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794 results for “publishing”
Bibliographic metadata of articles in "Krankenpflege" and "GERONTOLOGIE CH. Praxis + Forschung" published by Swiss university members
<p>The swissuniversities-funded project GOAL (Unlocking the Green Open Access PotentiaL in scholarly and professional journals in Switzerland) aims to develop case scenarios for the semi-automatic inclusion of full-text articles from professional journals in Open Access repositories. These articles originate from journals with which the project has successfully negotiated self-archiving rights. To this end, the project is testing semi-automated workflows that process and enrich bibliographic metadata and full-texts provided by two professional journals. The dataset contains bibliographic metadata of articles published in the two professional journals “Krankenpflege” (ISSN: 0253-0465) and “GERONTOLOGIE CH. Praxis + Forschung” by members of Swiss higher education institutions (HEI). Metadata provided by the publisher itself (Gerontologie CH.) or the database CINAHL Ultimate (“Krankenpflege”) were obtained, enriched, partly checked, and corrected to create a list of articles in tabular form as Excel and CSV of all articles published by Swiss university members between 2020 and 2023. Additionally, a table with the names of all swissuniversities members was created as a reference point to normalize the affiliation data. </p>
Metadata for "Publishing on the 'international' in the Philippines: a lexicometric inquiry" (Cruz, 2020)
<p><br>This repository contains supplementary data for keyness and topic modeling tests ran in connection with the following book chapter: </p> <p><span><span>·<span> </span></span></span><span>Cruz, F. A. (2020). Publishing on the ‘International’ in the Philippines: A Lexicometric Inquiry. In F. Cruz & N. M. Adiong (Eds.) <em>International Studies in the Philippines: Mapping New Frontiers in Theory and Practice </em>(p. 66-85). Oxon/New York: Routledge. doi: 10.4324/9780429056512. </span></p> <ul> <li>The topic selections published can be found under .csv files 12_TopicsInDocs.csv, 12_DocsInTopics.csv, 12_Topics_Words.csv and 30_TopicsInDocs.csv, 30_DocsInTopics.csv, and 30_Topics_Words.csv.</li> <li>Keyness and wordlists labeled per decade and are in .txt form. </li> <li>The file DataList.xls contains a list of journals and authors used in the overview. They appear according to the template<em> year-journalcode-surname, </em>with the following codes: <em>Philippine Political Science Journal</em> (PPSJ), <em>Kasarinlan</em> (KAS), <em>the Journal of Critical Perspectives on Asia</em> (JCPA), <em>Philippine Studies</em> (PS), and the <em>Asia Pacific Social Science Review</em> (APSSR). </li> </ul>
Two-time correlation function based on speckle patterns from x-ray photon correlation spectroscopy associated with "Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass" (scientific article published in Nature Communications, 2024)
<p>This dataset consists of contrast data, i.e., the two-time correlation function, based on speckle patterns measured at the at the 8ID-E beamline of the Advanced Photon Source at Argonne National Laboratory.</p> <p>Experimental details are stated in the paper specified under "related work" and in the accompanying supplementary information.</p> <p>You are welcome to use this dataset in compliance with the CC BY 4.0 licence assigned to this dataset.</p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data consists of 32 text files in total, which correspond to the main and lower panel Figure 2 of the main publication. </p> <p>30 of these text files are contrast data, which are named "contrast_DT250s_nn.text" wiith "nn" as the identifier of consecutive data sets going from 1 to 30. Each data set consists of p rows and q columns, DT250s denotes the time resolution of data points, which is 250 s along both row and column values.</p> <p>The data set called "Time_Contrast_1to30s.txt" states the start time in seconds of the first data point of each of the thirty contrast data set.</p> <p>The data set called "ScatteredIntensity.txt" states the scattered intensity at full time resolution, i.e. 2.5 s.</p> <p>The files are plain text files with the data points separated by "space" along rows and "new line" along columns.</p>
