Updated title

in_text/text_data_release.yml

@@ -1,12 +1,12 @@
 title: >-
-  Data release: Daily surface temperatures for 185,549 lakes in the Contiguous United States estimated using deep learning (1980-2020)
+  Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological conditions (1980-2020)
 
 
 abstract: >-
-  The data release of daily lake surface temperatures comprises 185,549 lakes across the continental United States from
-  1980 to 2020 that are generated using an entity-aware long short-term memory deep learning model. In-situ measurements
-  used for model training spanned a subset of 12,227 lakes. Model training was optimized for prediction on unmonitored
-  lakes through a cross validation framework. Median per-lake estimated error found through cross validation on lakes with
+  Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from
+  1980 to 2020 generated using an entity-aware long short-term memory deep learning model. In-situ measurements
+  used for model training and evaluation are from 12,227 lakes and are included as well as daily
+  meteorological conditions and lake properties. Median per-lake estimated error found through cross validation on lakes with
   in-situ surface temperature observations was 1.24 °C. The generated dataset will be beneficial for a wide range of
   applications including estimations of thermal habitats and the impacts of climate change on inland lakes.
 
@@ -21,7 +21,7 @@ larger-cites:
   -
     authors: ["Jared Willard", "Jordan S. Read", "Simon N. Topp", "Gretchen J.A. Hansen", "Vipin Kumar"]
     title: >-
-      Daily surface temperatures for 185,549 lakes in the Conterminous United States estimated using deep learning (1980-2020)
+      Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980-2020)
     pubdate: 2022
     form: publication
 cross-cites:

log/00_sb_posted_files.csv

@@ -1,7 +1,7 @@
 filepath,sb_id,time_uploaded_to_sb
 out_data/lake_metadata.csv,60341c3ed34eb12031172aa6,2022-01-09 13:05 UTC
 out_data/lake_surface_temp_obs_preds.csv,60341c3ed34eb12031172aa6,2022-01-13 19:57 UTC
-out_xml/data_release_metadata.xml,60341c3ed34eb12031172aa6,2022-01-13 19:57 UTC
+out_xml/data_release_metadata.xml,60341c3ed34eb12031172aa6,2022-01-28 15:44 UTC
 tmp/01_predicted_temp_N24-53_W98-126.nc,60341c3ed34eb12031172aa6,2022-01-09 13:20 UTC
 tmp/01_weather_N24-53_W98-126.nc,60341c3ed34eb12031172aa6,2021-07-22 15:41 UTC
 tmp/02_predicted_temp_N40-53_W67-98.nc,60341c3ed34eb12031172aa6,2022-01-09 13:33 UTC

