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PDB70 Structural Library and Metadata
<p>Protein structural library developed from the PDB70 sequence database as well as associated metadata for each structure. The structures associated with each entry in the PDB70 structural library are provided in a tarball. When available, the metadata for a protein has been gathered from UniProtKB flat files. A focus is given to enzyme commission (EC) number metadata as this type of ontology is of main focus for identifying and classifying enzymes within a proteome. </p> <p>Changelog: </p> <p>v1.0.2 - Added list file for structures that have active EC numbers, accounting for defunct EC numbers in UniProtKB metadata being replaced with an active EC number when relevant.</p> <p>v1.0.1 - Small clean up of the provided .ipynb document. </p> <p>v1.0.0 - Original push of the PDB70 structural database and metadata.</p>
Maintenance of Plan Libraries for Case-based Planning
<p>Case-based planning is an approach to planning where previous planning experience provides guidance to solving new problems. Such a guidance can be extremely useful, or even necessary, when the new problem is very hard to solve, or the stored previous experience is highly valuable, because, e.g., it was provided or validated by human experts, and the system should try to reuse it as much as possible. <br> <br> To do so, a case-based planning system stores in a library previous planning experience in the form of already encountered problems and their solutions. </p> <p>The quality of such a plan library critically influences the performance of the planner, and therefore it needs to be carefully designed and created. For this reason, it is also important to update the library during the lifetime of the system, as the type of problems being addressed may evolve or differ from the ones the library was originally designed for. Moreover, like in general case-based reasoning, the library needs to be maintained at a manageable size, otherwise the computational cost of querying it grows excessively, making the entire approach ineffective.</p>
MBC and ECBL Libraries: outstanding tools for drug discovery
<p><strong>UPDATE</strong>. New in this revision: python scripts to process DBs and calculate the percentage of molecules which pass the Veber and Ghose filters. Two new DBs were also added and considered for the analysis.</p> <p>Data and scripts to reproduce all the graphics reported in the Manuscript entitled: "MBC and ECBL Libraries: outstanding tools for drug discovery".</p> <p><strong>List of analyzed DBs:</strong></p> <ol> <li>MBC2016 (Total entries: 1,096 cmpds; 7.39% excluded from properties analysis - QikProp failure).</li> <li>MBC2022 (Total entries: 2,577 cmpds; 3.14% excluded from properties analysis - QikProp failure).</li> <li>ECBL (Total entries: 101,021 cmpds; 0.20% excluded from properties analysis - QikProp failure).</li> <li>ChEMBL v.31 (Total entries 1,908,325 cmpds; 2.97% excluded from properties analysis - QikProp failure).</li> <li>DrugBank v.5.0 (Total entries 10,981 cmpds; 4.13% excluded from properties analysis - QikProp failure).</li> <li>ZINC20 (Total entries 10,723,360 cmpds; 0.61% excluded from properties analysis - QikProp failure).</li> <li>NuBBE (Total entries 2,223 cmpds) - <strong>NEW</strong></li> <li>Approved drugs (Total entries: 3,140 cmpds) - <strong>NEW</strong></li> </ol> <p><strong>Files:</strong></p> <p><em>QikProp_properties.docx</em>: doc file containing the full list of QikProp properties calculated for each analyzed DB.</p> <p><em>DATA_comparison.xlsx</em>: excel file containing data used to reproduce plots in <strong>Figure 4</strong> of the MS.</p> <ul> <li><em>Murcko_scaffold_percentages</em>: distribution (%) of the first 50 most populated Murcko scaffolds for MBC2016, MBC2022 and ECBL.</li> <li><em>Murcko_scaffolds_comparison</em>: distribution (count) of the first 94 common Murcko scaffolds for MBC2016, MBC2022 and ECBL.</li> </ul> <p>QikProp properties for all the analyzed DBs (8 files; CSV format).</p> <p>SMILES codes for all the analyzed DBs (8 files; SMI format). </p> <p><em>joinplots.py</em>: python script to generate the 2D plots in <strong>Figure 2</strong> of the MS.</p> <p><em>fingerprint_similarity.py</em>: python script to run and generate the Tanimoto similarity plots in <strong>Figure 3</strong> of the MS.</p> <p><em>calc_kde.py</em>: python script to run kernel density analysis reported in <strong>Figure 5 </strong>of the MS.</p> <p><em>Veber_filter.py: python script to generate </em>data presented in <strong>Table 1 </strong>of the MS. <strong>(NEW)</strong></p> <p><em>Ghose filter.py: python script to generate </em>data presented in <strong>Table 1 </strong>of the MS. <strong>(NEW)</strong></p>
