-12-19 18:56:25",
+      "exit_code": 0,
+      "duration_sec": 13.2,
+      "cpu_pct": 149.2,
+      "peak_memory_mb": 12391,
+      "disk_read_mb": 169,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/zebrafish",
+    "method_id": "perfect_denoising",
+    "metric_component_name": "mse",
+    "resources": {
+      "submit": "2024-12-19 18:54:55",
+      "exit_code": 0,
+      "duration_sec": 10,
+      "cpu_pct": 149.8,
+      "peak_memory_mb": 10752,
+      "disk_read_mb": 151,
+      "disk_write_mb": 1
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/zebrafish",
+    "method_id": "perfect_denoising",
+    "metric_component_name": "poisson",
+    "resources": {
+      "submit": "2024-12-19 18:54:54",
+      "exit_code": 0,
+      "duration_sec": 14.1,
+      "cpu_pct": 168.1,
+      "peak_memory_mb": 13620,
+      "disk_read_mb": 134,
+      "disk_write_mb": 1
+    }
+  }
+]
diff --git a/results/denoising/data/metric_info.json b/results/denoising/data/metric_info.json
index 99cc11c1..d765561e 100644
--- a/results/denoising/data/metric_info.json
+++ b/results/denoising/data/metric_info.json
@@ -1,26 +1,32 @@
 [
   {
-    "metric_name": "Mean-squared error",
-    "metric_summary": "The mean squared error between the denoised counts of the training dataset and the true counts of the test dataset after reweighting by the train/test ratio.",
-    "paper_reference": "batson2019molecular",
-    "maximize": false,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
+    "task_id": "metrics",
+    "component_name": "mse",
     "metric_id": "mse",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/metrics/mse.py",
-    "code_version": "v1.0.0"
+    "metric_name": "Mean-squared error",
+    "metric_summary": "The mean squared error between the denoised counts and the true counts.",
+    "metric_description": "The mean squared error between the denoised counts of the training dataset and the true counts of the test dataset after reweighing by the train/test ratio",
+    "references_doi": "10.1101/786269",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_denoising/blob/252731bc7276eb8a6a3398dc4bea026ae70eca80/src/metrics/mse",
+    "image": "https://ghcr.io/openproblems-bio/task_denoising/metrics/mse:1.0.0",
+    "code_version": "1.0.0",
+    "commit_sha": "252731bc7276eb8a6a3398dc4bea026ae70eca80",
+    "maximize": false
   },
   {
-    "metric_name": "Poisson loss",
-    "metric_summary": "The Poisson log likelihood of observing the true counts of the test dataset given the distribution given in the denoised dataset.",
-    "paper_reference": "batson2019molecular",
-    "maximize": false,
-    "image": "https://github.com/openproblems-bio/openproblems/pkgs/container/openproblems-python-pytorch",
-    "task_id": "denoising",
-    "commit_sha": "b3456fd73c04c28516f6df34c57e6e3e8b0dab32",
+    "task_id": "metrics",
+    "component_name": "poisson",
     "metric_id": "poisson",
-    "implementation_url": "https://github.com/openproblems-bio/openproblems/blob/v1.0.0/openproblems/tasks/denoising/metrics/poisson.py",
-    "code_version": "v1.0.0"
+    "metric_name": "Poisson Loss",
+    "metric_summary": "The Poisson log likelihood of the true counts observed in the distribution of denoised counts",
+    "metric_description": "The Poisson log likelihood of observing the true counts of the test dataset given the distribution given in the denoised dataset.",
+    "references_doi": "10.1101/786269",
+    "references_bibtex": null,
+    "implementation_url": "https://github.com/openproblems-bio/task_denoising/blob/252731bc7276eb8a6a3398dc4bea026ae70eca80/src/metrics/poisson",
+    "image": "https://ghcr.io/openproblems-bio/task_denoising/metrics/poisson:1.0.0",
+    "code_version": "1.0.0",
+    "commit_sha": "252731bc7276eb8a6a3398dc4bea026ae70eca80",
+    "maximize": false
   }
-]
\ No newline at end of file
+]
diff --git a/results/denoising/data/quality_control.json b/results/denoising/data/quality_control.json
index d2bb908a..63aedc3c 100644
--- a/results/denoising/data/quality_control.json
+++ b/results/denoising/data/quality_control.json
@@ -1,862 +1,792 @@
 [
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Task info", 
         "name": "Pct 'task_id' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_id' should be defined\n  Task id: denoising\n  Field: task_id\n"
+        "message": "Task metadata field 'task_id' should be defined\n  Task id: task_denoising\n  Field: task_id\n"
     }, 
     {
-        "task_id": "denoising", 
-        "category": "Task info", 
-        "name": "Pct 'commit_sha' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
-        "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'commit_sha' should be defined\n  Task id: denoising\n  Field: commit_sha\n"
-    }, 
-    {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Task info", 
         "name": "Pct 'task_name' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_name' should be defined\n  Task id: denoising\n  Field: task_name\n"
+        "message": "Task metadata field 'task_name' should be defined\n  Task id: task_denoising\n  Field: task_name\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Task info", 
         "name": "Pct 'task_summary' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_summary' should be defined\n  Task id: denoising\n  Field: task_summary\n"
+        "message": "Task metadata field 'task_summary' should be defined\n  Task id: task_denoising\n  Field: task_summary\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Task info", 
         "name": "Pct 'task_description' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing([task_info], field)", 
-        "message": "Task metadata field 'task_description' should be defined\n  Task id: denoising\n  Field: task_description\n"
+        "message": "Task metadata field 'task_description' should be defined\n  Task id: task_denoising\n  Field: task_description\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Method info", 
         "name": "Pct 'task_id' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'task_id' should be defined\n  Task id: denoising\n  Field: task_id\n"
+        "message": "Method metadata field 'task_id' should be defined\n  Task id: task_denoising\n  Field: task_id\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Method info", 
         "name": "Pct 'commit_sha' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'commit_sha' should be defined\n  Task id: denoising\n  Field: commit_sha\n"
+        "message": "Method metadata field 'commit_sha' should be defined\n  Task id: task_denoising\n  Field: commit_sha\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Method info", 
         "name": "Pct 'method_id' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_id' should be defined\n  Task id: denoising\n  Field: method_id\n"
+        "message": "Method metadata field 'method_id' should be defined\n  Task id: task_denoising\n  Field: method_id\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Method info", 
         "name": "Pct 'method_name' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_name' should be defined\n  Task id: denoising\n  Field: method_name\n"
