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BASBLib - a library of bilevel test problems
<p><strong>BASBLib - A Library of Bilevel Test Problems</strong></p> <p>While the literature on the application of bilevel programming problems is extensive and diverse (see<br> e.g., [1, 6, 7, 11] and references therein), there have been limited efforts to establish a systematic test library for the evaluation of the bilevel algorithms and their implementations. While there exist generators of bilevel test problems [2, 3, 4], they are limited to linear and quadratic problems. There already exist several collections of bilevel test problems, however, again, limited to special subclasses:</p> <ul> <li>Chapter 9 in [9] contains linear and quadratic problems (<strong>19 problems in total</strong>)</li> <li>The GAMS EMP Library [8] contains mainly linear and quadratic problems (<strong>33 problems in total</strong>)</li> <li>The test set included with BIPA [5] contains convex inner problems (<strong>22 problems in total</strong>)</li> <li>A test set for bilevel problems [10] containing either nonconvex inner problems or problems with a structure that causes convergence issues for algorithms (<strong>36 problems in total</strong>)</li> <li>MIPLIB [12] containing bilevel problems with only binary variables (<strong>57 problems in total</strong>)</li> <li>Bilevel optimization problem library, version 0.1 [13] containing binary bilevel problems (<strong>315 problems in total</strong>)</li> </ul> <p>Thus, with the introduction of <strong>BASBLib, </strong>we present an actively growing online collection of general bilevel test problems, gathered from the various sources and devoted to bilevel programming. The library is designed as an open resource to which other<br> researchers in the bilevel programming community can easily contribute. An in-depth description of <strong>BASBLib</strong> is provided in an online resource: <strong>http://basblsolver.github.io/BASBLib/</strong>. It includes problem statements, a geometrical analysis of the problems, the best-known solutions, comments on inaccuracies in the literature, sources where the problem was used, AMPL input files in the BASBL format, and finally instructions on how to use it and contribute to it. We welcome contributions and corrections to this resource either<br> by email: <strong>remigijus.paulavicius@imperial.ac.uk</strong> or by forking the Github repository <strong>http://basblsolver.github.io/BASBLib/</strong>: which makes to possible to add and/or correct existing information, and then create a pull request to include new contributions.</p> <p><strong>Changelog: </strong></p> <p><strong>v2.3 - (2019-07-03) </strong></p> <p><strong>- Added</strong></p> <p><strong>7 flexibility index problems:</strong></p> <ul> <li><code>bpp_2002_01_FI</code></li> <li><code>bpp_2002_02_FI</code></li> <li><code>fgi_2001_01_FI</code></li> <li><code>fgi_2001_02_FI</code></li> <li><code>gf_1987_01_FI</code></li> <li><code>gf_1987_02_FI</code></li> <li><code>rbb_2000_01_FI</code></li> </ul> <p><strong>v2.2 - (2017-09-20) </strong></p> <p><strong>- Added</strong></p> <ul> <li>mb_2007_22v problem, variant of problem mb_2007_22</li> </ul> <p><strong>- Changed</strong></p> <p>Renamed the following problems:</p> <ul> <li>nwj_2016_01 to nwj_2017_01</li> <li>nwj_2016_02 to nwj_2017_02</li> <li>nwj_2016_03 to nwj_2017_03</li> <li>nwj_2016_04 to nwj_2017_04</li> <li>nwj_2016_05 to nwj_2017_05</li> </ul> <p><strong>References</strong></p> <ol> <li>J. F. Bard, <em>Practical Bilevel Optimization</em>, vol. 30 of Nonconvex Optimization and Its Applications, Springer US, 1998, <strong>doi:10.1007/978-1-4757-2836-1</strong>.</li> <li>P. H. Calamai and L. N. Vicente, <em>Generating linear and linear-quadratic bilevel programming problems</em>, SIAM Journal on Scientific Computing, 14 (1993), pp. 770–782, <strong>doi:10.1137/0914049</strong>.</li> <li>P. H. Calamai and L. N. Vicente, <em>Generating quadratic bilevel programming test problems</em>, ACM Transactions on Mathematical Software (TOMS), 20 (1994), pp. 103–119, <strong>doi:10.1145/174603.174411</strong>.</li> <li>P. H. Calamai, L. N. Vicente, and J. J. Júdice, <em>A new technique for generating quadratic programming test problems</em>, Mathematical Programming, 61 (1993), pp. 215–231, <strong>doi:10.1007/BF01582148</strong>.</li> <li>B. Colson, <em>BIPA (</em><em>BIlevel</em><em> Programming with Approximation Methods)(software guide and test problems)</em>, Cahiers du GERAD, (2002).</li> <li>S. Dempe, <em>Foundations of Bilevel Programming</em>, vol. 61 of Nonconvex Optimization and Its Applications, Kluwer Academic Publishers, Boston, 2002, <strong>doi:10.1007/b101970</strong>.</li> <li>S. Dempe, V. Kalashnikov, G. A. Pérez-Valdés, and N. Kalashnykova, <em>Bilevel programming problems</em>, Energy Systems, Springer Berlin Heidelberg, Berlin, Heidelberg, 2015, <strong>doi:10.1007/978-3-662-45827-3</strong>.</li> <li>EMPlib Library. https://www.gams.com/emplib/libhtml/alfindx.htm. Accessed: 2017-04-21.</li> <li>C. A. Floudas, P. M. Pardalos, C. S. Adjiman, W. R. Esposito, Z. H. Gümüs, S. T. Harding, J. L. Klepeis, C. A. Meyer, and C. A. Schweiger, <em>Handbook of test problems in local and global optimization</em>, Springer Science & Business Media, 1999, <strong>doi:10.1007/978-1-4757-3040-1</strong>.</li> <li>A. Mitsos and P. I. Barton, A test set for bilevel programs. http://www.researchgate.net/publication/228455291, 2007. [Last updated September 19, 2007].</li> <li>K. Shimizu, Y. Ishizuka, and J. F. Bard, <em>Nondifferentiable and two-level mathematical programming</em>, vol. 102, Kluwer Academic Publishers, Boston, 1997, <strong>doi:10.1016/S0377-2217(97)00228-2</strong></li> <li>M. Fischetti, I. Ljubić, M. Monaci, and M. Sinnl, Intersection Cuts for Bilevel Optimization, Springer International Publishing, 2016, pp. 77–88, <strong>doi</strong><strong>:</strong><strong>10.1007/978-3-319-33461-5_7</strong></li> <li>T. Ralphs and E. Adams, Bilevel instance library, 2016, http://coral.ise.lehigh.edu/data-sets/bilevel-instances/</li> </ol> <ul> </ul>
LIBER 2019 Workshop. Open Access books in academic libraries – how can we adapt workflows and cost management to an open scholarly communications landscape?