Figure 1. OER faculty support for academic libraries
<p>The deriving out of the answers given to questions about the extent to which Academic Libraries have supported and guided them to a. OER adoption/use, b. OER creation and c. OER intellectual property (copyright), it is revealed that the faculty of the research sample does not consider academic libraries much supportive of them in any of these three areas [1]. More precisely, only 22% of the survey’ s participants believe that academic libraries have guided them on OER intellectual property (copyright), another 26% consider that academic libraries have supported them in the OER creation, and less than one in three (30%) are of the opinion that the academic libraries have contributed to their OER adoption/use (see figure 1). </p>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
A biodiversity dataset graph: Biodiverity Heritage Library (BHL)
<p>A biodiversity dataset graph: BHL</p> <p>The intended use of this archive is to facilitate (meta-)analysis of the Biodiversity Heritage Library (BHL). The Biodiversity Heritage Library improves research methodology by collaboratively making biodiversity literature openly available to the world as part of a global biodiversity community.</p> <p>This dataset provides versioned snapshots of the BHL network as tracked by Preston [2] between 2019-05-19 and 2020-05-09 using "preston update -u https://biodiversitylibrary.org".</p> <p>The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the BHL content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 . </p> <p>To retrieve and verify the downloaded BHL biodiversity dataset graph, first concatenate all the downloaded preston-*.tar.gz files (e.g., cat preston-*.tar.gz > preston.tar.gz). Then, extract the archives into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using:</p> <p>$ java -jar preston.jar clone --remote https://zenodo.org/record/3849560/files</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history<br> <0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> .<br> <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> <http://purl.org/pav/previousVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> .<br> <hash://sha256/7582d5ba23e0d498ca4f55c29408c477d0d92b4fdcea139e8666f4d78c78a525> <http://purl.org/pav/previousVersion> <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> .<br> <hash://sha256/a70774061ccded1a45389b9e6063eb3abab3d42813aa812391f98594e7e26687> <http://purl.org/pav/previousVersion> <hash://sha256/7582d5ba23e0d498ca4f55c29408c477d0d92b4fdcea139e8666f4d78c78a525> .<br> <hash://sha256/007e065ba4b99867751d688754aa3d33fa96e6e03133a2097e8a368d613cd93a> <http://purl.org/pav/previousVersion> <hash://sha256/a70774061ccded1a45389b9e6063eb3abab3d42813aa812391f98594e7e26687> .<br> <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> <http://purl.org/pav/previousVersion> <hash://sha256/007e065ba4b99867751d688754aa3d33fa96e6e03133a2097e8a368d613cd93a> .<br> <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> <http://purl.org/pav/previousVersion> <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> .<br> <hash://sha256/e46cd4b0d7fdb51ea789fa3c5f7b73591aca62d2d8f913346d71aa6cf0745c9f> <http://purl.org/pav/previousVersion> <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> .<br> <hash://sha256/9215d543418a80510e78d35a0cfd7939cc59f0143d81893ac455034b5e96150a> <http://purl.org/pav/previousVersion> <hash://sha256/e46cd4b0d7fdb51ea789fa3c5f7b73591aca62d2d8f913346d71aa6cf0745c9f> .<br> <hash://sha256/1448656cc9f339b4911243d7c12f3ba5366b54fff3513640306682c50f13223d> <http://purl.org/pav/previousVersion> <hash://sha256/9215d543418a80510e78d35a0cfd7939cc59f0143d81893ac455034b5e96150a> .<br> <hash://sha256/7ee6b16b7a5e9b364776427d740332d8552adf5041d48018eeb3c0e13ccebf27> <http://purl.org/pav/previousVersion> <hash://sha256/1448656cc9f339b4911243d7c12f3ba5366b54fff3513640306682c50f13223d> .<br> <hash://sha256/34ccd7cf7f4a1ea35ac6ae26a458bb603b2f6ee8ad36e1a58aa0261105d630b1> <http://purl.org/pav/previousVersion> <hash://sha256/7ee6b16b7a5e9b364776427d740332d8552adf5041d48018eeb3c0e13ccebf27> .