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1,298 results for “Archive”
#MeToo and the Women's Rights Movement in China Web Archive collection derivatives
<p>Web archive derivatives of the <a href="https://archive-it.org/collections/12172">Literary Authors from Europe and Eurasia Web Archive</a> collection from the <a href="https://archive-it.org/home/IvyPlus">Ivy Plus Libraries Confederation</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a> and <a href="https://cloud.archivesunleashed.org/">Archives Unleashed Cloud</a>.</p> <p>The <strong>ivy-12145-parquet.tar.gz</strong> derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/datathon-nyc/parquet_pandas_stonewall.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Word processor files<br> </li> </ul> <p>The <strong>ivy-12145-auk.tar.gz </strong>derivatives<strong> </strong>are the <a href="https://cloud.archivesunleashed.org/derivatives">standard set of web archive derivatives</a> produced by the Archives Unleashed Cloud.</p> <ul> <li><strong>Gephi </strong>file, which can be loaded into <a href="https://gephi.org/">Gephi</a>. It will have basic characteristics already computed and a basic layout.</li> <li><strong>Raw Network</strong> file, which can also be loaded into <a href="https://gephi.org/">Gephi</a>. You will have to use that network program to lay it out yourself.</li> <li><strong>Full text</strong> file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content.</li> <li><strong>Domains count</strong> file. A text file containing the frequency count of domains captured within your web archive.</li> </ul>
Freely Accessible eJournals web archive collection derivatives
<p>Web archive derivatives of the <a href="https://archive-it.org/collections/5921">Freely Accessible eJournals</a> collection from <a href="https://archive-it.org/home/Columbia">Columbia University Libraries</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a> and <a href="https://cloud.archivesunleashed.org/">Archives Unleashed Cloud</a>.</p> <p>The <strong>cul-5921-parquet.tar.gz</strong> derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/datathon-nyc/parquet_pandas_stonewall.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Word processor files<br> </li> </ul> <p>The <strong>cul-12143-auk.tar.gz </strong>derivatives<strong> </strong>are the <a href="https://cloud.archivesunleashed.org/derivatives">standard set of web archive derivatives</a> produced by the Archives Unleashed Cloud.</p> <ul> <li><strong>Gephi </strong>file, which can be loaded into <a href="https://gephi.org/">Gephi</a>. It will have basic characteristics already computed and a basic layout.</li> <li><strong>Raw Network</strong> file, which can also be loaded into <a href="https://gephi.org/">Gephi</a>. You will have to use that network program to lay it out yourself.</li> <li><strong>Full text</strong> file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content.</li> <li><strong>Domains count</strong> file. A text file containing the frequency count of domains captured within your web archive.</li> </ul>
State Elections Web Archive collection derivatives
<p>Web archive derivatives of the <a href="https://archive-it.org/collections/10793">State Elections Web Archive</a> collection from the <a href="https://archive-it.org/home/IvyPlus">Ivy Plus Libraries Confederation</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a> and <a href="https://cloud.archivesunleashed.org/">Archives Unleashed Cloud</a>.</p> <p>The <strong>ivy-10793-parquet.tar.gz</strong> derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/datathon-nyc/parquet_pandas_stonewall.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Word processor files<br> </li> </ul> <p>The <strong>ivy-10793-auk.tar.gz </strong>derivatives<strong> </strong>are the <a href="https://cloud.archivesunleashed.org/derivatives">standard set of web archive derivatives</a> produced by the Archives Unleashed Cloud.</p> <ul> <li><strong>Gephi </strong>file, which can be loaded into <a href="https://gephi.org/">Gephi</a>. It will have basic characteristics already computed and a basic layout.</li> <li><strong>Raw Network</strong> file, which can also be loaded into <a href="https://gephi.org/">Gephi</a>. You will have to use that network program to lay it out yourself.</li> <li><strong>Full text</strong> file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content.</li> <li><strong>Domains count</strong> file. A text file containing the frequency count of domains captured within your web archive.</li> </ul>
Resistance web archive collection derivatives