<p>This dataset includes all the results from a workshop held at the LIBER Annual Conference 2019 on June 26, 2019, in Dublin, Ireland. The workshop aimed at collecting and discussing current library practices related to open access books.</p> <p>The dataset includes information from a survey made in preparation for the conference, where 67 European libraries responded to a questionnaire based on activities or workflows in libraries related to open access books. Both the survey questionnaire and the results from the survey are uploaded as separate files.</p> <p>The dataset also includes the presentation made by keynote speaker Eelco Ferwerda from the OAPEN Foundation. He presented results based on the 2017 landscape study report on open access monographs with some new results from more recent studies by Springer Nature and a follow-up report by Knowledge Exchange.</p> <p>Olaf Siegert from ZBW - Leibniz Information Centre for Economics presented a brief overview of what libraries can do to promote OA books in terms of collection management, publication services and development of staff and organisation. The conclusion is that it is not necessarily big changes that are needed.</p> <p>Sofie Wennström presented results from a survey aimed at European research libraries on behalf of the LIBER Open Access Working Group. The survey reveals that many libraries are already working with processes to promote OA books. This is done by libraries organising publishing services or inhouse publishing, by including OA books in discovery services and repositories and by supporting authors to learn more about open access and open licensing.</p> <p>Finally, the LIBER Open Access Working Group shares a report from the workshop providing some quick takeaways and some good examples brought up during the breakout session with the workshop participants.</p>
Data supporting the "Health Libraries Sharing Game"
<p>This dataset provides the necessary files to reproduce the "Health libraries sharing game". This game was created for the workshop "Health libraries: sharing through gaming" held in Basel on Wednesday the 19th of June 2019, as part of the EAHIL conference.</p> <p>Inspired by the game "Bucket of Doom", this game aims to help health librarians address challenging professional situations (based on real situations experienced by the authors). The players will have to be creative to overcome each challenge. </p> <p>The game is composed of cards that include possible situations to resolve, some tools, as well as resources available to solve those questions. The cards are accompanied by a "How to play" file explaining the rules of the game and a "Name sheet" file with the combination of funny names that participants can choose at the beginning of the game. The README.txt file describes the files and the content of the different folders of the dataset.</p> <p>Please consult the following article for further information about the creation of the game:</p> <p>Gómez-Sánchez, A., Kerdelhue, G., Isabel-Gómez, R., González-Cantalejo, M., Iriarte, P., & Muller, F. (2019). Health libraries: sharing through gaming. Journal of EAHIL, 15(3), 8-11. https://doi.org/10.32384/jeahil15329 </p>
The Portraits Collection Dataset of KU Leuven Libraries, Special Collections
<p>Full dataset from the Portraits Collection offered as open data for digital humanities research and other creative engagement</p>
Merged citation library dataset of 3 bibliographic databases for use in testing deduplication tools
<p>Endnote xml file of complete search results - not de-duplicated. </p> <p>Search query (cannabis based medicines, endocannabinoid system modulators and cannabinoids tested in animal models of pathological or injury-related persistent pain) conducted on 9 April 2019. </p> <p>Searched databases: Web of Science, Embase and PubMed</p> <p>The citations retrieved from the searches of each database have been merged together, there is a total of 15216 citations. </p> <p> </p>
Identifying and Implementing Relevant Research Data Management Services for the Library at the University of Dodoma, Tanzania
<p>This data set presents the results of research conducted at the University of Dodoma, Tanzania. The purpose of the research was to identify and report on relevant RDM services that need to be implemented so that researchers and university management could collaborate and make our research data accessible to the international community.</p> <p>The data set was used to support both the mini-dissertation as well as a paper published in the Data Science Journal. The journal paper presents findings on important issues for consideration when planning to develop and implement RDM services at a developing country, academic institution. The paper also mentions the requirements for the sustainability of these initiatives.</p>
Figure 3 in Endogean beetles (Coleoptera) of illustrated DNA barcode library Guatemala: deep soil sampling and
Figure 3. Neighbour Joining DNA barcode tree of 75 endogean beetles from Guatemala. Terminal names consist of the most detailed current taxonomic identification (genus, tribe, or subfamily), followed by specimen number, family name, sample number, length of the DNA barcode fragment [with the number of ambiguously read bases in square brackets], BIN number, and GenBank accession number.