</p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca file:/home/preston/preston-bhl/data/e0/c1/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca OK CONTENT_PRESENT_VALID_HASH 49458087 hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca<br> hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99 file:/home/preston/preston-bhl/data/1a/57/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99 OK CONTENT_PRESENT_VALID_HASH 25745 hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99<br> hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c file:/home/preston/preston-bhl/data/85/ef/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c OK CONTENT_PRESENT_VALID_HASH 519892 hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c<br> hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743 file:/home/preston/preston-bhl/data/25/1e/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743 OK CONTENT_PRESENT_VALID_HASH 787414 hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743</p> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".</p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README</p> <p>-- executable java jar containing preston[2] v0.1.15. --<br> preston.jar</p> <p>-- preston archives containing BHL data files, associated provenance logs and a provenance index --<br> preston-[00-ff].tar.gz</p> <p>-- individual provenance index files --<br> 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a<br> 2b1104cb7749e818c9afca78391b2d0099bbb0a32f2b348860a335cd2f8f6800<br> 4081bc59dff58d63f6a86c623cb770f01e9a355a42495b205bcb538cd526190f<br> 47a2816f8b5600b24487093adcddfea12434cc4f270f3ab09d9215fbdd546cd2<br> 6f99a1388823fca745c9e22ac21e2da909a219aa1ace55170fa9248c0276903c<br> 7ae46d7cd9b5a0f5889ba38bac53c82e591b0bdf8b605f5e48c0dce8fb7b717f<br> 82903464889fea7c53f53daedf4e41fa31092f82619edeb3415eb2b473f74af3<br> 9e8c86243df39dd4fe82a3f814710eccf73aa9291d050415408e346fa2b09e70<br> a8308fbf4530e287927c471d881ce0fc852f16543d46e1ee26f1caba48815f3a<br> bcec6df2ea7f74e9a6e2830d0072e6b2fbe65323d9ddb022dd6e1349c23996e2<br> cfe47c25ec0210ac73c06b407beb20d9c58355cb15bae427fdc7541870ca2e4e<br> f73fc9e70bce8f21f0c96b8ef0903749d8f223f71343ab5a8910968f99c9b8b6</p> <p>--- end of file descriptions ---</p> <p><br> References</p> <p>[1] Biodiversity Heritage Library (BHL, https://biodiversitylibrary.org) accessed from 2019-05-19 to 2020-05-09 with provenance hash://sha256/34ccd7cf7f4a1ea35ac6ae26a458bb603b2f6ee8ad36e1a58aa0261105d630b1.<br> [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .</p> <p><br> This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.</p>
BAGS: an automated barcode, audit & grade system for DNA barcode reference libraries
<p>Biodiversity studies greatly benefit from molecular tools, such as DNA metabarcoding, which provides an effective identification tool in biomonitoring and conservation programmes. The accuracy of species-level assignment, and consequent taxonomic coverage, relies on comprehensive DNA barcode reference libraries. The role of these libraries is to support species identification, but accidental errors in the generation of the barcodes may compromise their accuracy. Here we present an R-based application, BAGS (Barcode, Audit & Grade System; https://github.com/tadeu95/BAGS), that performs automated auditing and annotation of cytochrome c oxidase subunit I (COI) sequences libraries, for a given taxonomic group of animals, available in the Barcode of Life Data System (BOLD). This is followed by implementing a qualitative ranking system that assigns one of five grades (A to E) to each species in the reference library, according to the attributes of the data and congruency of species names with sequences clustered in Barcode Index Numbers (BINs). Our goal is to allow researchers to obtain the most useful and reliable data, highlighting and segregating records according to their congruency. Different tests were performed to perceive its usefulness and limitations. BAGS fulfils a significant gap in the current landscape of DNA barcoding research tools by quickly screening reference libraries to gauge the congruence status of data and facilitate the triage of ambiguous data for posterior review. Thereby, BAGS has the potential to become a valuable addition in forthcoming DNA metabarcoding studies, in the long term contributing to globally improve the quality and reliability of the public reference libraries.</p>
Library Service Platforms (LSPs) characteristics classification via DELPHI method raw data
<p>The purpose of the research that is related to the dataset is to identify the innovative features of the LSPs (Library Service Platforms) that differentiate them from the LMS (Library Management Systems), as well as to evaluate their importance, based on the opinions of the Greek information scientists. The method used is the Delphi 2-round questionnaire. The dataset contains the results of the Delphi method. Each sheet contains the ranking results from a group of 17 experts from two rounds of the DELPHI method.</p>
Poster for the Physics Divisional Library of the Göttingen State and University Library