<p>Web archive derivatives of the <a href="https://archive-it.org/collections/8752">Resistance</a> collection from <a href="https://archive-it.org/home/Columbia">Columbia University Libraries</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a> and <a href="https://cloud.archivesunleashed.org/">Archives Unleashed Cloud</a>.</p> <p>The <strong>cul-8752-parquet.tar.gz</strong> derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/datathon-nyc/parquet_pandas_stonewall.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>PDFs</li> <li>Spreadsheets</li> <li>Text files</li> <li>Word processor files<br> </li> </ul> <p>The <strong>cul-8752-auk.tar.gz </strong>derivatives<strong> </strong>are the <a href="https://cloud.archivesunleashed.org/derivatives">standard set of web archive derivatives</a> produced by the Archives Unleashed Cloud.</p> <ul> <li><strong>Gephi </strong>file, which can be loaded into <a href="https://gephi.org/">Gephi</a>. It will have basic characteristics already computed and a basic layout.</li> <li><strong>Raw Network</strong> file, which can also be loaded into <a href="https://gephi.org/">Gephi</a>. You will have to use that network program to lay it out yourself.</li> <li><strong>Full text</strong> file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content.</li> <li><strong>Domains count</strong> file. A text file containing the frequency count of domains captured within your web archive.</li> </ul>
Geologic Field Trip Guidebooks Web Archive collection derivatives
<p>Web archive derivatives of the collection <a href="https://archive-it.org/collections/12576">Geologic Field Trip Guidebooks Web Archive</a> from the <a href="https://archive-it.org/home/IvyPlus">Ivy Plus Libraries Confederation</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a> and <a href="https://cloud.archivesunleashed.org/">Archives Unleashed Cloud</a>.</p> <p>The <strong>ivy-12576-parquet.tar.gz</strong> derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/datathon-nyc/parquet_pandas_stonewall.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Word processor files<br> </li> </ul> <p>The <strong>ivy-12576-auk.tar.gz </strong>derivatives<strong> </strong>are the <a href="https://cloud.archivesunleashed.org/derivatives">standard set of web archive derivatives</a> produced by the Archives Unleashed Cloud.</p> <ul> <li><strong>Gephi </strong>file, which can be loaded into <a href="https://gephi.org/">Gephi</a>. It will have basic characteristics already computed and a basic layout.</li> <li><strong>Raw Network</strong> file, which can also be loaded into <a href="https://gephi.org/">Gephi</a>. You will have to use that network program to lay it out yourself.</li> <li><strong>Full text</strong> file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content.</li> <li><strong>Domains count</strong> file. A text file containing the frequency count of domains captured within your web archive.</li> </ul>
Coalition Avenir Québec (CAQ) web archive collection derivatives
<p>Web archive derivatives of the Coalition Avenir Québec (CAQ) collection from the <a href="https://www.banq.qc.ca/accueil/">Bibliothèque et Archives nationales du Québec</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a>. Merci beaucoup BAnQ!</p> <p>These derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/parquet_pandas_example.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Videos</li> <li>Word processor files</li> </ul>
Harvest Quebec Government Websites from December 2006 web archive collection derivatives
<p>Web archive derivatives of the Sites of the Harvest Quebec Government Websites from December 2006 collection from the <a href="https://www.banq.qc.ca/accueil/">Bibliothèque et Archives nationales du Québec</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a>. Merci beaucoup BAnQ!</p> <p>These derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/parquet_pandas_example.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Videos</li> <li>Word processor files</li> </ul>
Quebec International Relation and Economy web archive collection derivatives
<p>Web archive derivatives of the Sites of the Quebec International Relation and Economy collection from the <a href="https://www.banq.qc.ca/accueil/">Bibliothèque et Archives nationales du Québec</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a>. Merci beaucoup BAnQ!</p> <p>These derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/parquet_pandas_example.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Videos</li> <li>Word processor files</li> </ul>
Quebec Ministry of Tourism (2012 to 2017) web archive collection derivatives