Figure 2 in Endogean beetles (Coleoptera) of illustrated DNA barcode library Guatemala: deep soil sampling and
Figure 2. Sampling methods of the deep soil Guatemala beetles. (A–C) pits producing samples GT12, GT16, and GT25, respectively (note that sample GT16 is from an extremely dry habitat, while sample GT25 is twice as large in volume); (D) a floating soil sample in a barrel with water; (E) scooping floating organic foam containing live beetles on a fine mesh; (F) wet samples prior to specimen extraction; (G) two aluminium thermoeclectors of the novel larger and lighter design; (H) thermoeclectors exposed to the Sun.
Video: Creating LOD for Dutch public libraries - WikidataCon, Berlin, 25-26 October 2019
<p><span><strong>English: </strong></span> Directly inspired by a <a href="https://meta.wikimedia.org/w/index.php?title=GLAMTLV2018/Submissions/Don%27t_forget_the_libraries_-_increasing_the_coverage_in_Wikidata&oldid=18551326">lightning talk from GLAM-Wiki 2018</a> I started a project to improve the coverage and visibility of public libraries in the Netherlands on Wikidata.</p> <p>Although there are a number of websites listing all these plusminus 1300 library branches, I was quite surprised to find that none of them provide both structured and openly licensed, let alone linked data, while openness should be at the the core of every library's mission.</p> <p>I decided to solve this problem by adding address and geo data of every public library to Wikidata, hence for the very first time creating a (5-star) linked open data set for this type of library. See <a href="https://www.wikidata.org/wiki/Wikidata:WikiProject_Netherlands_Public_Libraries">https://www.wikidata.org/wiki/Wikidata:WikiProject_Netherlands_Public_Libraries</a></p> <p>This enables innovative uses, such as overviews and visualisations of the Dutch public library landscape on organisational, municipal, regional, provincial and national levels.</p> <p>This is of interest not only to Wikipedia and LOD enthousiasts, but also to e.g. policy makers, municipalities, scientists and journalists.</p> <p>In addition, to enable SPARQL generated library image galleries, I have started '1Lib1Photo' (inspired by 1Lib1Ref), an initiatieve to add an openly licensed image of every Dutch public library to Wikimedia Commons.</p> <p><strong>Takeaways</strong></p> <p>As a beginning Wikidatian myself, I hope to show that this type of project is suitable for beginners; adding address and geo infomation to Wikidata is a simple task that any one can do and a great way to learn SPARQL. Despite the simple nature of the inputs, the outputs (maps, image galleries, tables) are very relevant, not only for adding overviews of libraries to Wikipedia, but also for parties outside the Wiki sphere.</p> <p>As this project was directly inspired by work done in Wales by Simon Cobb (<a title="User:Sic19" href="https://commons.wikimedia.org/wiki/User:Sic19">User:Sic19</a>), I in my turn hope to inspire others to start similar projects in other countries.</p> <div> <h2><span></span>Context & presentation</h2> </div> <p><a href="https://www.wikidata.org/w/index.php?title=Wikidata:WikidataCon_2019/Program/Sessions/Libraries_panel&oldid=1039089450">https://www.wikidata.org/w/index.php?title=Wikidata:WikidataCon_2019/Program/Sessions/Libraries_panel&oldid=1039089450</a></p> <p>The recorded presentation was part of a <a title="d:Wikidata:WikidataCon 2019/Program/Sessions/Libraries panel" href="https://www.wikidata.org/wiki/Wikidata:WikidataCon_2019/Program/Sessions/Libraries_panel">panel about Wikidata in libraries</a></p> <p>This presentation (PDF) is also available on <a title="File:Creating LOD for Dutch public libraries - WikidataCon Berlin 25-26 October 2019 Olaf Janssen.pdf" href="https://commons.wikimedia.org/wiki/File:Creating_LOD_for_Dutch_public_libraries_-_WikidataCon_Berlin_25-26_October_2019_Olaf_Janssen.pdf">Wikimedia Commons</a> and on <a href="../records/12568513" rel="nofollow">Zenodo</a>.</p>
LangString Python Library
A Python library to handle langstrings and related datatypes.