+        "message": "Method metadata field 'method_name' should be defined\n  Task id: task_denoising\n  Field: method_name\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Method info", 
         "name": "Pct 'method_summary' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'method_summary' should be defined\n  Task id: denoising\n  Field: method_summary\n"
+        "message": "Method metadata field 'method_summary' should be defined\n  Task id: task_denoising\n  Field: method_summary\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Method info", 
         "name": "Pct 'paper_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
+        "value": 0.7142857142857143, 
+        "severity": 2, 
+        "severity_value": 3.0, 
         "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'paper_reference' should be defined\n  Task id: denoising\n  Field: paper_reference\n"
+        "message": "Method metadata field 'paper_reference' should be defined\n  Task id: task_denoising\n  Field: paper_reference\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Method info", 
         "name": "Pct 'is_baseline' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(method_info, field)", 
-        "message": "Method metadata field 'is_baseline' should be defined\n  Task id: denoising\n  Field: is_baseline\n"
+        "message": "Method metadata field 'is_baseline' should be defined\n  Task id: task_denoising\n  Field: is_baseline\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Metric info", 
         "name": "Pct 'task_id' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'task_id' should be defined\n  Task id: denoising\n  Field: task_id\n"
+        "message": "Metric metadata field 'task_id' should be defined\n  Task id: task_denoising\n  Field: task_id\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Metric info", 
         "name": "Pct 'commit_sha' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'commit_sha' should be defined\n  Task id: denoising\n  Field: commit_sha\n"
+        "message": "Metric metadata field 'commit_sha' should be defined\n  Task id: task_denoising\n  Field: commit_sha\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Metric info", 
         "name": "Pct 'metric_id' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_id' should be defined\n  Task id: denoising\n  Field: metric_id\n"
+        "message": "Metric metadata field 'metric_id' should be defined\n  Task id: task_denoising\n  Field: metric_id\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Metric info", 
         "name": "Pct 'metric_name' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_name' should be defined\n  Task id: denoising\n  Field: metric_name\n"
+        "message": "Metric metadata field 'metric_name' should be defined\n  Task id: task_denoising\n  Field: metric_name\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Metric info", 
         "name": "Pct 'metric_summary' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'metric_summary' should be defined\n  Task id: denoising\n  Field: metric_summary\n"
+        "message": "Metric metadata field 'metric_summary' should be defined\n  Task id: task_denoising\n  Field: metric_summary\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Metric info", 
         "name": "Pct 'paper_reference' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
+        "value": 1.0, 
+        "severity": 2, 
+        "severity_value": 3.0, 
         "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'paper_reference' should be defined\n  Task id: denoising\n  Field: paper_reference\n"
+        "message": "Metric metadata field 'paper_reference' should be defined\n  Task id: task_denoising\n  Field: paper_reference\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Metric info", 
         "name": "Pct 'maximize' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(metric_info, field)", 
-        "message": "Metric metadata field 'maximize' should be defined\n  Task id: denoising\n  Field: maximize\n"
+        "message": "Metric metadata field 'maximize' should be defined\n  Task id: task_denoising\n  Field: maximize\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Dataset info", 
         "name": "Pct 'task_id' missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
+        "value": 1.0, 
+        "severity": 2, 
+        "severity_value": 3.0, 
         "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'task_id' should be defined\n  Task id: denoising\n  Field: task_id\n"
+        "message": "Dataset metadata field 'task_id' should be defined\n  Task id: task_denoising\n  Field: task_id\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Dataset info", 
-        "name": "Pct 'commit_sha' missing", 
+        "name": "Pct 'dataset_id' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'commit_sha' should be defined\n  Task id: denoising\n  Field: commit_sha\n"
+        "message": "Dataset metadata field 'dataset_id' should be defined\n  Task id: task_denoising\n  Field: dataset_id\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Dataset info", 
-        "name": "Pct 'dataset_id' missing", 
+        "name": "Pct 'dataset_name' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_id' should be defined\n  Task id: denoising\n  Field: dataset_id\n"
+        "message": "Dataset metadata field 'dataset_name' should be defined\n  Task id: task_denoising\n  Field: dataset_name\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Dataset info", 
-        "name": "Pct 'dataset_name' missing", 
+        "name": "Pct 'dataset_summary' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_name' should be defined\n  Task id: denoising\n  Field: dataset_name\n"
+        "message": "Dataset metadata field 'dataset_summary' should be defined\n  Task id: task_denoising\n  Field: dataset_summary\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Dataset info", 
-        "name": "Pct 'dataset_summary' missing", 
+        "name": "Pct 'data_reference' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'dataset_summary' should be defined\n  Task id: denoising\n  Field: dataset_summary\n"
+        "message": "Dataset metadata field 'data_reference' should be defined\n  Task id: task_denoising\n  Field: data_reference\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Dataset info", 
-        "name": "Pct 'data_reference' missing", 
+        "name": "Pct 'data_url' missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "percent_missing(dataset_info, field)", 
-        "message": "Dataset metadata field 'data_reference' should be defined\n  Task id: denoising\n  Field: data_reference\n"
+        "message": "Dataset metadata field 'data_url' should be defined\n  Task id: task_denoising\n  Field: data_url\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw data", 
         "name": "Number of results", 
-        "value": 39, 
+        "value": 119, 
         "severity": 0, 
-        "severity_value": -1.8181818181818188, 
+        "severity_value": 0.0, 
         "code": "len(results) == len(method_info) * len(metric_info) * len(dataset_info)", 