<p>This poster has been created to celebrate the Physics Divisional Library of the Göttingen State and University Library.<br> https://www.sub.uni-goettingen.de/en/locations-facilities/locations-and-opening-hours/physics-divisional-library/</p>
Fig 5 in A checklist of the bats of Peninsular Malaysia and progress towards a DNA barcode reference library
Fig 5. Neighbour-joining tree showing all available DNA barcodes for species in family Rhinolophidae reported from Peninsular Malaysia. The percentage of pseudoreplicate trees (±70%) in which the DNA barcodes clustered together in the bootstrap test (500 pseudoreplicates) are shown above the branches. Abbreviation as follows: PM = Peninsular Malaysia, VN = Vietnam, BN = Borneo (including Sabah & Sarawak of East Malaysia, Brunei and Kalimantan Indonesia), TH = Thailand, LA = Laos, SM = Sumatera Indonesia, JV = Java Indonesia, IND = India, CH = China, CM = Cambodia, MN = Myanmar. https://doi.org/10.1371/journal.pone.0179555.g005
Fig 4 in A checklist of the bats of Peninsular Malaysia and progress towards a DNA barcode reference library
Fig 4. Neighbour-joining tree showing all available DNA barcodes for species in family Hipposideridae reported from Peninsular Malaysia. The percentage of pseudoreplicate trees (±70%) in which the DNA barcodes clustered together in the bootstrap test (500 pseudoreplicates) are shown above the branches. Abbreviation as follows: PM = Peninsular Malaysia, VN = Vietnam, BN = Borneo (including Sabah & Sarawak of East Malaysia, Brunei and Kalimantan Indonesia), TH = Thailand, LA = Laos, SM = Sumatera Indonesia, CH = China, CM = Cambodia. https://doi.org/10.1371/journal.pone.0179555.g004
Fig 2 in A checklist of the bats of Peninsular Malaysia and progress towards a DNA barcode reference library
Fig 2. Neighbour-joining tree showing all available DNA barcodes for species in family Pteropodidae reported from Peninsular Malaysia. The percentage of pseudoreplicate trees (±70%) in which the DNA barcodes clustered together in the bootstrap test (500 pseudoreplicates) are shown above the branches. Abbreviation as follows: PM = Peninsular Malaysia, VN = Vietnam, JV = Java, Indonesia, BN = Borneo (including Sabah, Sarawak, Brunei and Kalimantan), TH = Thailand, LA = Laos. https://doi.org/10.1371/journal.pone.0179555.g002
Fig 3 in A checklist of the bats of Peninsular Malaysia and progress towards a DNA barcode reference library
Fig 3. Neighbour-joining tree showing all available DNA barcodes for species in families Emballonuridae, Megadermatidae, Molossidae and Nycteridae reported from Peninsular Malaysia. The percentage of pseudoreplicate trees (±70%) in which the DNA barcodes clustered together in the bootstrap test (500 pseudoreplicates) are shown above the branches. Abbreviation as follows: PM = Peninsular Malaysia, VN = Vietnam, BN = Borneo (including Sabah & Sarawak of East Malaysia, Brunei and Kalimantan Indonesia), TH = Thailand, LA = Laos, SM = Sumatera Indonesia, CH = China. https://doi.org/10.1371/journal.pone.0179555.g003
Spectral Library of Impervious Urban Materials
<p>The radiative response of impervious urban materials is a highly influential surface property, due to its impacts on the radiation balance of incoming and outgoing long- and short-wave fluxes. Information about the material composition can be determined with data resolved to the wavelength level. Spectral reflectance in the visible- to short-wave infrared (VIS-SWIR) region is widely used in remote sensing-based land cover classification and spectral long-wave infrared (LWIR) emissivity is required for the observation of surface temperatures. The Spectral Library of impervious Urban Materials (SLUM) available from the London Urban Micromet data Archive (LUMA) includes LWIR emissivity spectra of 74 samples of impervious surfaces derived using measurements made by a portable Fourier Transform InfraRed (FTIR) spectrometer and matching short-wave reflectance spectra observed for each urban sample.</p> <p>The documentation (<a href="http://www.met.reading.ac.uk/micromet/documents/LUMA_SLUM.pdf">pdf</a>) contains photos, meta information for all 74 samples and plots of the short-wave reflactance (300-2500 nm) and long-wave (8-14 um) emissivity spectra.</p> <p>Data are provided in csv format for short-wave reflectance (LUMA_SLUM_SW.csv) and long-wave emissivity (LUMA_SLUM_LW.csv).</p> <p>Further details (including integrated broadband values of emissivity and albedo), methods and data analysis are presented in</p> <p>Kotthaus, S, TEL Smith, MJ Wooster, and CSB Grimmond 2014: Derivation of an urban materials spectral library through emittance and reflectance spectroscopy, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>, 94, 194–212. doi:10.1016/j.isprsjprs.2014.05.005</p> <p><strong>Please cite both this site and the paper above if you use this data</strong></p>