Published versions and illustrations of the Rubáiyát of Omar Khayyám
<p>These files cover published versions and illustrations of the Rubáiyát of Omar Khayyám. They form part of an archive of research data relating to the spread and influence of the <em>Rubáiyát</em> of Omar Khayyám. The data have been compiled by independent researchers W H (Bill) Martin and Sandra Mason; their contact details are in the README file. The files comprise a number of searchable listings relating to the poem and its different manifestations. </p> <p>This section of the archive contains two database tables covering published versions of the <em>Rubáiyát of Omar Khayyám.</em> The first of these, ROKlist2011, provides a listing of over 1600 editions or reprints of the poem, in different languages and translations, which we know either to exist or to have been identified by other compilers of <em>Rubáiyát </em>bibliographies. The second database ROKillus2011 provides more information on a subset of some 700 illustrated versions of the <em>Rubáiyát</em>. Further details of the coverage of the databases and the fields and codings used are given in the accompanying README document.</p>
Updates of listings of published versions and references relating to Rubáiyát of Omar Khayyám
<p>These files provide updates of listings of published versions of the <em>Rubáiyát </em>of Omar Khayyám and references relating to <em>Rubáiyát</em>. They form part of an archive of research data relating to the spread and influence of the <em>Rubáiyát</em> of Omar Khayyám. The data have been compiled by independent researchers W H (Bill) Martin and Sandra Mason; their contact details are in the README file. The files comprise a number of searchable listings relating to the poem and its different manifestations. </p> <p>This section of the archive contains two database tables updating earlier material to September 2018<em>.</em> The first of these, ROKlist2018, provides a listing of over 1750 editions or reprints of the poem, in different languages and translations, which we know either to exist or to have been identified by other compilers of <em>Rubáiyát </em>bibliographies. The second database ROKref2018 lists more than 500 books, articles and other material relating to the <em>Rubáiyát of Omar Khayyám</em>, which we have identified in the course of our research. Further details of sources, and of the fields and codings used in the databases, are given in the accompanying README document.</p>
Title, Author, Publisher, Place of Publication, and Language-related Network Graphs of the Berlin State Library Main Catalog
<p>The dataset contains graphs in GML, GraphML, and a simple JSON format.</p> <p>For each of the following languages:</p> <ol> <li>cze</li> <li>dan</li> <li>dut</li> <li>eng</li> <li>fre</li> <li>fry</li> <li>ger</li> <li>gre</li> <li>ice</li> <li>ita</li> <li>lat</li> <li>nor</li> <li>pol</li> <li>por</li> <li>rum</li> <li>rus</li> <li>slo</li> <li>spa</li> <li>swe</li> </ol> <p>two graphs are made available linking</p> <ul> <li>author, publisher, and place of publication</li> <li>author, publisher, place of publication, and title</li> </ul> <p>Additionaly, a third graph links authors and publishers to the language of publication (incl. year of the publication).</p> <p>The core statistics of each graph are outlined in <em>social_analysis_statistics.csv</em>. The smallest graph (fry, author_publisher_location) has 298 nodes and 264 edges, while the largest (ger, author_publisher_location_title) has 2,499,943 nodes and 3,950,900 edges.</p> <p>The language graphs spans all languages and has 1,706,273 nodes and 1,827,759 edges.</p> <p>All graphs have been created by a Python script available <a href="https://github.com/elektrobohemian/CulturalAnalytics/blob/master/SocialAnalysisStabikat.ipynb">here.</a></p>
rdemolgen/MNV-test-data: Published version for journal paper.