-        "message": "Number of results should be equal to #methods × #metrics × #datasets.\n  Task id: denoising\n  Number of results: 39\n  Number of methods: 11\n  Number of metrics: 2\n  Number of datasets: 3\n"
+        "message": "Number of results should be equal to #methods × #metrics × #datasets.\n  Task id: task_denoising\n  Number of results: 119\n  Number of methods: 7\n  Number of metrics: 2\n  Number of datasets: 17\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
         "name": "Metric 'mse' %missing", 
-        "value": -0.18181818181818188, 
-        "severity": 0, 
-        "severity_value": -1.8181818181818188, 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  Metric id: mse\n  Percentage missing: -18%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  Metric id: mse\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
         "name": "Metric 'poisson' %missing", 
-        "value": -0.18181818181818188, 
-        "severity": 0, 
-        "severity_value": -1.8181818181818188, 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  Metric id: poisson\n  Percentage missing: -18%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  Metric id: poisson\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Method 'alra_log' %missing", 
+        "name": "Method 'no_denoising' %missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: alra_log\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  method id: no_denoising\n  Percentage missing: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Method 'alra_sqrt' %missing", 
+        "name": "Method 'perfect_denoising' %missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: alra_sqrt\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  method id: perfect_denoising\n  Percentage missing: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Method 'dca' %missing", 
+        "name": "Method 'alra' %missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: dca\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  method id: alra\n  Percentage missing: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Method 'knn_naive' %missing", 
+        "name": "Method 'dca' %missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: knn_naive\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  method id: dca\n  Percentage missing: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
         "name": "Method 'knn_smoothing' %missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: knn_smoothing\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  method id: knn_smoothing\n  Percentage missing: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
         "name": "Method 'magic' %missing", 
         "value": 0.0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: magic\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  method id: magic\n  Percentage missing: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Method 'magic_approx' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
+        "name": "Method 'scprint' %missing", 
+        "value": 1.0, 
+        "severity": 3, 
+        "severity_value": 10.0, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: magic_approx\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  method id: scprint\n  Percentage missing: 100%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Method 'magic_approx_reverse_norm' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
+        "name": "Dataset 'openproblems_v1/pancreas' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: magic_approx_reverse_norm\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: openproblems_v1/pancreas\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Method 'magic_reverse_norm' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
+        "name": "Dataset 'cellxgene_census/mouse_pancreas_atlas' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: magic_reverse_norm\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: cellxgene_census/mouse_pancreas_atlas\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Method 'no_denoising' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
+        "name": "Dataset 'openproblems_v1/cengen' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: no_denoising\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: openproblems_v1/cengen\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Method 'perfect_denoising' %missing", 
-        "value": 0.0, 
-        "severity": 0, 
-        "severity_value": 0.0, 
+        "name": "Dataset 'openproblems_v1/tenx_1k_pbmc' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  method id: perfect_denoising\n  Percentage missing: 0%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: openproblems_v1/tenx_1k_pbmc\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Dataset 'pancreas' %missing", 
-        "value": -0.18181818181818188, 
-        "severity": 0, 
-        "severity_value": -1.8181818181818188, 
+        "name": "Dataset 'openproblems_v1/tnbc_wu2021' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  dataset id: pancreas\n  Percentage missing: -18%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: openproblems_v1/tnbc_wu2021\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Dataset 'pbmc' %missing", 
-        "value": -0.18181818181818188, 
-        "severity": 0, 
-        "severity_value": -1.8181818181818188, 
+        "name": "Dataset 'cellxgene_census/dkd' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  dataset id: pbmc\n  Percentage missing: -18%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: cellxgene_census/dkd\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Raw results", 
-        "name": "Dataset 'tabula_muris_senis_lung_random' %missing", 
-        "value": -0.18181818181818188, 
-        "severity": 0, 
-        "severity_value": -1.8181818181818188, 
+        "name": "Dataset 'cellxgene_census/hcla' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
         "code": "pct_missing <= .1", 
-        "message": "Percentage of missing results should be less than 10%.\n  Task id: denoising\n  dataset id: tabula_muris_senis_lung_random\n  Percentage missing: -18%\n"
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: cellxgene_census/hcla\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Worst score alra_log mse", 
-        "value": -0.08824883049777621, 
-        "severity": 0, 
-        "severity_value": 0.08824883049777621, 
-        "code": "worst_score >= -1", 
-        "message": "Method alra_log performs much worse than baselines.\n  Task id: denoising\n  Method id: alra_log\n  Metric id: mse\n  Worst score: -0.08824883049777621%\n"
+        "task_id": "task_denoising", 
+        "category": "Raw results", 
+        "name": "Dataset 'openproblems_v1/tenx_5k_pbmc' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: openproblems_v1/tenx_5k_pbmc\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Best score alra_log mse", 
-        "value": -0.0432249510912992, 
-        "severity": 0, 
-        "severity_value": -0.0216124755456496, 
-        "code": "best_score <= 2", 