out_xml/data_release_metadata.xml

@@ -9,7 +9,7 @@
         <origin>Gretchen J.A. Hansen</origin>
         <origin>Vipin Kumar</origin>
         <pubdate>2022</pubdate>
-        <title>Data release: Daily surface temperatures for 185,549 lakes in the Contiguous United States estimated using deep learning (1980-2020)</title>
+        <title>Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological conditions (1980-2020)</title>
         <geoform>Two comma-delimited files and six NetCDF files</geoform>
         <pubinfo>
           <pubplace>Online (digital release)</pubplace>
@@ -24,14 +24,14 @@
             <origin>Gretchen J.A. Hansen</origin>
             <origin>Vipin Kumar</origin>
             <pubdate>2022</pubdate>
-            <title>Daily surface temperatures for 185,549 lakes in the Conterminous United States estimated using deep learning (1980-2020)</title>
+            <title>Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980-2020)</title>
             <geoform>publication</geoform>
           </citeinfo>
         </lworkcit>
       </citeinfo>
     </citation>
     <descript>
-      <abstract>The data release of daily lake surface temperatures comprises 185,549 lakes across the continental United States from 1980 to 2020 that are generated using an entity-aware long short-term memory deep learning model. In-situ measurements used for model training spanned a subset of 12,227 lakes. Model training was optimized for prediction on unmonitored lakes through a cross validation framework. Median per-lake estimated error found through cross validation on lakes with in-situ surface temperature observations was 1.24 °C. The generated dataset will be beneficial for a wide range of applications including estimations of thermal habitats and the impacts of climate change on inland lakes.</abstract>
+      <abstract>Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term memory deep learning model. In-situ measurements used for model training and evaluation are from 12,227 lakes and are included as well as daily meteorological conditions and lake properties. Median per-lake estimated error found through cross validation on lakes with in-situ surface temperature observations was 1.24 °C. The generated dataset will be beneficial for a wide range of applications including estimations of thermal habitats and the impacts of climate change on inland lakes.</abstract>
       <purpose>This dataset is well suited as a research complement to the fields of fisheries biology, limnology, climate change science, and aquatic invasive species research. The temperature predictions included here can be inputs to other models where water temperature is required.</purpose>
     </descript>
     <timeperd>
@@ -354,7 +354,7 @@
       <procstep>
         <procdesc>This dataset was generated using the methodology explained in the peer-reviewed paper Willard et al. 2022. Details within that paper describe how models were built and other data were assembled. Modeling environment information is captured below: This dataset was generated using open source tools available in the R programming language (R version 4.1.0 (2021-05-18)). The computing platform for generating data and metadata was x86_64-apple-darwin17.0. R packages loaded into this environment: ncdf4, version: 1.17; remake, version: 0.3.0; viridis, version: 0.6.1; viridisLite, version: 0.4.0; ncdfgeom, version: 1.1.2; glmtools, version: 0.15.0; rLakeAnalyzer, version: 1.11.4.1; GLMr, version: 3.1.16; whisker, version: 0.4; sf, version: 1.0-2; lwgeom, version: 0.2-6; rgdal, version: 1.5-23; sp, version: 1.4-5; yaml, version: 2.2.1; sbtools, version: 1.1.15; scipiper, version: 0.0.24; meddle, version: 0.0.16; mapdata, version: 2.3.0; maps, version: 3.3.0; forcats, version: 0.5.1; readr, version: 1.4.0; ggplot2, version: 3.3.4; tidyverse, version: 1.3.1; ffanalytics, version: 2.0.10.0001; xml2, version: 1.3.2; httr, version: 1.4.2; stringr, version: 1.4.0; tidyr, version: 1.1.3; tibble, version: 3.1.4; dplyr, version: 1.0.7; purrr, version: 0.3.4; rvest, version: 1.0.1.