<p>A BAM file containing five MNVs (Multiple Nucleotide Variants) for the purposes of testing bioinformatics pipelines.</p>
RDF Linked Data representation of GC-MS data from the 'Rose Genome' article published in Nature genetics, June, 2018
<p>This dataset corresponds to the RDF Linked Data representation of the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. Most of the semantics resources belong to the <a href="http://obofoundry.org">OBO foundry</a>.</p> <p>The transformation to RDF was performed on a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holding the data extracted from a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018. This supplementary material table was deposited to Zenodo and is identified by the following doi: <a href="https://doi.org/10.5281/zenodo.2598799">https://doi.org/10.5281/zenodo.2598799</a></p> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p>
Data accompanying the manuscript "Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake", published in Limnology and Oceanography (doi: 10.1002/lno.12687)
<p>CTD and geochemical data accompanying the publication: Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake in Limnology & Oceanography (doi: 10.1002/lno.12687).</p>
Secondary Data from Insights from Publishing Open Data in Industry-Academia Collaboration
<h1>Secondary Data from Insights from Publishing Open Data in Industry-Academia Collaboration</h1> <h2>Authors</h2> <p>Per Erik Strandberg [1], Philipp Peterseil [2], Julian Karoliny [3], Johanna Kallio [4], and Johannes Peltola [4].</p> <p>[1] Westermo Network Technologies AB (Sweden).<br>[2] Johannes Kepler University Linz (Austria)<br>[3] Silicon Austria Labs GmbH (Austria).<br>[4] VTT Technical Research Centre of Finland Ltd. (Finland).</p> <h2>Description</h2> <p>This data is to accompany a paper submitted to Elsevier's data in brief in 2024, with the title <em>Insights from Publishing Open Data in Industry-Academia Collaboration</em>.</p> <p><em>Tentative Abstract:</em> Effective data management and sharing are critical success factors in industry-academia collaboration. This paper explores the motivations and lessons learned from publishing open data sets in such collaborations. Through a survey of participants in a European research project that published 13 data sets, and an analysis of metadata from almost 281 thousand datasets in Zenodo, we collected qualitative and quantitative results on motivations, achievements, research questions, licences and file types. Through inductive reasoning and statistical analysis we found that planning the data collection is essential, and that only few datasets (2.4%) had accompanying scripts for improved reuse. We also found that authors are not well aware of the importance of licences or which licence to choose. Finally, we found that data with a synthetic origin, collected with simulations and potentially mixed with real measurements, can be very meaningful, as predicted by Gartner and illustrated by many datasets collected in our research project.</p> <h2>Secondary data from Survey</h2> <p>The file <code>survey.txt</code> contains secondary data from a survey of participants that published open data sets in the 3-year European research project InSecTT.</p> <h2>Secondary data from Zenodo</h2> <p>The file <code>secondary_data_zenodo.json</code> contains secondary data from an analysis of data sets published in Zenodo. It is accompanied with a <code>py</code>-file and a <code>ipynb</code>-file to serve as examples.</p> <h2>License</h2> <p>This data is licenced with the Creative Commons Attribution 4.0 International license. You are free to use the data if you attribute the authors. Read the license text for details.</p>
CD5 index of works published in the period 1945-2018