-        "message": "Method alra_log performs a lot better than baselines.\n  Task id: denoising\n  Method id: alra_log\n  Metric id: mse\n  Best score: -0.0432249510912992%\n"
+        "task_id": "task_denoising", 
+        "category": "Raw results", 
+        "name": "Dataset 'openproblems_v1/mouse_hspc_nestorowa2016' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: openproblems_v1/mouse_hspc_nestorowa2016\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Worst score alra_sqrt mse", 
-        "value": -0.04036796164907197, 
-        "severity": 0, 
-        "severity_value": 0.04036796164907197, 
-        "code": "worst_score >= -1", 
-        "message": "Method alra_sqrt performs much worse than baselines.\n  Task id: denoising\n  Method id: alra_sqrt\n  Metric id: mse\n  Worst score: -0.04036796164907197%\n"
+        "task_id": "task_denoising", 
+        "category": "Raw results", 
+        "name": "Dataset 'cellxgene_census/gtex_v9' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: cellxgene_census/gtex_v9\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Best score alra_sqrt mse", 
-        "value": 0.027482608312493717, 
-        "severity": 0, 
-        "severity_value": 0.013741304156246859, 
-        "code": "best_score <= 2", 
-        "message": "Method alra_sqrt performs a lot better than baselines.\n  Task id: denoising\n  Method id: alra_sqrt\n  Metric id: mse\n  Best score: 0.027482608312493717%\n"
+        "task_id": "task_denoising", 
+        "category": "Raw results", 
+        "name": "Dataset 'openproblems_v1/mouse_blood_olsson_labelled' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: openproblems_v1/mouse_blood_olsson_labelled\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Worst score dca mse", 
-        "value": 0.11691012188156669, 
-        "severity": 0, 
-        "severity_value": -0.11691012188156669, 
-        "code": "worst_score >= -1", 
-        "message": "Method dca performs much worse than baselines.\n  Task id: denoising\n  Method id: dca\n  Metric id: mse\n  Worst score: 0.11691012188156669%\n"
+        "task_id": "task_denoising", 
+        "category": "Raw results", 
+        "name": "Dataset 'cellxgene_census/hypomap' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: cellxgene_census/hypomap\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Best score dca mse", 
-        "value": 0.19457688013784935, 
-        "severity": 0, 
-        "severity_value": 0.09728844006892468, 
-        "code": "best_score <= 2", 
-        "message": "Method dca performs a lot better than baselines.\n  Task id: denoising\n  Method id: dca\n  Metric id: mse\n  Best score: 0.19457688013784935%\n"
+        "task_id": "task_denoising", 
+        "category": "Raw results", 
+        "name": "Dataset 'cellxgene_census/immune_cell_atlas' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: cellxgene_census/immune_cell_atlas\n  Percentage missing: 14%\n"
+    }, 
+    {
+        "task_id": "task_denoising", 
+        "category": "Raw results", 
+        "name": "Dataset 'openproblems_v1/allen_brain_atlas' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: openproblems_v1/allen_brain_atlas\n  Percentage missing: 14%\n"
+    }, 
+    {
+        "task_id": "task_denoising", 
+        "category": "Raw results", 
+        "name": "Dataset 'openproblems_v1/immune_cells' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: openproblems_v1/immune_cells\n  Percentage missing: 14%\n"
+    }, 
+    {
+        "task_id": "task_denoising", 
+        "category": "Raw results", 
+        "name": "Dataset 'cellxgene_census/tabula_sapiens' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: cellxgene_census/tabula_sapiens\n  Percentage missing: 14%\n"
+    }, 
+    {
+        "task_id": "task_denoising", 
+        "category": "Raw results", 
+        "name": "Dataset 'openproblems_v1/zebrafish' %missing", 
+        "value": 0.1428571428571429, 
+        "severity": 1, 
+        "severity_value": 1.428571428571429, 
+        "code": "pct_missing <= .1", 
+        "message": "Percentage of missing results should be less than 10%.\n  Task id: task_denoising\n  dataset id: openproblems_v1/zebrafish\n  Percentage missing: 14%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score knn_naive mse", 
-        "value": 0.08175097425019529, 
+        "name": "Worst score no_denoising mse", 
+        "value": 0, 
         "severity": 0, 
-        "severity_value": -0.08175097425019529, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method knn_naive performs much worse than baselines.\n  Task id: denoising\n  Method id: knn_naive\n  Metric id: mse\n  Worst score: 0.08175097425019529%\n"
+        "message": "Method no_denoising performs much worse than baselines.\n  Task id: task_denoising\n  Method id: no_denoising\n  Metric id: mse\n  Worst score: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score knn_naive mse", 
-        "value": 0.16114857838168273, 
+        "name": "Best score no_denoising mse", 
+        "value": 0, 
         "severity": 0, 
-        "severity_value": 0.08057428919084136, 
+        "severity_value": 0.0, 
         "code": "best_score <= 2", 
-        "message": "Method knn_naive performs a lot better than baselines.\n  Task id: denoising\n  Method id: knn_naive\n  Metric id: mse\n  Best score: 0.16114857838168273%\n"
+        "message": "Method no_denoising performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: no_denoising\n  Metric id: mse\n  Best score: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score knn_smoothing mse", 
-        "value": 0.09082714173836992, 
+        "name": "Worst score perfect_denoising mse", 
+        "value": 1, 
         "severity": 0, 
-        "severity_value": -0.09082714173836992, 
+        "severity_value": -1.0, 
         "code": "worst_score >= -1", 
-        "message": "Method knn_smoothing performs much worse than baselines.\n  Task id: denoising\n  Method id: knn_smoothing\n  Metric id: mse\n  Worst score: 0.09082714173836992%\n"
+        "message": "Method perfect_denoising performs much worse than baselines.\n  Task id: task_denoising\n  Method id: perfect_denoising\n  Metric id: mse\n  Worst score: 1%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score knn_smoothing mse", 
-        "value": 0.17426517380173512, 
+        "name": "Best score perfect_denoising mse", 
+        "value": 1, 
         "severity": 0, 
-        "severity_value": 0.08713258690086756, 
+        "severity_value": 0.5, 
         "code": "best_score <= 2", 
-        "message": "Method knn_smoothing performs a lot better than baselines.\n  Task id: denoising\n  Method id: knn_smoothing\n  Metric id: mse\n  Best score: 0.17426517380173512%\n"
+        "message": "Method perfect_denoising performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: perfect_denoising\n  Metric id: mse\n  Best score: 1%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score magic mse", 
-        "value": 0.23906123480992536, 
-        "severity": 0, 
-        "severity_value": -0.23906123480992536, 
+        "name": "Worst score alra mse", 
+        "value": -9.7088, 
+        "severity": 3, 