 This dataset was generated using open source tools available in the Python programming language (Python version 3.6.10) with K40 GPUs with CUDA version 11.4. Python packages loaded into this environment: _libgcc_mutex=0.1=main; _r-mutex=1.0.0=anacondar_1; arrow-cpp=0.15.1=py36h7cd5009_5; asn1crypto=1.3.0=py36_0; binutils_impl_linux-64=2.33.1=he6710b0_7; binutils_linux-64=2.33.1=h9595d00_15; blas=1.0=mkl; boost-cpp=1.71.0=h7b6447c_0; brotli=1.0.7=he6710b0_0; bwidget=1.9.11=1; bzip2=1.0.8=h7b6447c_0; c-ares=1.15.0=h7b6447c_1001; ca-certificates=2020.7.22=0; cairo=1.14.12=h8948797_3; captum=0.2.0=0; certifi=2020.6.20=py36_0; cffi=1.14.0=py36h2e261b9_0; chardet=3.0.4=py36_1003; cryptography=2.8=py36h1ba5d50_0; cudatoolkit=10.0.130=0; curl=7.67.0=hbc83047_0; cycler=0.10.0=py36_0; dbus=1.13.12=h746ee38_0; double-conversion=3.1.5=he6710b0_1; expat=2.2.6=he6710b0_0; feather-format=0.4.0=py_1003; fontconfig=2.13.0=h9420a91_0; freetype=2.9.1=h8a8886c_1; fribidi=1.0.10=h7b6447c_0; gcc_impl_linux-64=7.3.0=habb00fd_1; gcc_linux-64=7.3.0=h553295d_15; gdrive=2.1.0=haa36a5b_0; gflags=2.2.2=he6710b0_0; gfortran_impl_linux-64=7.3.0=hdf63c60_1; gfortran_linux-64=7.3.0=h553295d_15; glib=2.63.1=h5a9c865_0; glog=0.4.0=he6710b0_0; graphite2=1.3.14=h23475e2_0; grpc-cpp=1.26.0=hf8bcb03_0; gsl=2.4=h14c3975_4; gst-plugins-base=1.14.0=hbbd80ab_1; gstreamer=1.14.0=hb453b48_1; gxx_impl_linux-64=7.3.0=hdf63c60_1; gxx_linux-64=7.3.0=h553295d_15; harfbuzz=1.8.8=hffaf4a1_0; icu=58.2=h9c2bf20_1; intel-openmp=2020.0=166; joblib=0.14.1=py_0; jpeg=9b=h024ee3a_2; kiwisolver=1.1.0=py36he6710b0_0; krb5=1.16.4=h173b8e3_0; ld_impl_linux-64=2.33.1=h53a641e_7; libboost=1.71.0=h97c9712_0; libcurl=7.67.0=h20c2e04_0; libedit=3.1.20181209=hc058e9b_0; libevent=2.1.8=h1ba5d50_0; libffi=3.2.1=hd88cf55_4; libgcc-ng=9.1.0=hdf63c60_0; libgfortran-ng=7.3.0=hdf63c60_0; libpng=1.6.37=hbc83047_0; libprotobuf=3.11.2=hd408876_0; libssh2=1.9.0=h1ba5d50_1; libstdcxx-ng=9.1.0=hdf63c60_0; libtiff=4.1.0=h2733197_0; libuuid=1.0.3=h1bed415_2; libxcb=1.13=h1bed415_1; libxml2=2.9.9=hea5a465_1; lz4-c=1.8.1.2=h14c3975_0; make=4.2.1=h1bed415_1; matplotlib=3.1.3=py36_0; matplotlib-base=3.1.3=py36hef1b27d_0; mkl=2020.0=166; mkl-service=2.3.0=py36he904b0f_0; mkl_fft=1.0.15=py36ha843d7b_0; mkl_random=1.1.0=py36hd6b4f25_0; ncurses=6.2=he6710b0_0; ninja=1.9.0=py36hfd86e86_0; numpy=1.18.1=py36h4f9e942_0; numpy-base=1.18.1=py36hde5b4d6_1; olefile=0.46=py36_0; openssl=1.1.1g=h516909a_0; pandas=1.0.1=py36h0573a6f_0; pango=1.42.4=h049681c_0; pcre=8.43=he6710b0_0; pip=20.0.2=py36_1; pixman=0.40.0=h7b6447c_0; pyarrow=0.15.1=py36h0573a6f_0; pycparser=2.19=py36_0; pyopenssl=19.1.0=py36_0; pyparsing=2.4.6=py_0; pyqt=5.9.2=py36h05f1152_2; pysocks=1.7.1=py36_0; python=3.6.10=h0371630_0; python-dateutil=2.8.1=py_0; python_abi=3.6=1_cp36m; pytorch=1.2.0=py3.6_cuda10.0.130_cudnn7.6.2_0; pytz=2019.3=py_0; qt=5.9.7=h5867ecd_1; r=3.6.0=r36_0; r-base=3.6.1=h9bb98a2_1; r-boot=1.3_20=r36h6115d3f_0; r-class=7.3_15=r36h96ca727_0; r-cluster=2.0.8=r36ha65eedd_0; r-codetools=0.2_16=r36h6115d3f_0; r-foreign=0.8_71=r36h96ca727_0; r-kernsmooth=2.23_15=r36ha65eedd_4; r-lattice=0.20_38=r36h96ca727_0; r-mass=7.3_51.3=r36h96ca727_0; r-matrix=1.2_17=r36h96ca727_0; r-mgcv=1.8_28=r36h96ca727_0; r-nlme=3.1_139=r36ha65eedd_0; r-nnet=7.3_12=r36h96ca727_0; r-recommended=3.6.0=r36_0; r-rpart=4.1_15=r36h96ca727_0; r-spatial=7.3_11=r36h96ca727_4; r-survival=2.44_1.1=r36h96ca727_0; re2=2019.08.01=he6710b0_0; readline=7.0=h7b6447c_5; requests=2.22.0=py36_1; scipy=1.4.1=py36h0b6359f_0; setuptools=45.2.0=py36_0; sip=4.19.8=py36hf484d3e_0; six=1.14.0=py36_0; snappy=1.1.7=hbae5bb6_3; sqlite=3.31.1=h7b6447c_0; thrift-cpp=0.11.0=h02b749d_3; tk=8.6.8=hbc83047_0; tktable=2.10=h14c3975_0; torchvision=0.4.0=py36_cu100; tornado=6.0.4=py36h7b6447c_1; uriparser=0.9.3=he6710b0_1; urllib3=1.25.8=py36_0; wheel=0.34.2=py36_0; xz=5.2.4=h14c3975_4; zlib=1.2.11=h7b6447c_3; zstd=1.3.7=h0b5b093_0; aiohttp==3.6.2; async-timeout==3.0.1; attrs==19.3.0; beautifulsoup4==4.8.2; click==7.0; cloudpickle==1.3.0; colorama==0.4.3; filelock==3.0.12; funcsigs==1.0.2; google==2.0.3; idna==2.9; idna-ssl==1.1.0; importlib-metadata==1.5.0; jsonschema==3.2.0; more-itertools==8.2.0; multidict==4.7.5; packaging==20.1; pillow==6.2.2; pluggy==0.13.1; protobuf==3.11.3; py==1.8.1; py-spy==0.3.3; pyrsistent==0.15.7; pytest==5.3.5; pyyaml==5.3; ray==0.8.2; redis==3.4.1; scikit-learn==0.22.2.post1; soupsieve==2.0; tabulate==0.8.6; tensorboardx==2.0; typing-extensions==3.7.4.1; wcwidth==0.1.8; yarl==1.4.2; zipp==3.1.0</procdesc>
-        <procdate>20220113</procdate>
+        <procdate>20220128</procdate>
       </procstep>
     </lineage>
   </dataqual>
@@ -1308,7 +1308,7 @@ This dataset was generated using open source tools available in the Python progr
     </stdorder>
   </distinfo>
   <metainfo>
-    <metd>20220113</metd>
+    <metd>20220128</metd>
     <metc>
       <cntinfo>
         <cntperp>