Estimated reflectance hyperspectral libraries for Vigo sediment sample, seafloor sand samples and marine organism
<h2>Abstract</h2> <p>Estimated reflectance hyperspectral libraries created for sand samples from seafloor at Vigo sea zone 1, 2 and 3, sediment samples from Vigo fieldwork Sept. 2023 and some Vigo marine organisms such as sea cucumber, sea pens, sea stars, coral, seaweed.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Estimated reflectance hyperspectral libraries for Vigo sediment sample, seafloor sand samples and marine organism</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Estimated reflectance hyperspectral libraries created for sand samples from seafloor at Vigo sea zone 1, 2 and 3, sediment samples from Vigo fieldwork Sept. 2023 and some Vigo marine organisms such as sea cucumber, sea pens, sea stars, coral, seaweed.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Reflectance hyperspectral signature, library, sediment, sand, sea cucumber, sea star, coral, seaweed</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>25.05.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>25.05.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>CSV</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.25m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 3035</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone AS</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Hieracium alpinun PAI33838 (2n = 2x = 18) Oxford Nanopore Technology sequences library
<p>Sample 1 000 000 reads (trimmed).</p>
Understanding API Usage and Testing: An Empricial Study of C Libraries (Artifact)
<h1>LibProbe Artifact</h1> <p>For the sake of the evaluation we preprocessed the CCScanner data to identify all clients of the libraries used in our evaluation and included those in our <code>MongoDB</code> database. This artifact will start by importing this pre-processed dependency information into a docker image which is then used for the evaluation.</p> <h2>Running the docker image</h2> <ul> <li> <p>First load the docker image by running</p> <ul> <li> <div> <div><code>gunzip -c libprobe_v2.2.tar.gz | sudo docker import - libprobe:latest </code></div> </div> </li> </ul> <p>this will load the image in your local docker images.</p> </li> <li> <p>Run a container from the image: <code>sudo docker run -it --device /dev/snd --privileged libprobe:latest /bin/bash</code> This will run a docker container which maps the pulseaudio and alsa configurations from your local host to the docker image. This is necessary to get some clients for some target libraries to build correctly. You will need an ubuntu host machine which has pulseaudio and alsa installed.</p> </li> <li>Run <code>mongod --fork --logpath /var/log/mongodb/mongod.log</code> followed by <code>mongorestore --drop --db apiusage /tmp/libprobe/database/apiusage</code> to import the results saved in the artifact. </li> </ul> <h2>Validating analysis results</h2> <ul> <li>The docker image provided does not contain any clients due to size limitations on sharing. The Mongo database contains all the results of running this evaluation.</li> <li>We provide a script <code>clone_clients.py</code> with a <code>client_repos.json </code>file in <code>/tmp/libprobe/extra</code> which can be used to clone the clients in the clients directory. </li> <li>To get the results it's possible to run <code>python3 libprobe.py analyse all -n</code> from <code>/tmp/libprobe</code>. This will overwrite the JSON files in the <code>json_files</code> directory and overwrite the graphs in the <code>graphs</code> directory.</li> </ul> <h2>Running the evaluation for one library (vorbis)</h2> <ul> <li>Download clients: Go to <code>/tmp/libprobe</code> and run <code>python3 libprobe.py download vorbis</code></li> <li>Prepare the library: <ul> <li>In /tmp/data/libraries/xiph@@vorbis run make clean then make and make check and make install.</li> <li>Copy all C files from /tmp/data/libraries/xiph@@vorbis/lib to /tmp/data/libraries/xiph@@vorbis/lib/.libs to collect accurate API coverage information.</li> </ul> </li> <li>Process the library to get the APIs and the coverage information : <code>python3 libprobe.py processlib vorbis</code></li> <li>Prepare clients for excluding sub directories that might contain vorbis library code: <code>python3 libprobe.py prepclients vorbis</code></li> <li>Get client usages: <code>python3 libprobe.py fetchusages vorbis</code></li> <li>Analyse: <code>python3 libprobe.py analyse vorbis -n</code></li> <li>(optional) Measure differential coverage for improved coverage libs: <code>python3 libprobe.py coverage vorbis</code></li> </ul> <h2>Running the evaluation for all libraries (this requires at least 300GB of disk space)</h2> <ul> <li>Download clients: Go to <code>\tmp\libprobe</code> and run <code>python3 libprobe.py download all</code></li> <li>Process the libraries: <code>python3 libprobe.py processlib all</code></li> <li>Prepare clients: <code>python3 libprobe.py prepclients all</code></li> <li>Get usages: <code>python3 libprobe.py fetchusages all</code></li> <li>Analyse: <code>python3 libprobe.py analyse all -n</code></li> <li>(optional) Measure differential coverage for improved coverage libs: <code>python3 libprobe.py coverage <library></code></li> </ul> <h2>Getting baseline coverage for libraries</h2> <p>All libraries are cloned in <code>/tmp/data/libraries</code> and clients are cloned in <code>/tmp/data/clients</code>.