+        "severity_value": 9.7088, 
         "code": "worst_score >= -1", 
-        "message": "Method magic performs much worse than baselines.\n  Task id: denoising\n  Method id: magic\n  Metric id: mse\n  Worst score: 0.23906123480992536%\n"
+        "message": "Method alra performs much worse than baselines.\n  Task id: task_denoising\n  Method id: alra\n  Metric id: mse\n  Worst score: -9.7088%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score magic mse", 
-        "value": 0.3037404140348672, 
+        "name": "Best score alra mse", 
+        "value": 0.0163, 
         "severity": 0, 
-        "severity_value": 0.1518702070174336, 
+        "severity_value": 0.00815, 
         "code": "best_score <= 2", 
-        "message": "Method magic performs a lot better than baselines.\n  Task id: denoising\n  Method id: magic\n  Metric id: mse\n  Best score: 0.3037404140348672%\n"
+        "message": "Method alra performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: alra\n  Metric id: mse\n  Best score: 0.0163%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score magic_approx mse", 
-        "value": 0.2389228519856721, 
-        "severity": 0, 
-        "severity_value": -0.2389228519856721, 
+        "name": "Worst score dca mse", 
+        "value": -8.4495, 
+        "severity": 3, 
+        "severity_value": 8.4495, 
         "code": "worst_score >= -1", 
-        "message": "Method magic_approx performs much worse than baselines.\n  Task id: denoising\n  Method id: magic_approx\n  Metric id: mse\n  Worst score: 0.2389228519856721%\n"
+        "message": "Method dca performs much worse than baselines.\n  Task id: task_denoising\n  Method id: dca\n  Metric id: mse\n  Worst score: -8.4495%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score magic_approx mse", 
-        "value": 0.3042795795970009, 
+        "name": "Best score dca mse", 
+        "value": 0.2002, 
         "severity": 0, 
-        "severity_value": 0.15213978979850046, 
+        "severity_value": 0.1001, 
         "code": "best_score <= 2", 
-        "message": "Method magic_approx performs a lot better than baselines.\n  Task id: denoising\n  Method id: magic_approx\n  Metric id: mse\n  Best score: 0.3042795795970009%\n"
+        "message": "Method dca performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: dca\n  Metric id: mse\n  Best score: 0.2002%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score magic_approx_reverse_norm mse", 
-        "value": 0.24000776413948743, 
-        "severity": 0, 
-        "severity_value": -0.24000776413948743, 
+        "name": "Worst score knn_smoothing mse", 
+        "value": -7.4691, 
+        "severity": 3, 
+        "severity_value": 7.4691, 
         "code": "worst_score >= -1", 
-        "message": "Method magic_approx_reverse_norm performs much worse than baselines.\n  Task id: denoising\n  Method id: magic_approx_reverse_norm\n  Metric id: mse\n  Worst score: 0.24000776413948743%\n"
+        "message": "Method knn_smoothing performs much worse than baselines.\n  Task id: task_denoising\n  Method id: knn_smoothing\n  Metric id: mse\n  Worst score: -7.4691%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score magic_approx_reverse_norm mse", 
-        "value": 0.30381809668430537, 
+        "name": "Best score knn_smoothing mse", 
+        "value": 0.1747, 
         "severity": 0, 
-        "severity_value": 0.15190904834215269, 
+        "severity_value": 0.08735, 
         "code": "best_score <= 2", 
-        "message": "Method magic_approx_reverse_norm performs a lot better than baselines.\n  Task id: denoising\n  Method id: magic_approx_reverse_norm\n  Metric id: mse\n  Best score: 0.30381809668430537%\n"
+        "message": "Method knn_smoothing performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: knn_smoothing\n  Metric id: mse\n  Best score: 0.1747%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score magic_reverse_norm mse", 
-        "value": 0.24054344382246362, 
-        "severity": 0, 
-        "severity_value": -0.24054344382246362, 
+        "name": "Worst score magic mse", 
+        "value": -7.6469, 
+        "severity": 3, 
+        "severity_value": 7.6469, 
         "code": "worst_score >= -1", 
-        "message": "Method magic_reverse_norm performs much worse than baselines.\n  Task id: denoising\n  Method id: magic_reverse_norm\n  Metric id: mse\n  Worst score: 0.24054344382246362%\n"
+        "message": "Method magic performs much worse than baselines.\n  Task id: task_denoising\n  Method id: magic\n  Metric id: mse\n  Worst score: -7.6469%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score magic_reverse_norm mse", 
-        "value": 0.3033183444568984, 
+        "name": "Best score magic mse", 
+        "value": 0.203, 
         "severity": 0, 
-        "severity_value": 0.1516591722284492, 
+        "severity_value": 0.1015, 
         "code": "best_score <= 2", 
-        "message": "Method magic_reverse_norm performs a lot better than baselines.\n  Task id: denoising\n  Method id: magic_reverse_norm\n  Metric id: mse\n  Best score: 0.3033183444568984%\n"
+        "message": "Method magic performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: magic\n  Metric id: mse\n  Best score: 0.203%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score no_denoising mse", 
-        "value": 0.0, 
+        "name": "Worst score scprint mse", 
+        "value": 0, 
         "severity": 0, 
         "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method no_denoising performs much worse than baselines.\n  Task id: denoising\n  Method id: no_denoising\n  Metric id: mse\n  Worst score: 0.0%\n"
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_denoising\n  Method id: scprint\n  Metric id: mse\n  Worst score: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score no_denoising mse", 
-        "value": 0.0, 
+        "name": "Best score scprint mse", 
+        "value": 0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "best_score <= 2", 
-        "message": "Method no_denoising performs a lot better than baselines.\n  Task id: denoising\n  Method id: no_denoising\n  Metric id: mse\n  Best score: 0.0%\n"
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: scprint\n  Metric id: mse\n  Best score: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score perfect_denoising mse", 
-        "value": 1.0, 
+        "name": "Worst score no_denoising poisson", 
+        "value": 0, 
         "severity": 0, 
-        "severity_value": -1.0, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method perfect_denoising performs much worse than baselines.\n  Task id: denoising\n  Method id: perfect_denoising\n  Metric id: mse\n  Worst score: 1.0%\n"
+        "message": "Method no_denoising performs much worse than baselines.\n  Task id: task_denoising\n  Method id: no_denoising\n  Metric id: poisson\n  Worst score: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score perfect_denoising mse", 
-        "value": 1.0, 
+        "name": "Best score no_denoising poisson", 
+        "value": 1, 
         "severity": 0, 
         "severity_value": 0.5, 
