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1,079 results for “source data”
X-Ray Diffraction data from Membrane transport protein AcrB, V612F mutant with bound minocycline, source of 9FHC structure
<p>Crystals were grown of the membrane transport protein AcrB, V612F mutant, with bound minocycline. </p> <p>X-ray diffraction data of this upload: 400 frames of 0.5° width were collected on 2007-04-30 at the X06SA beamline of Swiss Light Source at Paul-Scherrer-Institute (Switzerland).</p> <p>The data can be processed with XDS; XDS.INP is provided as part of the upload.</p> <p>The data are the basis of the PDB 9FHC structure.</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part IV: Post-processing)
<p>This is Part IV of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the post-processing of all model results.</p> <p>To be able to run the scripts as is, the folder structure should be as follows:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post/Basic/Channels<br>Post/Basic/Cross_sections<br>Post/Basic/Integrals<br>Post/Basic/Median_neighborhood_analysis (includes all unzipped MNA_TIGER_XX.zip folders)<br>Post/Basic/Skeleton_clean<br>Post/Basic/Skeleton_final<br>Post/Basic/Skeleton_raw<br>Post/Basic/Unchanneled_path_length<br>Post/Basic/Watersheds<br>Post/Basic/Scenarios.txt<br>Post/Basic/TIGER_2km_5m.slf<br>Post/Paper_1/Erosion-deposition<br>Post/Paper_1/Fluxes<br>Post/Paper_1/Profiles<br>Post/Paper_1/Std</p>
Example data set from Diamond Light Source VMXi beamline (Eiger 4M data, NeXus format)
<p>Data set recorded from Thermolysin crystal record <em>in situ</em> with Eiger 4M detector, to demonstrate file format used for this instrument at Diamond Light Source. Processing results using xia2 / DIALS:</p> <p> </p> <pre>For AUTOMATIC/DEFAULT/SAD Overall Low High High resolution limit 1.97 5.35 1.97 Low resolution limit 46.86 46.87 2.01 Completeness 59.8 73.2 6.0 Multiplicity 5.8 8.0 1.1 I/sigma 13.6 24.4 1.8 Rmerge(I) 0.072 0.050 0.308 Rmerge(I+/-) 0.067 0.048 0.000 Rmeas(I) 0.078 0.054 0.436 Rmeas(I+/-) 0.076 0.054 0.000 Rpim(I) 0.028 0.018 0.308 Rpim(I+/-) 0.035 0.023 0.000 CC half 0.997 0.998 0.450 Wilson B factor 13.401 Anomalous completeness 51.3 78.8 0.8 Anomalous multiplicity 3.3 4.8 1.0 Anomalous correlation 0.039 -0.006 0.000 Anomalous slope 0.987 dF/F 0.103 dI/s(dI) 1.086 Total observations 85069 8229 82 Total unique 14747 1031 73 Assuming spacegroup: P 6 2 2 Other likely alternatives are: P 61 2 2 P 65 2 2 P 62 2 2 P 64 2 2 P 63 2 2 Unit cell (with estimated std devs): 93.7184(3) 93.7184(3) 130.864(2) 90.0 90.0 120.0 </pre> <p> </p>
GRTSmh_diffres: the raster data source GRTSmaster_habitats converted to 9 hierarchical cell address levels at the corresponding lower resolution
<p>The <code>GRTSmh_diffres</code> data source file is a file collection, composed of nine monolayered GeoTIFF files of the <code>INT4S</code> datatype plus a GeoPackage with six polygon layers:</p> <ul> <li> <p>The polygon layers in the GeoPackage are the dissolved, polygonized versions of levels 4 to 9 of the <code>GRTSmh_brick</code> data source (<a href="https://doi.org/10.5281/zenodo.3354403">link</a>). This means that they provide the decimal (i.e. base 10) integer values of these <em>higher hierarchical levels</em> of the GRTS cell addresses of the raw data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Hence, the polygons are typically squares that correspond to the GRTS cell at the specified hierarchical level. The polygon layer is however restricted to the non-<code>NA</code> cells of the original <code>GRTSmaster_habitats</code> raster. Consequently, a part of the polygons is clipped along the Flemish border. Levels 1 to 3 are not provided for the whole of Flanders, because this would inflate the GPKG file. You can look at the <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf5">source code</a> to do such things.</p> </li> <li> <p>The GeoTIFF files provide the respective levels 1 to 9 of the <code>GRTSmh_brick</code> data source in a raster format, at the resolution that corresponds to the GRTS cell at the specified hierarchical level. The presence of <code>NA</code> cells around Flanders at level 0 implies that, with decreasing resolution, the raster's extent increases and larger areas outside Flanders are covered by non-<code>NA</code> cells along the border.</p> </li> </ul> <p>The higher-level ranking numbers (compared to the original level 0) allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes.</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_30_GRTSmh_diffres">'n2khab-preprocessing' at commit ecadaf5</a> for the creation from the <code>GRTSmh_brick</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>Beware that not all GRTS ranking numbers at the specified level are provided, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>