</p> <div> </div> <ul> <li>MBedtls: Copy the script <code>coverage_mbedtls.sh</code> from <code>/tmp/libprobe/extra</code> into the the build directory of Mbedtls <code>/tmp/data/libraries/Mbed-TLS@@mbedtls/build</code> and run <code>./coverage_mbedtls.sh</code> followed by <code>genhtml baseline.info --output-directory</code> out this will calculate the baseline coverage for mbedtls.</li> <li>FFTW: Copy the script coverage.sh from /tmp/libprobe/extra into the the root dir of FFTW and run ./coverage.sh baseline this will calculate the baseline coverage for fftw</li> <li>HDF5: Copy the script coverage_hdf.sh from /tmp/libprobe/extra into the the root dir of HDF and run <code>./coverage_hdf.sh baseline</code> this will calculate the baseline coverage for HDF.</li> <li>LMDB: Copy the script coverage_lmdb.sh from /tmp/libprobe/extra into the <code>/tmp/data/libraries/LMDB@@lmdb/libraries/liblmdb</code> and run <code>./coverage_lmdb.sh baseline</code> this will calculate the baseline coverage for LMDB.</li> <li>Zip: Copy the script coverage_zip.sh from /tmp/libprobe/extra into <code>/tmp/data/libraries/kuba--@@zip/build/CMakeFiles/zip.dir/sr</code>c and run <code>./coverage_zip.sh baseline</code> this will calculate the baseline coverage for zip.</li> <li>Vorbis: Copy the script cal_cov.py from /tmp/libprobe/extra to <code>/tmp/data/libraries/xiph@@vorbis/lib/.libs</code> and then copy all source files in the <code>.libs</code> folder by running <code>cp ../*.c .</code> from the <code>.libs</code> folder. Finally run <code>python3 cal_cov.py .</code>.</li> <li>XXhash: Copy the script coverage.sh from /tmp/libprobe/extra into /tmp/data/libraries/Cyan4973@@xxHash and run ./coverage.sh baseline this will calculate the baseline coverage for xxhash</li> </ul> <h2>Reproducing increased coverage using clients</h2> <ul> <li> <p>LMDB: The client we will use is Knot DNS.</p> <ul> <li>Change directory to <code>/tmp/data/clients/CZ-NIC@@knot</code> and run <code>autogen.sh</code>.</li> <li>Run <code>./configure --with-lmdb=/usr/local</code>.</li> <li>Then <code>make && make check</code>.</li> </ul> <p>Now go back to the LMDB directory and run</p> <ul> <li> <p>Run <code>./coverage_lmdb.sh after_knot</code>.</p> </li> <li> <p>Now go /tmp/libprobe and run <code>python3 libprobe.py coverage lmdb</code></p> </li> </ul> </li> <li> <p>VORBIS: The client we will use in SFML.</p> <p>Go to the vorbis library dir <code>/tmp/data/libraries/xiph@@vorbis</code> and run <code>make clean</code>.</p> <ul> <li>Run <code>make && make check && make install</code>.</li> <li>Go to the <code>.libs</code> folder and copy all c files there by doing <code>cp ../*.c .</code>.</li> <li>Copy <code>/tmp/libprobe/extra/cal_cov.py</code> into the <code>.libs</code> folder and run <code>python3 cal_cov.py .</code>. This will show the baseline coverage.</li> </ul> <p>Go to the SFML directory <code>/tmp/data/clients/SFML@@SFML</code>.</p> <ul> <li>Create build directory <code>mkdir build && cd build</code>.</li> <li>Run <code>cmake -DSFML_BUILD_TEST_SUITE=TRUE -GNinja ..</code>.</li> <li>Run <code>ninja</code>.</li> <li>Run <code>ctest</code>. You will see some failing tests. Thats normal as we are only interested in the Audio tests for vorbis. All Audio tests should pass.</li> </ul> <p>Go back to the <code>.libs</code> folder in vorbis and re-run the <code>cal_cov.py</code> script.</p> <ul> <li>Now go /tmp/libprobe and run <code>python3 libprobe.py coverage vorbis</code></li> </ul> </li> </ul> <ul> <li>SDL: The client we will use in UFOAI.<br> <ul> <li> <p>Go to the SDL library dir <code>/tmp/data/libraries/libsdl-org\@\@SDL</code> and then the <code>build2</code> directory where the built library is. .</p> </li> <li>Run <code>make clean && make && make install && make test</code>.</li> <li>Copy <code>/tmp/libprobe/extra/coverage_sdl.sh</code> into the <code>build2</code> folder and run <code>./coverage_sdl.sh baseline</code>. Run <code>genhtml baseline.info --output-directory out</code></li> </ul> </li> </ul> <p> Go to the UFOAI directory <code>/tmp/data/clients/ufoaiorg\@\@ufoai</code></p> <ul> <li> <ul> <li>Run <code>./configure --target-os=linux --disable-uforadiant</code> && <code>make</code>.</li> <li>Run .<code>/testall</code></li> </ul> </li> </ul> <p> Now go /tmp/libprobe and run <code>python3 libprobe.py coverage sdl</code></p> <p> </p> <ul> <li>FFTW: The client we will use in CAVA.</li> </ul> <p> Go to the FFTW3 library dir <code>/tmp/data/libraries/FFTW@@fftw3</code> and run <code>reset_cov.sh</code> then <code>make clean</code></p> <ul> <li> <ul> <li>Run <code>make && make install && make check</code>.</li> <li>Copy <code>/tmp/libprobe/extra/coverage.sh</code> into root directory of the library and run the <code>.libs</code> folder and run <code>./coverage.sh baseline</code> This will show the baseline coverage.</li> </ul> </li> </ul> <p> Go to the CAVA directory <code>/tmp/data/clients/karlstav@@cava</code>.</p> <ul> <li> <ul> <li>Run <code>./autogen.sh</code> followed by <code>./configure</code> then <code>make</code></li> <li>Run the script <code>./run_all_tests.sh</code> . </li> </ul> </li> </ul> <p> Go back to the FFTW3 library and run <code>./coverage.sh after_cava</code></p> <ul> <li> <ul> <li>Now go /tmp/libprobe and run <code>python3 libprobe.py coverage fftw3</code></li> </ul> </li> </ul>
Linked collectors and determiners for: A checklist of the bats of Peninsular Malaysia and progress towards a DNA barcode reference library.