         "code": "best_score <= 2", 
-        "message": "Method perfect_denoising performs a lot better than baselines.\n  Task id: denoising\n  Method id: perfect_denoising\n  Metric id: mse\n  Best score: 1.0%\n"
+        "message": "Method no_denoising performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: no_denoising\n  Metric id: poisson\n  Best score: 1%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score alra_log poisson", 
-        "value": -0.4910232707982263, 
+        "name": "Worst score perfect_denoising poisson", 
+        "value": 0, 
         "severity": 0, 
-        "severity_value": 0.4910232707982263, 
+        "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method alra_log performs much worse than baselines.\n  Task id: denoising\n  Method id: alra_log\n  Metric id: poisson\n  Worst score: -0.4910232707982263%\n"
+        "message": "Method perfect_denoising performs much worse than baselines.\n  Task id: task_denoising\n  Method id: perfect_denoising\n  Metric id: poisson\n  Worst score: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score alra_log poisson", 
-        "value": -0.17080706376936683, 
+        "name": "Best score perfect_denoising poisson", 
+        "value": 1, 
         "severity": 0, 
-        "severity_value": -0.08540353188468341, 
+        "severity_value": 0.5, 
         "code": "best_score <= 2", 
-        "message": "Method alra_log performs a lot better than baselines.\n  Task id: denoising\n  Method id: alra_log\n  Metric id: poisson\n  Best score: -0.17080706376936683%\n"
+        "message": "Method perfect_denoising performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: perfect_denoising\n  Metric id: poisson\n  Best score: 1%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score alra_sqrt poisson", 
-        "value": -2.3012026201185467, 
-        "severity": 2, 
-        "severity_value": 2.3012026201185467, 
+        "name": "Worst score alra poisson", 
+        "value": -6.6588, 
+        "severity": 3, 
+        "severity_value": 6.6588, 
         "code": "worst_score >= -1", 
-        "message": "Method alra_sqrt performs much worse than baselines.\n  Task id: denoising\n  Method id: alra_sqrt\n  Metric id: poisson\n  Worst score: -2.3012026201185467%\n"
+        "message": "Method alra performs much worse than baselines.\n  Task id: task_denoising\n  Method id: alra\n  Metric id: poisson\n  Worst score: -6.6588%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score alra_sqrt poisson", 
-        "value": -1.7521723656810537, 
+        "name": "Best score alra poisson", 
+        "value": 0.5077, 
         "severity": 0, 
-        "severity_value": -0.8760861828405269, 
+        "severity_value": 0.25385, 
         "code": "best_score <= 2", 
-        "message": "Method alra_sqrt performs a lot better than baselines.\n  Task id: denoising\n  Method id: alra_sqrt\n  Metric id: poisson\n  Best score: -1.7521723656810537%\n"
+        "message": "Method alra performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: alra\n  Metric id: poisson\n  Best score: 0.5077%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
         "name": "Worst score dca poisson", 
-        "value": -0.07602158378601098, 
+        "value": -0.823, 
         "severity": 0, 
-        "severity_value": 0.07602158378601098, 
+        "severity_value": 0.823, 
         "code": "worst_score >= -1", 
-        "message": "Method dca performs much worse than baselines.\n  Task id: denoising\n  Method id: dca\n  Metric id: poisson\n  Worst score: -0.07602158378601098%\n"
+        "message": "Method dca performs much worse than baselines.\n  Task id: task_denoising\n  Method id: dca\n  Metric id: poisson\n  Worst score: -0.823%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
         "name": "Best score dca poisson", 
-        "value": -0.03348925480745857, 
+        "value": 0.4456, 
         "severity": 0, 
-        "severity_value": -0.016744627403729284, 
+        "severity_value": 0.2228, 
         "code": "best_score <= 2", 
-        "message": "Method dca performs a lot better than baselines.\n  Task id: denoising\n  Method id: dca\n  Metric id: poisson\n  Best score: -0.03348925480745857%\n"
+        "message": "Method dca performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: dca\n  Metric id: poisson\n  Best score: 0.4456%\n"
     }, 
     {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Worst score knn_naive poisson", 
-        "value": -0.07984726373877193, 
-        "severity": 0, 
-        "severity_value": 0.07984726373877193, 
-        "code": "worst_score >= -1", 
-        "message": "Method knn_naive performs much worse than baselines.\n  Task id: denoising\n  Method id: knn_naive\n  Metric id: poisson\n  Worst score: -0.07984726373877193%\n"
-    }, 
-    {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Best score knn_naive poisson", 
-        "value": -0.028799829793881626, 
-        "severity": 0, 
-        "severity_value": -0.014399914896940813, 
-        "code": "best_score <= 2", 
-        "message": "Method knn_naive performs a lot better than baselines.\n  Task id: denoising\n  Method id: knn_naive\n  Metric id: poisson\n  Best score: -0.028799829793881626%\n"
-    }, 
-    {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
         "name": "Worst score knn_smoothing poisson", 
-        "value": -10.298315065894421, 
+        "value": -13.3967, 
         "severity": 3, 
-        "severity_value": 10.298315065894421, 
+        "severity_value": 13.3967, 
         "code": "worst_score >= -1", 
-        "message": "Method knn_smoothing performs much worse than baselines.\n  Task id: denoising\n  Method id: knn_smoothing\n  Metric id: poisson\n  Worst score: -10.298315065894421%\n"
+        "message": "Method knn_smoothing performs much worse than baselines.\n  Task id: task_denoising\n  Method id: knn_smoothing\n  Metric id: poisson\n  Worst score: -13.3967%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
         "name": "Best score knn_smoothing poisson", 
-        "value": -9.381554355715261, 
-        "severity": 0, 
-        "severity_value": -4.690777177857631, 
+        "value": 11.1446, 
+        "severity": 3, 
+        "severity_value": 5.5723, 
         "code": "best_score <= 2", 
-        "message": "Method knn_smoothing performs a lot better than baselines.\n  Task id: denoising\n  Method id: knn_smoothing\n  Metric id: poisson\n  Best score: -9.381554355715261%\n"
+        "message": "Method knn_smoothing performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: knn_smoothing\n  Metric id: poisson\n  Best score: 11.1446%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
         "name": "Worst score magic poisson", 
-        "value": 0.5000706596522453, 
+        "value": -0.7622, 
         "severity": 0, 
-        "severity_value": -0.5000706596522453, 
+        "severity_value": 0.7622, 
         "code": "worst_score >= -1", 
-        "message": "Method magic performs much worse than baselines.\n  Task id: denoising\n  Method id: magic\n  Metric id: poisson\n  Worst score: 0.5000706596522453%\n"