<p>Web archive derivatives of the Quebec Ministry of Tourism (2012 to 2017) collection from the <a href="https://www.banq.qc.ca/accueil/">Bibliothèque et Archives nationales du Québec</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a>. Merci beaucoup BAnQ!</p> <p>These derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/parquet_pandas_example.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Videos</li> <li>Word processor files</li> </ul>
Quebec Health Ministry (2013-2018) web archive collection derivatives
<p>Web archive derivatives of the Quebec Health Ministry (2013-2018) collection from the <a href="https://www.banq.qc.ca/accueil/">Bibliothèque et Archives nationales du Québec</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a>. Merci beaucoup BAnQ!</p> <p>These derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/parquet_pandas_example.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>PDFs</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Word processor files</li> </ul>
Model runs generated by publication "Re-evaluating 14 C dating accuracy in deep-sea sediment archives"
<p>Model runs generated by following publication:</p> <p>B.C. Lougheed, P. Ascough, A. Dolman, L. Löwemark and B. Metcalfe, 2020. “Re-evaluating 14C dating accuracy in deep-sea sediment archives.” Geochronology, doi:10.5194/gchron-2019-10</p> <p>Contains .mat files that can be opened by the Matlab / Octave environment.</p>
Data archive for paper "WRF‐TEB: Implementation and Evaluation of the Coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) Model"
<p><strong>WRF-TEB data archive</strong></p> <p>This archive contains data and tools to reproduce results as included in <a href="https://doi.org/10.1029/2019ms001961">Meyer et al. (2020)</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li><a href="https://sylabs.io/">Singularity</a> version >= 3.</li> </ul> <p><strong>Usage</strong></p> <p>To run all models and plotting scripts included in integration test and meteorological evaluation, run the following command from your command-line interface.</p> <pre><code>NPROC=8 TYPE=evaluate tools/singularity/run.sh</code></pre> <p>where <code>NPROC=8</code> is the maximum number of processes to use. The output can be found in the <code>work/</code> folder.</p> <p><strong>HPC</strong></p> <p>If you want to use this in an HPC environment, use <code>tools/hpc</code> as a template. As an example, to run the evaluation on Imperial HPC using PBS (Portable Batch System), use:</p> <pre><code>qsub -v REPO_ROOT=$(pwd),TYPE=evaluate tools/hpc/job_imperial.sh</code></pre> <p><strong>Copyright and License</strong></p> <p>Copyright and licensing information are included at the top of source files or as separate files in folders.</p> <p><strong>References</strong></p> <p>Meyer, D., Schoetter, R., Riechert, M., Verrelle, A., Tewari, M., Dudhia, J., Masson, V., Reeuwijk, M., & Grimmond, S. (2020). WRF‐TEB: implementation and evaluation of the coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) model. Journal of Advances in Modeling Earth Systems. <a href="https://doi.org/10.1029/2019ms001961">https://doi.org/10.1029/2019ms001961</a></p>
New Data Types in Social Science Research and Data Archives
<p>During the CESSDA event "Strengthening and Widening of the European Infrastructure of Social Science Data Archives" in Skopje, North Macedonia on 5-6 November 2019, Libby Bishop presented on New Data Types in Social Science Research and Data Archives. </p> <p>The talk addressed four questions: What are researchers doing with social media and other data? What are repositories currently doing to hold and share new forms data? Are there useful resources for repository staff, and what next steps are planned? What are repositories’ responsibilities in the broader debates? It is challenging to share data (consistent with Open Science and FAIR principles) while also meeting obligations to obey laws and protect confidentiality. Whereas the ethical procedures for “researcher-generated” data, such as surveys and interviews are well-established, no such consensus exists for social media and other data-in-the-wild. Even the basic conception of whether virtual spaces are public or private remains contested among researchers and the public. The talk delineated the key ethical issues of this debate and described practical solutions that some data repositories currently offer. To date, these solutions strictly interpret legal restrictions placed on data sharing. </p> <p> </p> <p>Alongside the presentation, also a video introducing these issues is available. The video is also available for <a href="https://youtu.be/GvirMb1vmww">viewing on Youtube</a>.</p> <p> </p>
How Do Data Archives Stay Connected to User Needs?