Natural history specimen data linked to collectors and determiners held within, "A checklist of the bats of Peninsular Malaysia and progress towards a DNA barcode reference library". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6ea2cc5c-857b-4b47-8135-8bff7efbd1fc">https://bionomia.net/dataset/6ea2cc5c-857b-4b47-8135-8bff7efbd1fc</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6ea2cc5c-857b-4b47-8135-8bff7efbd1fc">https://gbif.org/dataset/6ea2cc5c-857b-4b47-8135-8bff7efbd1fc</a>. Formatted as a Frictionless Data package.
Systematic Scoping Literature Review of Embryonic Stem Cells In Vitro Developmental Toxicity Tests: Included publications library
<p>The publications included in the systematic scoping review conducted according to the protocol:<a href="https://zenodo.org/record/2528920">https://zenodo.org/record/2528920</a></p> <p> </p>
BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space
<p>BonMOLière is a set of small-sized libraries of readily purchasable compounds, optimized to produce genuine hits in biological screens across the protein space. The corresponding research article has been published in <em>Int. J. Mol. Sci.</em> 2021, 22(15), 7773, DOI: <a href="https://doi.org/10.3390/ijms22157773">https://doi.org/10.3390/ijms22157773</a></p>
Vorau Abbey library Cod. 253 dataset for Document Layout Analysis
<p>VORAU-253 is a music manuscript referred to as Cod. 253 of the Vorau Abbey library, which was provided by the Austrian Academy of Sciences. It is written in German Gothic notation and dated around year 1450.</p> <p>This manuscript is interesting because of the complexity of its layout, where staff, text and decorations are intertwined to<br> compose the structure of the document.</p> <p>This database is a subset of 228 pages of the archive, using 128 randomly selected pages for training/validation and 100 for test.</p> <p>The database was manually annotated into the following three layout regions:</p> <p>* staff: represents the regions that contains a set of horizontal lines and spaces where each one represent a different musical pitch. This region type does not contain text lines. Hence, no baselines.</p> <p>* lyrics: are the words that are sung appear below their corresponding staff, and other text in the document. In all cases, text to be sung and the other text are assigned to different layout regions under the lyrics label.</p> <p>* drop-capital: is a decorated letter that might appear at the beginning of a word or text line. As it is a single big letter, it contain no text lines nor baselines.</p> <p>On average each page contains 12.5 [7,23] text lines distributed over an average of 10.5[7,15] ``lyrics'' regions. Moreover, each page contains 22.3[14,28] layout regions on average.</p>
Comprehensive analysis of commercial fragment libraries.
<p><strong>Description of the files</strong></p> <p>Fragments from libraries:</p> <ul> <li><strong>allFragments_annotations.sdf:</strong> SDF file containing the 3D conformation of all unique 512284 fragments. <ul> <li>Descriptors present on the file: Library type (type), Library size (size) and the number of times the fragment is present in the ensemble of libraries (Frequency).</li> </ul> </li> <li><strong>unique_nonbiased_MW_logP_PBF.sdf:</strong> SDF file containing the ensemble of unique fragments from the non-chemically biased libraries. <ul> <li>Descriptors present on the file: Canonical Smiles (Canonical_Smiles), Molecular Weight (MW), Partition logarithm (ALogP) and Plane of best fit value (PBF).</li> </ul> </li> <li><strong>unique_nonbiased_principal_descriptors.tsv:</strong> TSV file containing the ensemble of unique fragments from the non-chemically biased libraries with the descriptors that were explicitely used in the article. <ul> <li>Descriptors present on the file: Canonical Smiles (Canonical_Smiles), molecular weight (MW), partition logarithm (ALogP) and plane of best fit value (PBF), number of heavy atoms (HAC), number of hydrogen bond acceptors and donors (HBA and HBD), number of rotatable bonds (NRot), number of violations of the rule of three (Ro3_Vio) and quantitative estimate of<strong> </strong>drug-likeness (QED).</li> </ul> </li> <li><strong>unique_nonbiased_descriptors.tsv:</strong> TSV file containing the ensemble of unique fragments from the non-chemically biased libraries with all the calculated descriptors.</li> </ul> <p>GTM model:</p> <ul> <li><strong>training_set_GTM.sdf: </strong>SDF file containing the set of 6017 fragments used for training the GTM model.</li> <li><strong>Model_IIAB2-4_cycle_Freq_05_m301.xml:</strong> xml file of the generated model allowing the generation of GTM landscapes.</li> </ul> <p>Scaffold data:</p> <ul> <li><strong>scaffold_data.tsv:</strong> TSV file containing the scaffold data. For an easy exploration of the file please refer to: <a href="https://gtmfrag.drugdesign.unistra.fr/">https://gtmfrag.drugdesign.unistra.fr/</a></li> </ul>
Real data supporting HTTomolib/GPU libraries