+        "message": "Method magic performs much worse than baselines.\n  Task id: task_denoising\n  Method id: magic\n  Metric id: poisson\n  Worst score: -0.7622%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
         "name": "Best score magic poisson", 
-        "value": 0.5903190095352917, 
-        "severity": 0, 
-        "severity_value": 0.29515950476764585, 
-        "code": "best_score <= 2", 
-        "message": "Method magic performs a lot better than baselines.\n  Task id: denoising\n  Method id: magic\n  Metric id: poisson\n  Best score: 0.5903190095352917%\n"
-    }, 
-    {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Worst score magic_approx poisson", 
-        "value": 0.5047105153603313, 
+        "value": 0.5245, 
         "severity": 0, 
-        "severity_value": -0.5047105153603313, 
-        "code": "worst_score >= -1", 
-        "message": "Method magic_approx performs much worse than baselines.\n  Task id: denoising\n  Method id: magic_approx\n  Metric id: poisson\n  Worst score: 0.5047105153603313%\n"
-    }, 
-    {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Best score magic_approx poisson", 
-        "value": 0.5933196737483786, 
-        "severity": 0, 
-        "severity_value": 0.2966598368741893, 
+        "severity_value": 0.26225, 
         "code": "best_score <= 2", 
-        "message": "Method magic_approx performs a lot better than baselines.\n  Task id: denoising\n  Method id: magic_approx\n  Metric id: poisson\n  Best score: 0.5933196737483786%\n"
+        "message": "Method magic performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: magic\n  Metric id: poisson\n  Best score: 0.5245%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Worst score magic_approx_reverse_norm poisson", 
-        "value": 0.9770398381710521, 
-        "severity": 0, 
-        "severity_value": -0.9770398381710521, 
-        "code": "worst_score >= -1", 
-        "message": "Method magic_approx_reverse_norm performs much worse than baselines.\n  Task id: denoising\n  Method id: magic_approx_reverse_norm\n  Metric id: poisson\n  Worst score: 0.9770398381710521%\n"
-    }, 
-    {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Best score magic_approx_reverse_norm poisson", 
-        "value": 0.9847398989203555, 
-        "severity": 0, 
-        "severity_value": 0.49236994946017776, 
-        "code": "best_score <= 2", 
-        "message": "Method magic_approx_reverse_norm performs a lot better than baselines.\n  Task id: denoising\n  Method id: magic_approx_reverse_norm\n  Metric id: poisson\n  Best score: 0.9847398989203555%\n"
-    }, 
-    {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Worst score magic_reverse_norm poisson", 
-        "value": 0.9770382645348494, 
-        "severity": 0, 
-        "severity_value": -0.9770382645348494, 
-        "code": "worst_score >= -1", 
-        "message": "Method magic_reverse_norm performs much worse than baselines.\n  Task id: denoising\n  Method id: magic_reverse_norm\n  Metric id: poisson\n  Worst score: 0.9770382645348494%\n"
-    }, 
-    {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Best score magic_reverse_norm poisson", 
-        "value": 0.9847373494114091, 
-        "severity": 0, 
-        "severity_value": 0.49236867470570456, 
-        "code": "best_score <= 2", 
-        "message": "Method magic_reverse_norm performs a lot better than baselines.\n  Task id: denoising\n  Method id: magic_reverse_norm\n  Metric id: poisson\n  Best score: 0.9847373494114091%\n"
-    }, 
-    {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Worst score no_denoising poisson", 
-        "value": 0.0, 
+        "name": "Worst score scprint poisson", 
+        "value": 0, 
         "severity": 0, 
         "severity_value": -0.0, 
         "code": "worst_score >= -1", 
-        "message": "Method no_denoising performs much worse than baselines.\n  Task id: denoising\n  Method id: no_denoising\n  Metric id: poisson\n  Worst score: 0.0%\n"
+        "message": "Method scprint performs much worse than baselines.\n  Task id: task_denoising\n  Method id: scprint\n  Metric id: poisson\n  Worst score: 0%\n"
     }, 
     {
-        "task_id": "denoising", 
+        "task_id": "task_denoising", 
         "category": "Scaling", 
-        "name": "Best score no_denoising poisson", 
-        "value": 0.0, 
+        "name": "Best score scprint poisson", 
+        "value": 0, 
         "severity": 0, 
         "severity_value": 0.0, 
         "code": "best_score <= 2", 
-        "message": "Method no_denoising performs a lot better than baselines.\n  Task id: denoising\n  Method id: no_denoising\n  Metric id: poisson\n  Best score: 0.0%\n"
-    }, 
-    {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Worst score perfect_denoising poisson", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": -1.0, 
-        "code": "worst_score >= -1", 
-        "message": "Method perfect_denoising performs much worse than baselines.\n  Task id: denoising\n  Method id: perfect_denoising\n  Metric id: poisson\n  Worst score: 1.0%\n"
-    }, 
-    {
-        "task_id": "denoising", 
-        "category": "Scaling", 
-        "name": "Best score perfect_denoising poisson", 
-        "value": 1.0, 
-        "severity": 0, 
-        "severity_value": 0.5, 
-        "code": "best_score <= 2", 
-        "message": "Method perfect_denoising performs a lot better than baselines.\n  Task id: denoising\n  Method id: perfect_denoising\n  Metric id: poisson\n  Best score: 1.0%\n"
+        "message": "Method scprint performs a lot better than baselines.\n  Task id: task_denoising\n  Method id: scprint\n  Metric id: poisson\n  Best score: 0%\n"
     }
 ]
\ No newline at end of file
diff --git a/results/denoising/data/results.json b/results/denoising/data/results.json
index b2892dc6..26ba90db 100644
--- a/results/denoising/data/results.json
+++ b/results/denoising/data/results.json
@@ -1,938 +1,2620 @@
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+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/immune_cells",
+    "method_id": "scprint",
+    "metric_values": {
+      "poisson": "NA",
+      "mse": "NA"
+    },
+    "scaled_scores": {
+      "poisson": 0,
+      "mse": 0
+    },
+    "mean_score": 0,
+    "resources": {
+      "submit": "2024-12-19 17:45:05",
+      "exit_code": 1,
+      "duration_sec": 40.1,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/mouse_blood_olsson_labelled",
+    "method_id": "scprint",
+    "metric_values": {
+      "poisson": "NA",
+      "mse": "NA"
+    },
+    "scaled_scores": {
+      "poisson": 0,
+      "mse": 0
+    },
+    "mean_score": 0,
+    "resources": {
+      "submit": "2024-12-19 17:42:35",
+      "exit_code": 1,
+      "duration_sec": 30,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/mouse_hspc_nestorowa2016",
+    "method_id": "scprint",
+    "metric_values": {
+      "poisson": "NA",
+      "mse": "NA"
+    },
+    "scaled_scores": {
+      "poisson": 0,
+      "mse": 0
+    },
+    "mean_score": 0,
+    "resources": {
+      "submit": "2024-12-19 17:42:35",
+      "exit_code": 1,