<p>The xz-compressed data file (2.7 GiB uncompressed) contains 44,008,797 records comprising two fields separated by a "|" character:</p> <ol> <li>Publication DOI</li> <li>Five-year consolidation / disruption (CD₅) index (when available)</li> </ol> <p>The data set was derived from the <a href="https://www.crossref.org/blog/2024-public-data-file-now-available-featuring-new-experimental-formats/">Crossref 2024 public data file</a> using <a href="https://github.com/dspinellis/alexandria3k">Alexandria3k</a> and <a href="https://github.com/dspinellis/fast-cdindex">fast-cdindex</a>. More information about the process can be found in the following papers.</p> <ul> <li>Diomidis Spinellis. Open reproducible scientometric research with Alexandria3k. <em>PLoS ONE</em>, 18(11):e0294946, November 2023. <a href="https://dx.doi.org/10.1371/journal.pone.0294946">doi:10.1371/journal.pone.0294946</a></li> <li>Diomidis Spinellis. Efficient graph processing. <em>IEEE Software</em>, 42(1):22–25, January 2025. <a href="https://dx.doi.org/10.1109/ms.2024.3477013">doi:10.1109/ms.2024.3477013</a></li> </ul> <p>In common with the latest revision of the previously released 1945–2016 data set, this one incorporates into the calculation works lacking a reference list, but not calculating a CD₅ index for them. It also excludes works published after 2018, as they lack five years of citations to them.</p>
Set of images published in publication "Cleaning strategies for 3D-printed porous scaffolds used for bone regeneration fabricated via ceramic vat photopolymerization"
<p>Figures of publication "Cleaning strategies for 3D-printed porous scaffolds used for bone regeneration fabricated via ceramic vat photopolymerization".</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ceramint.2024.10.160" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ceramint.2024.10.160</span></span></a></p>
Dataset for the manuscript "Are remote sensing evapotranspiration models reliable 2 across South American ecoregions?" published in WRR
<p><strong>Metadata of ‘<em>Are remote sensing evapotranspiration models reliable across South American ecoregions?</em>’ </strong></p> <p>This document describes the file formatting and data used to run and evaluate the evapotranspiration models in this study. Because forcing data varies among models, each input file contains a different set of meteorological data placed within a folder named after the corresponding model.</p> <p> </p> <p><strong>File format and time stamps</strong></p> <p>Data files are CSV formatted with timestamps in the first column of the file. The following timestamps are used:</p> <ul> <li>GLEAM: Year (YYYY); Day of Year (DDD)</li> <li>PT-JPL: Year (YYYY); Month (MM); Day (DD)</li> <li>PM-MOD: Year (YYYY); Month (MM); Day (DD)</li> <li>PM-VI: Date (MM/DD/YYYY)</li> </ul> <p> </p> <p><strong>Missing data</strong></p> <p>Missing data are reported using ‘NaN’ as a replacement flag. Data for all days in a leap year are reported. </p> <p> </p> <p><strong>Data format</strong></p> <p>The column headers Name, Description and Units are adopted used in the data files to describe the following variables::</p> <ul> <li>ETo, Penman-Monteith FAO-56 reference evapotranspiration (mm day<sup>-1</sup>);</li> <li>ETobs, Observed evapotranspiration (mm day<sup>-1</sup>);</li> <li>Rn, Surface Net Radiation (w m<sup>-2</sup>);</li> <li>Rg, Daylight shortwave Incoming Radiation (w m<sup>-2</sup>);</li> <li>Rgs_out, Shortwave Radiation - outgoing (w m<sup>-2</sup>);</li> <li>G, Soil heat flux (w m<sup>-2</sup>);</li> <li>P, Rainfall (mm day<sup>-1</sup>);</li> <li>T, Surface Air Temperature (ºC);</li> <li>Tmax, Maximum Temperature (ºC);</li> <li>Tmin, Minimum Temperature (ºC);</li> <li>Tday, Daytime Temperature (ºC);</li> <li>TminDay, Daytime Minimum Temperature (ºC);</li> <li>TminNight, Nighttime Minimum Temperature (ºC);</li> <li>Patm, Atmospheric Air Pressure (Pa);</li> <li>ea, Actual Vapor Pressure (kPa);</li> <li>es, Saturation Vapor Pressure (kPa);</li> <li>VPD, Vapor Pressure Deficit (kPa);</li> <li>eaDay, Daytime Actual Vapor Pressure (kPa);</li> <li>eaNight, Nighttime Actual Vapor Pressure (kPa);</li> <li>RH, Air Relative Humidity;</li> <li>RHDayTime, Daytime Air Relative Humidity;</li> <li>RHNightTime, Nighttime Air Relative Humidity;</li> <li>LAI, Leaf Area Index (m² m<sup>-</sup>²);</li> <li>SWC, Soil Water Content (mm m<sup>-1</sup>).</li> </ul> <p> </p> <p><strong>Forcing data per model</strong></p> <p>Each model requires a different set of forcing data, as follows:</p> <ul> <li>GLEAM: Rn, P, T, Rgs_out;</li> <li>PT-JPL: Tmax, Rn, RH (or e<sub>a</sub>);</li> <li>PM-MOD: Rg, Tday, TminDay, TminNight, RHDayTime, RHNighttime, eaDay, eaNight;</li> <li>PM-VI: ETo.</li> </ul> <p> </p> <p><strong>Tower sites (IDs) and co-authors/PIs:</strong></p> <ul> <li>SDF: J. P. Quezada and M. Galleguillos;</li> <li>TF1 and TF2: L. Kutzbach and D. Holl;</li> <li>GRO and SLU: G. Posse;</li> <li>BAL and MCC: M. Gassman and C. Perez;</li> <li>PDG, EUC and USR: O. Cabral;</li> <li>FM and SIN: J.S. Nogueira and T. Range;</li> <li>CAA: M. Moura;</li> <li>CST: A. C. D. Antonino;</li> <li>SJO: E. S. Souza and J. R. S. Lima;</li> <li>ESEC: B. Bezerra.</li> </ul>