<p>This is an archive containing real data for testing methods of <a href="https://github.com/DiamondLightSource/httomolib">HTTomoLib</a> and <a href="https://github.com/DiamondLightSource/httomolibgpu">HTTomoLibGPU</a> packages. Also see Python scripts and images. </p> <div> <h2>Data description:</h2> </div> <h3>I12 Beamline data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><strong>i12_dataset1.npz</strong> (119647.nxs)</td> <td>180-degrees scan of a sandstone rock</td> <td> <p>Data dimensions <code>[1801, 50, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[50, 50, 2560]</code> and darks are <code>[20, 50, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>The resolution of the sample is well-defined and the sample is fully within the field of view (FOV). This provides some freedom for experimentation using various preprocesing and reconstruction methods.</td> </tr> <tr> <td><strong>i12_dataset2.npz </strong>(146344.nxs)</td> <td>180-degrees scan</td> <td> <p>Data dimensions <code>[2050, 50, 2560]</code> as <code>[angles, detY, detX]</code>, flats and darks are <code>[51, 50, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td> <p>The sample is larger than FoV and noisy. VoCentering method to find the centre of rotation can be tested on this data.</p> </td> </tr> <tr> <td> <p><strong>i12_dataset3.npz </strong>(119647.nxs)</p> </td> <td>180-degrees scan of a sandstone rock</td> <td> <p>projections, darks and flats are <code>[2050, 2560]</code> as <code>[detY, detX]</code>. Data type is <code>uint16</code>.</p> </td> <td>This data is suitable to test the centering method based on the phase cross-correlation</td> </tr> <tr> <td><strong>i12_dataset4.npz </strong>(<a href="https://zenodo.org/records/1443568">68067.nxs</a>)</td> <td>180-degrees scan</td> <td>Data dimensions <code>[2050, 30, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[50, 30, 2560]</code> and darks are <code>[20, 30, 2560].</code> Data type is <code>uint16</code>.</td> <td>This data is well suitable for testing stripe (ring) removal artefacts and is a part of this <a href="https://zenodo.org/records/1443568">dataset</a>. </td> </tr> <tr> <td><strong>i12_dataset5.npz </strong>(162289.nxs)</td> <td>360-degrees scan</td> <td>Data dimensions <code>[3601, 15, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[101, 15, 2560]</code> and darks are <code>[101, 15, 2560].</code> Data type is <code>uint16</code>.</td> <td>360 degrees scan with the left rotation. Testing 360 to 180 conversion methods.</td> </tr> <tr> <td><strong>i12_dataset6.npz </strong>(164301.nxs)</td> <td>180-degrees scan, bio-sample</td> <td>Data dimensions <code>[1801, 50, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[50, 50, 2560]</code> and darks are <code>[20, 50, 2560]</code>. Data type is <code>uint16</code>.</td> <td>Biological sample of a very poor contrast. Phase contrast method needs to be applied to boost the contrast up and then reconstruction with the increased padding of the detector. Iterative methods can be tested. </td> </tr> </tbody> </table> <h3>I13 Beamline data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td> <p><strong>i13_dataset1.npz </strong>(179623.nxs)</p> </td> <td>360-degrees scan of some biological sample</td> <td> <p>Projection data dimensions <code>[6001, 10, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[40, 10, 2560]</code> and darks are <code>[40, 10, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>This is a 360-degrees scan than needs to be converted into traditional 180-degrees data. The stitched data enlarges the FoV of the detector while reduced the angular dimension.</td> </tr> <tr> <td><strong>i13_dataset2.npz</strong> (179468.nxs)</td> <td>180-degrees scan of some biological sample.</td> <td> <p>Projection data dimensions <code>[2501, 10, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[40, 10, 2560]</code> and darks are <code>[40, 10, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>A well defined resolution for this biological sample.</td> </tr> <tr> <td><strong>i13_dataset3.npz </strong>(180622.nxs)</td> <td>360-degrees scan</td> <td> <p>Projection data dimensions <code>[6001, 3, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[40, 3, 2560]</code> and darks are <code>[40, 3, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>This is a 360-degrees scan than needs to be converted into traditional 180-degrees data. The stitched data enlarges the FoV of the detector, while reduced the angular dimension. </td> </tr> </tbody> </table> <h3>K11 Beamline data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><strong>k11_dataset1.npz </strong>(k11-37101)<strong><br></strong></td> <td>180-degrees scan of some soil/rock sample.</td> <td>Projection data dimensions <code>[4001, 10, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[100, 10, 2560]</code> and darks are <code>[100, 10, 2560]</code>. Data type is <code>uint16</code>.</td> <td>The sample is within the FOV.</td> </tr> <tr> <td><strong>k11_dataset2.npz </strong>(k11-38750)</td> <td>180-degrees scan of some soil/rock sample.</td> <td>Projection data dimensions <code>[521, 25, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[10, 25, 2560]</code> and darks are <code>[10, 25, 2560]</code>. Data type is <code>uint16</code>.</td> <td>The sample is larger than the FOV and <span>acquired with a</span> very quick (1ms) exposure. Great for testing with iterative methods. </td> </tr> </tbody> </table> <h3>Simulated data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><strong>geant4_dataset1.npz</strong></td> <td>Geant4 simulated tomographic dataset in 180-degrees parallel-beam. </td> <td> <p>Data dimensions <code>[360, 540, 640]</code> as <code>[angles, detY, detX]</code>, flats and darks are <code>[20, 50, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>The data is perfectly centered and within FOV. Various reconstruction methods can be tested, direct and iterative.</td> </tr> <tr> <td><strong>synth_tomophantom1.npz</strong></td> <td> <p><a href="https://github.com/dkazanc/TomoPhantom">TomoPhantom</a> simulated data of 3d Shepp-Logan with noise and stripe artefacts in 180-degrees parallel beam</p> </td> <td> <p>Data dimensions <code>[400, 314, 600]</code> as <code>[detY, angles, detX]</code>, data type is <code>float32</code>.</p> </td> <td>The data is perfectly centered and normalised with negative log taken. Quite a significant number of stripe artefacts present.</td> </tr> </tbody> </table>