Training dataset: Generation of a spectral library from HEK-Ecoli Spike-in mass spectrometry data
<p>The five raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in the following ratios (amount in µg):</p> <p>Sample HEK E.coli MS method<br> Sample1 2.5 0.00 DDA<br> Sample2 2.5 0.05 DDA<br> Sample3 2.5 0.15 DDA<br> Sample4 2.5 0.40 DDA<br> Sample5 2.5 0.80 DDA</p> <p>Additionally, iRT peptides were added and 1µg of each samples was measured with a Q-Exactive Plus mass spectrometer. Besides the five raw files, we uploaded two fasta files that serve as human and ecoli protein sequence databases, an transition list for the iRT peptides as well as an experimental design for the MaxQuant search.<br> Additionally, we uploaded the Galaxy MaxQuant training result files: protein groups, peptides, mqpar, msms, evidence and PTXQC.</p>
Datasets of Twitter mentions and publications in Information Science & Library Science and Microbiology
<p>Datasets used in the study 'Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics'.</p> <p><strong>Microbiology publications (mic_publiccations.tsv).</strong> Dataset of 101,206 Microbiology publications with their author keywords.</p> <p><strong>Microbiology mentions (mic_mentions.tsv).</strong> Dataset of 328,110 Twitter mentions to Microbiology publications.</p> <p><strong>Information Science & Library Science publications (lis_publications.tsv).</strong> Dataset of 8452 Information Science & Library Science publications with their author keywords.</p> <p><strong>Information Science & Library Science mentions (lis_mentions.tsv).</strong> Dataset of 35,411 Twitter mentions to Information Science & Library Science publications.</p>
Checkbot API raw results from Libraries, Archives and Museums websites for evaluating a data-driven Search Engine Optimization methodology
<p>Results from Checkbot API to measure and collect 341 websites compatibility on multiple SEO variables (34 variables). Checkbot API indexes the website's code to find features capable of impacting SEO performance. Each website has been tested with the maximum number of links allowed to be crawled equally to 10.000 per test. In this way, we retrieved data about the overall websites performance including their sub-pages, and not only the main domain names. A scale from 0 (lowest rate) to 100 (highest rate) was adopted for each examined variable. This constitutes a useful managerial indicator of dealing with the quantification of websites performance while avoiding complex measurement systems that are difficult to be adopted by administrators. Websites tested were also categorized by the CMS type used. More information about the variables and the meaning of the results can be found at https://www.checkbot.io/ </p>
Output of webXray analysis of German library websites
<p>[1] was analyzed with [2]. The deposited files are the result.</p> <p> </p> <p>[1] Steeg, Fabian et al.. (2016). URLs von Webseiten mit Typ Bibliothek aus Lobid.org. Zenodo. 10.5281/zenodo.50969</p> <p>[2] Tim Libert et al.. (2016). webXray: First Release. Zenodo. 10.5281/zenodo.57272</p> <p> </p>
Library of simulated root images
<p>This depository contains the images and associated RSML files used in the paper entitled "Using a structural root system model for an in-depth assessment of root image analysis pipeline" from the same authors.</p> <p> </p> <p>It contains: </p> <p>- 10.000 simulated root images;</p> <p>- 10.000 corresponding RSML files;</p> <p>- .csv files containing the ground-truth data for each modelled root system;</p> <p>- .csv files containing the image descriptors extracted using RIA-J;</p> <p>- .tps files containing the shape descriptors data.</p> <p> </p> <p>The codes used with this data is available here: https://zenodo.org/record/62063</p>
Exit poll survey data from users of Helsinki University Library
<p>Survey data from exit poll user surveys at five Helsinki University Library locations collected from 2015 to 2016. Patrons coming out the library premises were asked about their user category (student at the University of Helsinki, researcher or lecturer, student at another institution, other library user) and the reason for their library visit. Also the duration of the visit was noted and wether they needed help during their stay at the library. The file contains 3084 rows of tabular data in CSV format. The language is Finnish.</p>
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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.