<p>This video has been realized in occasion of the CESSDA Training Days 2019. The CESSDA Training Days were a two-day training event showcasing diverse training resources on both CESSDA tools and services. They took place on November 27 and 28, 2019, and were hosted by the GESIS – Leibniz-Institute for the Social Sciences in Cologne, Germany.</p> <p>In this video, Jonas Recker discusses the concept of the “Designated Community” from the OAIS Reference Model.</p> <p>The video is also available for <a href="https://youtu.be/mTnz47X02U4">viewing on Youtube</a>.</p>
Heatwave tolerance of a marine polychaete (Hediste diversicolor): physiological and molecular data archive
<p>The aim of this work was to test the effect of long-lasting heatwaves on the intertidal polychaete <em>Hediste diversicolor</em> (24 ºC <em>vs</em> 30 ºC for a month). We analysed the whole-body proteome and carried out fatty acid analysis after 28 days of exposure and then estimated cumulative survival and upper thermal tolerance limits (after 30 days of exposure). Worms' wet weight was also compared between temperatures (after 30 days) to understand if elevated temperature has effects on growth. </p> <p>NOTE: The associated mass spectrometry proteomics dataset has been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD020635</p> <p>Dataset associated to the paper DOI:<a href="https://doi.org/10.1016/j.envres.2021.110885">10.1016/j.envres.2021.110885</a></p>
Disputed Archival Claims - 1998 and 2019 Survey Data
<p>This dataset comprises a spreadsheet containing the responses to two international surveys about displaced archives. The spreadsheet has two sheets: the first contains data pulled from Leopold Auer's 1998 report on his survey, Disputed Archival Claims, conducted for UNESCO and the International Council on Archives (ICA); the second sheet contains data received by James Lowry for his survey, conducted for the ICA's Expert Group on Shared Archival Heritage in 2018 and 2019, published in 2020.</p> <p>This dataset was compiled by James Lowry, John Moffat and Pauline Soum-Paris. It contains data collected and presented by Leopold Auer (1997/1998). Marianne Deraze contributed to the collection of the 2019 data by posting the survey to the ICA website. The data was contributed by representatives of the countries and organisations making the claims.</p>
Fracture dolomite as an archive of continental palaeo-environmental conditions
<p>Supplementary Information File (raw data; Table 1-8) to the article "Fracture dolomite as an archive of continental palaeo-environmental conditions", published in Communications Earth & Environment</p>
Landscape mosaic map archive for "Forest cover dynamics in the shifting landscape mosaic of the continental United States from 2001 to 2016"
<p>This data archive contains two zipfiles, each containing a raster map of the continental United States showing the landscape mosaic classification at 30-meter resolution as described in the citing publication.</p>
Data and code archive for 'Time perception and patience: Individual differences in interval timing precision predict choice impulsivity in European starlings, Sturnus vulgaris'
<p>Data and code for Andrews et al. '<strong>Time perception and patience: Individual differences in interval timing precision predict choice impulsivity in European starlings, <em>Sturnus vulgaris'</em></strong></p> <p>Version of November 02 2020</p> <p>The data are presented here with different degrees of processing (i.e. from every trial on every day by every bird separately in 'timing data.Rdata', to one summary row per bird in 'timing data by bird.csv'). Separate scripts do the processing, fit polynomials, reproduce the by-bird analyses in the paper, and run the numerical model. Please see the document 'Description of R scripts and files' for details of the different versions of the data and what each script does.</p> <p> </p>
READ | Recognition and Enrichment of Archival Data
<p>1<sup>st</sup> Public Project Presentation</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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