Merge branch 'master' of https://github.com/FAIR-Universe/FAIR-Univer…

…se.github.io

docs/_sources/pages/terms.md.txt

@@ -17,4 +17,18 @@ To ensure proper representation and verification of your affiliation with an org
 - Ensure that your Codabench profile includes your current organizational affiliation. This helps in verifying your credentials and associating your contributions with the correct institution.
 - If participants are freelancers they should provide brief details (via email to [email protected]) of their freelance operation, any supporting URL, and their country of residence.
 - Use your organization-issued email address to register for the competition. This will help us confirm that you are officially associated with the organization and prevent unauthorized entries.
-- Registration is not required to access the dataset that is provided on the “Data” tab. We encourage the use of the dataset for research. In the event that a participant who is not eligible for submission via codabench according to the conditions above, can demonstrate a successful method on the challenge task we encourage them to contact the organizers for attribution and potential inclusion in the final competition stage if possible.
+- Registration is not required to access the dataset that is provided on the “Data” tab. We encourage the use of the dataset for research. In the event that a participant who is not eligible for submission via codabench according to the conditions above, can demonstrate a successful method on the challenge task we encourage them to contact the organizers for attribution and potential inclusion in the final competition stage if possible.
+
+## Team Participation
+
+If you are interested in participating as a team, please read the following terms. To register your team, contact us by sending an email to [email protected].
+
+1. A team can consist of 2-5 members. All team members must be declared in advance, including their names, emails, and affiliations.
+2. Each team must designate one member as the team leader, who will be responsible for communication and submissions.
+3. The team leader must create a Codabench account using their email, and all submissions should be made through this account only.
+4. Multiple accounts for a single team are not allowed. Any team found using multiple accounts will be disqualified.
+5. Only the designated team leader is allowed to submit entries on behalf of the entire team.
+6. Any changes to the team composition after registration must be communicated to the competition organizers for approval.
+7. The team leader is responsible for ensuring that all work submitted is original and adheres to the competition’s guidelines.
+8. If a team wins a prize, the prize will be awarded to the team leader, who will be responsible for distributing the prize among the team members. The competition organizers are not responsible for prize distribution within the team.
+9. All team members will be acknowledged in the results announcements.

docs/pages/terms.html

@@ -103,6 +103,21 @@ <h2>Participant Affiliation Guidelines<a class="headerlink" href="#participant-a
 <li><p>Registration is not required to access the dataset that is provided on the “Data” tab. We encourage the use of the dataset for research. In the event that a participant who is not eligible for submission via codabench according to the conditions above, can demonstrate a successful method on the challenge task we encourage them to contact the organizers for attribution and potential inclusion in the final competition stage if possible.</p></li>
 </ul>
 </section>
+<section id="team-participation">
+<h2>Team Participation<a class="headerlink" href="#team-participation" title="Link to this heading"></a></h2>
+<p>If you are interested in participating as a team, please read the following terms. To register your team, contact us by sending an email to fair-universe&#64;lbl.gov.</p>
+<ol class="arabic simple">
+<li><p>A team can consist of 2-5 members. All team members must be declared in advance, including their names, emails, and affiliations.</p></li>
+<li><p>Each team must designate one member as the team leader, who will be responsible for communication and submissions.</p></li>
+<li><p>The team leader must create a Codabench account using their email, and all submissions should be made through this account only.</p></li>
+<li><p>Multiple accounts for a single team are not allowed. Any team found using multiple accounts will be disqualified.</p></li>
+<li><p>Only the designated team leader is allowed to submit entries on behalf of the entire team.</p></li>
+<li><p>Any changes to the team composition after registration must be communicated to the competition organizers for approval.</p></li>
+<li><p>The team leader is responsible for ensuring that all work submitted is original and adheres to the competition’s guidelines.</p></li>
+<li><p>If a team wins a prize, the prize will be awarded to the team leader, who will be responsible for distributing the prize among the team members. The competition organizers are not responsible for prize distribution within the team.</p></li>
+<li><p>All team members will be acknowledged in the results announcements.</p></li>
+</ol>
+</section>
 </section>
 
 

docs/rst_source/datasets.html

@@ -192,7 +192,7 @@ <h1>Datasets module<a class="headerlink" href="#datasets-module" title="Link to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="datasets.Data.load_train_set">
-<span class="sig-name descname"><span class="pre">load_train_set</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#datasets.Data.load_train_set" title="Link to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">load_train_set</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#datasets.Data.load_train_set" title="Link to this definition"></a></dt>
 <dd></dd></dl>
 
 </dd></dl>

docs/rst_source/ingestion.html

@@ -159,7 +159,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="ingestion.Ingestion.load_train_set">
-<span class="sig-name descname"><span class="pre">load_train_set</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ingestion.Ingestion.load_train_set" title="Link to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">load_train_set</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ingestion.Ingestion.load_train_set" title="Link to this definition"></a></dt>
 <dd><p>Load the training set.</p>
 <dl class="simple">
 <dt>Returns:</dt><dd><p>object: The loaded training set.</p>

docs/rst_source/ingestion_parallel.html

@@ -172,7 +172,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="ingestion_parallel.Ingestion.load_train_set">
-<span class="sig-name descname"><span class="pre">load_train_set</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ingestion_parallel.Ingestion.load_train_set" title="Link to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">load_train_set</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ingestion_parallel.Ingestion.load_train_set" title="Link to this definition"></a></dt>
 <dd><p>Load the training set.</p>
 <dl class="simple">
 <dt>Returns:</dt><dd><p>object: The loaded training set.</p>