GRTSmh_brick: the raster data source GRTSmaster_habitats converted to 10 hierarchical cell address levels at the original resolution
<p>The data source file is a 10-layered GeoTIFF file, derived from the raster data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Both GeoTIFFs (<code>GRTSmaster_habitats</code>, <code>GRTSmh_brick</code>) use the <code>INT4S</code> datatype. The <code>GRTSmh_brick</code> data source (resolution 32 m) holds the decimal integer ranking numbers of 10 hierarchical levels of the GRTS cell addresses, including the one from <code>GRTSmaster_habitats</code> (with GRTS cell addresses at the resolution level).</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_20_GRTSmh_brick">'n2khab-preprocessing' at commit ecadaf5</a> for its creation from the <code>GRTSmaster_habitats</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>The higher-level ranking numbers of the RasterBrick allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes. The provided hierarchical levels correspond to the resolutions vector <code>32 * 2^(0:9)</code> (minimum: 32 meters, maximum: 16384 meters).</p> <p>Beware that not all GRTS ranking numbers are present in the data source, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>
Citations to software and data in Zenodo via open sources
<p>In January 2019, the Asclepias Broker harvested citation links to Zenodo objects from three discovery systems: the NASA Astrophysics Datasystem (ADS), Crossref Event Data and Europe PMC. Each row of our dataset represents one unique link between a citing publication and a Zenodo DOI. Both endpoints are described by basic metadata. The second dataset contains usage metrics for every cited Zenodo DOI of our data sample. </p> <p> </p>
Source data for "Synthetic gauge fields for phonon transport in a nano-optomechanical system"
<ul> <li>Experimental raw data for density plots in Fig 2. Each .csv contains an array, where 1st row corresponds to x_axis (mechanical frequency in MHz for panels 1,2,3,4) and first column the y_axis (optical frequency in THz for panel 1, modulation frequency in MHz for panels 2,3,4). First nonzero component is the 2nd for each array. Remaining array elements contain the z values (Thermomechanical noise spectral for panel 1, Amplitude of driven responses for panels 2,3,4). An illustrative example of plotting in an ipython notebook follows:</li> </ul> <p> %pylab inline</p> <p> A= genfromtxt('Fig2_data_modVolt=0mV_experiment.csv', delimiter=',') </p> <p> x = A[0,1:]<br> y = A[1:,0]<br> z = A[1:,1:]<br> imshow(z,aspect='auto',vmin=z.min(),vmax=z.max(),extent=[x.min(),x.max(),y.min(),y.max()],cmap='magma') </p> <ul> <li> Theoretical data for panel 4 in Fig 2, stored in a .csv with the same structure as previous.</li> <li> Raw experimental data for upper panels in Fig 3. Each .csv contains an array where 1st row corresponds to x_axis (modulation phase) and first column the y_axis (optical frequency in THz). Z values contain the experimental signal proportional to the Y optical quadrature of the transferred mode.</li> <li>Theoretical data for lower panels in Fig 3, stored in a .csv with the same structure as previous.</li> <li>Jupyter notebook to produce and plot typical data for Fig 4: phononic amplitude averaged over 100 disorder realizations, normalized to the maximum value (*extra_dependencies: Kwant Python library: <a href="https://kwant-project.org/">https://kwant-project.org/</a>).</li> </ul>
Source Data and Scripts - MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy
<p>Experimental and simulated STED data and scripts associated with Naas et al. "<em>MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy.</em>" <em>bioRxiv</em> (2024): 2024-02. </p> <p>The MultiMatch Python package and further illustrative examples are available on GitHub repository <a href="https://github.com/gnies/multi_match">https://github.com/gnies/multi_match</a>.</p>
Integration of data sets from different sources for modeling gender violence and perception of insecurity
<p>The dataset is composed of three distinct files which aggregate processed data derived from open datasets of three cities: Dublin, San Francisco, and Valencia. The data has been mapped to a grid of 25m² for Valencia and 50m² for Dublin and San Francisco. The respective files are named DATA_ES_VLC.csv, DATA_IE_DUB.csv, and DATA_US_SFO.csv. Additionally, there is a dataset for tweets named DATA_TWT.csv, which contains tweets collected through web scraping and analysed using natural language processing (NLP) algorithms and neural networks. The aim is to identify and classify tweets that discuss gender-based violence in the city of Valencia. Another file, MAP_ES_VLC.csv, includes points collected during various mapathons conducted by the Polytechnic University of Valencia campus for a science project aimed at identifying potentially insecure locations.</p>
Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)
<p>This repository concerns the supplementary material data of the research article entitled "<em>Personalised in silico model for radiation-induced pulmonary fibrosis</em>" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.</p>
Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset
<p>This dataset accompanies the report <em>"Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values"</em>, which is available via Zenodo.<br><br>It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record.</p> <p>The code used to generate the dataset from open data sources is available on GitHub. </p> <p>The following data sources were used:</p> <ul> <li> <p>Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13)</p> </li> <li> <p>Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23)</p> </li> <li> <p>OpenAlex (data snapshot 2023-10-18)</p> </li> <li> <p>Unpaywall (data snapshot 2023-11-27)</p> </li> <li> <p>IRUS UK (2024-04-03)</p> </li> <li> <p>Crossref Event Data (2023-04-01)</p> </li> </ul> <p><strong></strong><br><br>The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>. </p>
Source Data for Manuscript: Identifying genomic data use with the Data Citation Explorer
<p>This page contains the source data for the manuscript describing the Data Citation Explorer, currently in review for publication. The preprint version can be found on this page.</p> <p>Files:</p> <p><strong>DCE_manual_eval_sample.xlsx:</strong></p> <p>This file was used to manually evaluate hits generated by the Data Citation Explorer. There are two separate sheets: one with publications returned by searches in PubMed and PubMed Central and another with publications returned by searches in Dimensions. Column descriptions can be found in the file itself. Each row in each evaluation sheet refers to a pair between a JAMO record and a linked publication.</p> <p><strong>DCE_citation_report.csv</strong></p> <p>Contains JAMO record IDs and PubMed IDs from the initial 2020 DCE trial run. There are 238,994 unique JAMO IDs and 30,641 unique PubMed IDs. 78,104 JAMO records are linked with publications.</p> <p>Columns:</p> <ul> <li>jamo_id - unique JAMO record ID</li> <li>sample_group - Sample strata from which manually evaluated records were pulled</li> <li>citation_count - Number of citations associated with each record</li> <li>citations - comma-delimited PubMed IDs for linked publications</li> <li>sampled - True/False, denoting which records were included in the initial evaluation sample</li> <li>notes - descriptions for why certain sampled records were excluded from manual evaluation</li> <li>unprocessed - True/False. These 7,890 records contained anomalous fields that caused them to be rejected for processing. They are represented as zero-length files in the archive.</li> </ul> <p><strong>DCE_source_files.zip:</strong></p> <p>This folder contains 3 files for each JAMO record in DCE_citation_report.tsv. For each JAMO record listed in the citation report, three files are provided:</p> <ol> <li>JAMO_ID_source.yaml - The fields extracted from the JAMO record that were relevant to the citation search, including any previously known PMIDs (manually curated).</li> <li>JAMO_ID_expand.yaml - The source record augmented with additional metadata discovered in other resources, including the citations that were discovered based on querying PubMed Central for the values in those metadata fields.</li> <li>JAMO_ID_audit.json - The audit path as a directed acyclic graph, in JSON.</li> </ol>
Cows, Pigs and People: Example data of cubic insulin from three different species recorded on Diamond Light Source I24
<p>Data collected at 100K on 10th May 2024 at I24 (Diamond Light Source) to investigate automatic grouping of datasets containing very subtle differences. Crystals grown by Cicely Tam following standard techniques with coordination from Felicity Bertram. For each of bovine, porcine, and human insulin, 10 degree wedges are included. Insulin from these three sources differ by 1-3 amino acids, but are otherwise structurally isomorphous. </p> <p>The purpose of the data upload is to make data available for tutorials using the DIALS toolchain (see e.g. examples at https://github.com/graeme-winter/dials_tutorials) however data are available for all purposes without limitation. </p> <p>Key:</p> <p>CIX - bovine insulin</p> <p>PIX - porcine insulin</p> <p>X - human insulin</p>
RDF version of the data from Choi, JS. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources (2018)
<p>This is an RDFied version of the dataset published in Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1038/s41598-018-24483-z">https://doi.org/10.1038/s41598-018-24483-z</a></p> <p>The Original publication authors: Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun</p>