KinFragLib: Combinatorial library
<p><strong>KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination.</strong></p> <p><strong>Project description.</strong></p> <p>Protein kinases play a crucial role in many cell signaling processes, making them one of the most important families of drug targets. In this context, fragment-based drug design strategies have been successfully applied to develop novel kinase inhibitors, usually following a knowledge-driven approach to optimize a focused set of fragments to a potent kinase inhibitor.</p> <p>Alternatively, KinFragLib is a new method that allows to explore and extend the chemical space of kinase inhibitors using data-driven fragmentation and recombination, built on available structural kinome data from the KLIFS database for over 3,200 kinase DFG-in complexes. The computational fragmentation method splits the co-crystallized non-covalent kinase inhibitors into fragments with respect to their 3D proximity to six predefined functionally relevant subpocket centers. The resulting fragment library consists of six subpocket pools with over 9,000 fragments, available at <a href="https://github.com/volkamerlab/KinFragLib">https://github.com/volkamerlab/KinFragLib</a>.</p> <p>KinFragLib offers two main applications: (i) In-depth analyses of the chemical space of known kinase inhibitors, subpocket characteristics and connections, as well as (ii) subpocket-informed recombination of fragments to generate potential novel inhibitors. The latter showed that recombining only a subset of 722 representative fragments generated a combinatorial library of 11.3 million molecules, containing, besides some known kinase inhibitors, more than 99% novel chemical matter compared to ChEMBL and 56% molecules compliant with Lipinski's rule of five.</p> <p><strong>Combinatorial library dataset.</strong></p> <p>The dataset offered here is part of the KinFragLib GitHub repository (<a href="https://github.com/volkamerlab/KinFragLib">https://github.com/volkamerlab/KinFragLib</a>) and contains the metadata and properties of the KinFragLib combinatorial library.</p> <p><strong>1. Raw data</strong></p> <ul> <li><em>combinatorial_library.json</em>: Full combinatorial library, please refer to <em>notebooks/4_1_combinatorial_library_data_preparation.ipynb</em> at <a href="https://github.com/volkamerlab/KinFragLib">https://github.com/volkamerlab/KinFragLib</a> for detailed information about this data format.</li> <li><em>combinatorial_library_deduplicated.json</em>: Deduplicated combinatorial library (based on InChIs).</li> <li><em>chembl_standardized_inchi.csv</em>: Standardized ChEMBL 36 molecules in the form of InChI strings.</li> <li><em>klifs_download_summary.csv</em>: PDB codes of all KLIFS structures used to generate the KinFragLib fragmentation library. </li> </ul> <p><strong>2. Processed data</strong></p> <p>Data extracted from <em>combinatorial_library_deduplicated.json</em>, performed in <em>notebooks/4_1_combinatorial_library_data_preparation.ipynb</em> at <a href="https://github.com/volkamerlab/KinFragLib">https://github.com/volkamerlab/KinFragLib</a>.</p> <ul> <li><em>n_atoms.csv</em>: Number of atoms for each recombined ligand.</li> <li><em>ro5.csv</em>: Number of ligands that fulfill Lipinski's rule of five (Ro5) and its individual criteria; number of ligands in total.</li> <li><em>subpockets.csv</em>: Number of ligands per subpocket combination.</li> <li><em>original_exact.json</em>: Ligands with exact matches in original ligands, i.e. KLIFS ligands that were used for the fragmentation.</li> <li><em>original_substructure.json</em>: Ligands with substructure matches in original ligands, i.e. KLIFS ligands that were used for the fragmentation.</li> <li><em>chembl_exact.json</em>: Ligands with exact matches in ChEMBL.</li> <li><em>chembl_most_similar.json</em>: Most similar ligand in ChEMBL for each recombined ligand.</li> <li><em>chembl_highly_similar.json</em>: Most similar ligand in ChEMBL for each recombined ligand with similarity greater than 0.9.</li> </ul> <p><strong>Usage.</strong></p> <p>This dataset can be used to run the notebooks available on <a href="https://github.com/volkamerlab/KinFragLib">https://github.com/volkamerlab/KinFragLib</a>.</p> <ol> <li>Clone the KinFragLib repository.</li> <li>Download the tar.bz2 file provided here.</li> <li>Extract the archive content to the combinatorial library folder in your local KinFragLib folder and run the notebooks.</li> </ol> <pre><code>tar -xvf combinatorial_library.tar.bz2 -C /path_to_kinfraglib/data/combinatorial_library/</code></pre> <p> <strong>Citation.</strong></p> <p>This dataset is part of the KinFragLib publication:</p> <p>Sydow, D., Schmiel, P., Mortier, J., and Volkamer, A. KinFragLib: Exploring the Kinase Inhibitor Space Using Subpocket-Focused Fragmentation and Recombination. <em>J. Chem. Inf. Model.</em> <strong>2020</strong>. <a href="https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c00839">https://pubs.acs.org/doi/abs/10.1021/acs.jcim.0c00839</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.