ESG Hound Published Findings on Starship Project Environmental Assessment
<p>Starship Project Environmental Assessment Critique and Findings. The Boca Chica launch site's environmental impact statement draft is full of errors and missing important details - ESG Hound is on the case.</p> <p> </p> <p>Latest version of dataset at <a href="https://www.esghound.com">https://www.esghound.com</a>.</p>
Simulated thickness profiles of ALD film in a wide microchannel of 500 nm height published as Fig.4 in PCCP 24 (2022) 8645-8660
<p>A series of simulated thickness profiles of atomic layer deposition (ALD) film grown in a wide lateral high-aspect-ratio (LHAR) microchannel is archived as an Excel file. This dataset has been published as Figure 4 in the publication "Conformality of atomic layer deposition in microchannels: impact of process parameters on the simulated thickness profile" (Yim and Verkama et al., Phys. Chem. Chem. Phys. 24 (2022) 8645-8660. https://doi.org/10.1039/D1CP04758B). A diffusion-reaction model by Ylilammi et al. (Ylilammi et al., J. Appl. Phys. 123 (2018) 205301. https://doi.org/10.1063/1.5028178) was re-implemented for the simulation. For this simulation, a channel height of 500 nm, which is a typical height for microscopic PillarHallTM LHAR test chips (Yim and Ylivaara et al., Phys. Chem. Chem. Phys., 22 (2020) 23107-23120. https://doi.org/10.1039/D0CP03358H), was used.<br> The Excel file consists of 11 tabs in total: metadata, baseline thickness profile, and Fig4a to Fig4i. The baseline thickness profile and data of fig4a Fig4i are also available as a CSV file. The metadata page describes the data with its baseline conditions. The baseline conditions used in the simulation are: sticking coefficient = 0.01, temperature = 250 °C, initial partial pressure of Reactant A = 100 Pa, molar mass of Reactant A = 0.100 kg mol-1, hard-sphere diameter of Reactant A = 6 × 10-10 m, partial pressure of inert gas I = 500 Pa, molar mass of inert gas I = 0.028 kg mol-1, hard-sphere diameter of inert gas I = 3.74 × 10-10 m, mass density of deposited film = 3500 kg m-3, areal number density of metal M atoms in MyZx material = 4 nm-2, number of metal atoms in a Reactant A molecule = 1, number of metal atoms in a formula unit of growing film = 1, number of cycle = 250, desorption probability = 0.01 s-1, channel height = 500 nm, and channel width = 10 mm. The baseline thickness profile tab contains a thickness profile obtained in the baseline conditions as film thickness versus distance within a microchannel. The thickness profile stored from Fig4a to Fig4i tabs was obtained by varying individual parameters with other parameter values in baseline conditions: initial partial pressure of Reactant A (Fig4a), pulse time (Fig4b), molar mass of Reactant A (Fig4c), mass density of deposited film (Fig4d), adsorption density (Fig4e), desorption probability (Fig4f), sticking coefficient (Fig4g), temperature (Fig4h) and partial pressure of inert gas (Fig4i).</p>
A mapping of keywords from published papers on alien squirrels to biological invasion research themes
<p><strong>Context</strong></p> <p>This dataset was used to produce the worldl and the graphs in the editorial to the research topic <a href="https://www.frontiersin.org/research-topics/29270/ecology-impact-and-management-of-squirrel-invasions"><em>Ecology, impact and management of squirrel invasions</em></a> (La Morgia et al. 2023).</p> <p><strong>Contents of the dataset</strong></p> <p>The dataset contains the keywords of papers since 2000 harvested with a Web of Science search (performed on 29/05/2023) using the advanced search string TS=(invasive squirrel) OR TI=(invasive squirrel) OR AB=(invasive squirrel). We screened the search results, excluding papers irrelevant to alien squirrels, for example, papers on computer science or physiology, medical or other aspects without any bearing to conservation science. To do this, we checked the abstract and keywords of the papers. Out of the 401 initial papers, after this first screening, we kept 217 in this dataset. The keywords of these papers were manually assigned to alien squirrel research