+      "duration_sec": 30.2,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/pancreas",
+    "method_id": "scprint",
+    "metric_values": {
+      "poisson": "NA",
+      "mse": "NA"
+    },
+    "scaled_scores": {
+      "poisson": 0,
+      "mse": 0
+    },
+    "mean_score": 0,
+    "resources": {
+      "submit": "2024-12-19 17:43:05",
+      "exit_code": 1,
+      "duration_sec": 30.1,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/tenx_1k_pbmc",
+    "method_id": "scprint",
+    "metric_values": {
+      "poisson": "NA",
+      "mse": "NA"
+    },
+    "scaled_scores": {
+      "poisson": 0,
+      "mse": 0
+    },
+    "mean_score": 0,
+    "resources": {
+      "submit": "2024-12-19 17:43:05",
+      "exit_code": 1,
+      "duration_sec": 39.9,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/tenx_5k_pbmc",
+    "method_id": "scprint",
+    "metric_values": {
+      "poisson": "NA",
+      "mse": "NA"
+    },
+    "scaled_scores": {
+      "poisson": 0,
+      "mse": 0
+    },
+    "mean_score": 0,
+    "resources": {
+      "submit": "2024-12-19 17:43:05",
+      "exit_code": 1,
+      "duration_sec": 40.1,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/tnbc_wu2021",
+    "method_id": "scprint",
+    "metric_values": {
+      "poisson": "NA",
+      "mse": "NA"
+    },
+    "scaled_scores": {
+      "poisson": 0,
+      "mse": 0
+    },
+    "mean_score": 0,
+    "resources": {
+      "submit": "2024-12-19 17:43:06",
+      "exit_code": 1,
+      "duration_sec": 40.1,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  },
+  {
+    "dataset_id": "openproblems_v1/zebrafish",
+    "method_id": "scprint",
+    "metric_values": {
+      "poisson": "NA",
+      "mse": "NA"
+    },
+    "scaled_scores": {
+      "poisson": 0,
+      "mse": 0
+    },
+    "mean_score": 0,
+    "resources": {
+      "submit": "2024-12-19 17:43:05",
+      "exit_code": 1,
+      "duration_sec": 39.8,
+      "cpu_pct": "NA",
+      "peak_memory_mb": "NA",
+      "disk_read_mb": "NA",
+      "disk_write_mb": "NA"
+    }
+  }
+]
diff --git a/results/denoising/data/state.yaml b/results/denoising/data/state.yaml
new file mode 100644
index 00000000..abbb0fc1
--- /dev/null
+++ b/results/denoising/data/state.yaml
@@ -0,0 +1,9 @@
+id: process
+output_scores: !file results.json
+output_method_info: !file method_info.json
+output_metric_info: !file metric_info.json
+output_dataset_info: !file dataset_info.json
+output_task_info: !file task_info.json
+output_qc: !file quality_control.json
+output_metric_execution_info: !file metric_execution_info.json
+
diff --git a/results/denoising/data/task_info.json b/results/denoising/data/task_info.json
index 11503706..a8defe2b 100644
--- a/results/denoising/data/task_info.json
+++ b/results/denoising/data/task_info.json
@@ -1,10 +1,11 @@
 {
-  "task_id": "denoising",
-  "commit_sha": "c97decf07adb2e3050561d6fa9ae46132be07bef",
+  "task_id": "task_denoising",
+  "commit_sha": null,
   "task_name": "Denoising",
   "task_summary": "Removing noise in sparse single-cell RNA-sequencing count data",
-  "task_description": "\nSingle-cell RNA-Seq protocols only detect a fraction of the mRNA molecules present\nin each cell. As a result, the measurements (UMI counts) observed for each gene and each\ncell are associated with generally high levels of technical noise ([Grün et al.,\n2014](https://openproblems.bio/bibliography#grn2014validation)). Denoising describes the\ntask of estimating the true expression level of each gene in each cell. In the\nsingle-cell literature, this task is also referred to as *imputation*, a term which is\ntypically used for missing data problems in statistics. Similar to the use of the terms\n\"dropout\", \"missing data\", and \"technical zeros\", this terminology can create confusion\nabout the underlying measurement process ([Sarkar and Stephens,\n2021](https://openproblems.bio/bibliography#sarkar2021separating)).\n\nA key challenge in evaluating denoising methods is the general lack of a ground truth. A\nrecent benchmark study ([Hou et al.,\n2020](https://openproblems.bio/bibliography#hou2020systematic))\nrelied on flow-sorted datasets, mixture control experiments ([Tian et al.,\n2019](https://openproblems.bio/bibliography#tian2019benchmarking)), and comparisons with\nbulk RNA-Seq data. Since each of these approaches suffers from specific limitations, it\nis difficult to combine these different approaches into a single quantitative measure of\ndenoising accuracy. Here, we instead rely on an approach termed molecular\ncross-validation (MCV), which was specifically developed to quantify denoising accuracy\nin the absence of a ground truth ([Batson et al.,\n2019](https://openproblems.bio/bibliography#batson2019molecular)). In MCV, the observed\nmolecules in a given scRNA-Seq dataset are first partitioned between a *training* and a\n*test* dataset. Next, a denoising method is applied to the training dataset. Finally,\ndenoising accuracy is measured by comparing the result to the test dataset. The authors\nshow that both in theory and in practice, the measured denoising accuracy is\nrepresentative of the accuracy that would be obtained on a ground truth dataset.\n\n",
-  "repo": "https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks/denoising",
+  "task_description": "A key challenge in evaluating denoising methods is the general lack of a ground truth. A\nrecent benchmark study ([Hou et al.,\n2020](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02132-x))\nrelied on flow-sorted datasets, mixture control experiments ([Tian et al.,\n2019](https://www.nature.com/articles/s41592-019-0425-8)), and comparisons with bulk\nRNA-Seq data. Since each of these approaches suffers from specific limitations, it is\ndifficult to combine these different approaches into a single quantitative measure of\ndenoising accuracy. Here, we instead rely on an approach termed molecular\ncross-validation (MCV), which was specifically developed to quantify denoising accuracy\nin the absence of a ground truth ([Batson et al.,\n2019](https://www.biorxiv.org/content/10.1101/786269v1)). In MCV, the observed molecules\nin a given scRNA-Seq dataset are first partitioned between a *training* and a *test*\ndataset. Next, a denoising method is applied to the training dataset. Finally, denoising\naccuracy is measured by comparing the result to the test dataset. The authors show that\nboth in theory and in practice, the measured denoising accuracy is representative of the\naccuracy that would be obtained on a ground truth dataset.\n",
+  "repo": "https://github.com/openproblems-bio/task_denoising",
+  "issue_tracker": "https://github.com/openproblems-bio/task_denoising/issues",
   "authors": [
     {
       "name": "Wesley Lewis",
@@ -38,6 +39,6 @@
       }
     }
   ],
-  "version": "v1.0.0",
+  "version": "1.0.0",
   "license": "MIT"
 }
diff --git a/results/denoising/index.qmd b/results/denoising/index.qmd
index 5808551e..7bc424f0 100644
--- a/results/denoising/index.qmd
+++ b/results/denoising/index.qmd
@@ -20,3 +20,4 @@ params <- list(data_dir = "./data")
 ```
 
 {{< include ../_include/_task_template.qmd >}}
+