topics by the authors of this dataset (using an own categorisation) and then mapped to the seven broad themes of invasive alien species research of <a href="https://doi.org/10.1007/s10530-023-03067-7">Stevenson et al. (2023)</a>: </p> <ol> <li>Ecosystems: topics which discuss a specific region, or biome, or focused on a particular species strongly associated with one ecosystem type;</li> <li>Monitoring: topics regarding all aspects of monitoring, including detection, identification, and distributional mapping;</li> <li>Management and decision-making: topics discussing the management and socio-political aspects of invasion science, such as prevention, control, and policy;</li> <li>Interactions: topics discussing the interactions with native species, or the effects of those interactions</li> <li>Assessing change: topics focused on studying and analysing temporal and ecological change;</li> <li>Traits: topics that explored the characteristics of alien squirrels;</li> <li>Invasion mechanisms: topics discussing dispersal pathways and drivers of spread.</li> </ol> <p><strong>Dataset description</strong></p> <p>Every row (N = 1275) in the comma-separated .csv represents one original keyword with reference to the paper in which that keyword appears and mapped to the research topics on invasive squirrels and the broad themes in invasion biology research. The .csv contains the following fields:</p> <ul> <li>ID: a unique ID assigned to the combination of an original keyword and the corresponding paper harvested from the WoS search</li> <li>original_keyword: the original keywords associated with the paper (WoS search)</li> <li>keyword_topic: categorization of original keywords into topics related to invasive squirrel research by La Morgia et al. (2023)</li> <li>mapped_category: mapping to one of the seven broad themes of invasive alien species research of <a href="https://doi.org/10.1007/s10530-023-03067-7">Stevenson et al. (2023)</a> as listed and described above</li> <li>authors: author(s) of the paper (WoS search)</li> <li>year: publication year of paper (WoS search)</li> <li>title: title of the paper (WoS search)</li> <li>journal: full journal name (WoS search)</li> <li>doi: full doi of the paper (WoS search)</li> </ul> <p><strong>Potential applications of the dataset</strong></p> <p>This dataset can be used to reproduce the graphs in La Morgia et al. (2023) or to perform more in-depth review or analysis of the literature on alien squirrel invasions. For more information and graph code, we refer to <a href="https://github.com/Vale-LaMo/squirrels">this GitHub repository</a>.</p>
List of articles resulting from the Google Scholar search "graph based author name disambiguation" published after 1/1/2021
<p>This dataset contains the list of articles resulting from the Google Scholar search “graph based author name disambiguation” published after 1/1/2021. The list is provided for reproducibility of the survey article “Graph-based Methods for Author Name Disambiguation: A Survey” and it was obtained using the following Python script available at <a href="https://github.com/WittmannF/sort-google-scholar">https://github.com/WittmannF/sort-google-scholar</a>:</p> <blockquote> <p>$ python sortgs.py --kw “graph based author name disambiguation” --startyear 2021</p> </blockquote> <p>The command returned the CSV file that contains the first 94 publications matching the query (articles with corrupted metadata have been excluded), each with metadata about Title, Number of Citations, and Rank. The CSV contains a column that specified which articles have been eventually selected for the survey.</p>
Difference and number of works published over the years grouped by the objective of the generative process
<p>Difference and number of works published over the years grouped by the objective of the generative process. Part of the study "What do we mean by GenAI?"</p>
Number of works published over the last five years
<p>Number of works published over the last five years. Part of the study "What do we mean by GenAI?"</p>
Number of works published over the years grouped by generated content type
<p>Number of works published over the years grouped by generated content type. Part of the study "What do we mean by GenAI?"</p>
ScienceDex guides
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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.