Source data for Synthetic dynamic hydrogels promote degradation-independent in vitro organogenesis
<p>Source data and statistical analysis results for Synthetic dynamic hydrogels promote degradation-independent in vitro organogenesis</p>
Advanced open source data formats for geometrically and physically coupled systems - examples
<p>Some model files to describe a geometrically and physically coupled PDE and a ODE system, arising from discretization in space. The files correspond to:</p> <ul> <li>a simplified two component problem with coupling and an FMU in <strong>withFMU.json</strong></li> <li>the corresponding ODE in <strong>io_withFMU_FECoupled.json</strong></li> <li>a PDE model of a complete machine in <strong>ictimt_coupledModel.zip</strong>. This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> <li>the corresponding discrete model in <strong>ictimt_feCoupled.zip </strong>This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> </ul> <p> </p> <p> </p>
data set, source of https://doi.org/10.1016/j.foodcont.2021.108062
<p>data source of "Pierrine Didier, Christophe Nguyen-The, Lydia Martens, Mike Foden, Loredana Dumitrascu, Augustin Octavian Mihalache, Anca Ioana Nicolau, Silje Elisabeth Skuland, Monica Truninger, Luís Junqueira, Isabelle Maitre,<br> Washing hands and risk of cross-contamination during chicken preparation among domestic practitioners in five European countries,<br> Food Control, Volume 127, 2021, 108062, ISSN 0956-7135, https://doi.org/10.1016/j.foodcont.2021.108062." </p> <p>SafeConsumeQuali_ParticipantsDescription.csv includes the description of participants to the SafeConsume qualitative survey.</p> <p>SafeconsumeQuali_WashingHandsDuration.csv includes the duration of washning hands in the SafeConsume qualitative survey.</p> <p>SafeConsumeQuali_WashHands.csv includes the different occasions of washning hands in the SafeConsume qualitative survey.</p> <p>SafeConsumeSurvey_extract_ATTR20200527.csv includes the list of variables of the SafeConsume quantitative survey used in the article .</p> <p>SafeConsumeSurvey_extract_DATA20200527.csv includes the data of the SafeConsume quantitative survey used in the article</p>
Dataset linking to the publication "An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020"
<p>This dataset links to the study “An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020”. This study is published in the journal “Environmental Research Letters” which can be found at <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b">https://iopscience.iop.org/article/10.1088/1748-9326/abd81b</a>. The dataset contains two files, one csv file, and one shape file. The two files contain the same data to meet the different users' needs. The dataset contains variables for assessing national forest monitoring data sources i.e., RS and/or NFI. Separate indicators namely 'Use of RS', and 'Use of NFI' were used to analyze the two data sources (RS and NFI). The description of each variable for these two indicators contained in the dataset is given in the Table below.</p> <table> <caption><strong>The description of the variables in the datase</strong>t <strong>for country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Variables Name</strong></td> <td><strong>Description of the variables</strong></td> </tr> <tr> <td>Country</td> <td>Country</td> </tr> <tr> <td>ISO_A3_CODE</td> <td>ISO A3 Code for country</td> </tr> <tr> <td>ADM0_CODE</td> <td>ADMO Code for country</td> </tr> <tr> <td>CONTINENT</td> <td>Continent</td> </tr> <tr> <td>Region</td> <td>Region</td> </tr> <tr> <td>RSInd_05</td> <td>Use of remote sensing (RS) for forest area (change) monitoring 2005 Indicator</td> </tr> <tr> <td>RSSc_05</td> <td>Use of RS for forest area (change) monitoring 2005 Score</td> </tr> <tr> <td>RSInd _10</td> <td>Use of RS for forest area (change) monitoring 2010 Indicator</td> </tr> <tr> <td>RSSc _10</td> <td>Use of RS for forest area (change) monitoring 2010 Score</td> </tr> <tr> <td>RSInd_15</td> <td>Use of RS for forest area (change) monitoring 2015 Indicator</td> </tr> <tr> <td>RSSc _15</td> <td>Use of RS for forest area (change) monitoring 2015 Score</td> </tr> <tr> <td>RSInd_20</td> <td>Use of RS for forest area (change) monitoring 2020 Indicator</td> </tr> <tr> <td>RSSc _20</td> <td>Use of RS for forest area (change) monitoring 2020 Score</td> </tr> <tr> <td>DRS05_20</td> <td>Difference ‘use of RS’ 2005-2020</td> </tr> <tr> <td>NFIInd_05</td> <td>Use of national forest inventories (NFI) for forest monitoring 2005 Indicator</td> </tr> <tr> <td>NFISc_05</td> <td>Use of NFI for forest monitoring 2005 Score</td> </tr> <tr> <td>NFIInd _10</td> <td>Use of NFI for forest monitoring 2010 Indicator</td> </tr> <tr> <td>NFISc _10</td> <td>Use of NFI for forest monitoring 2010 Score</td> </tr> <tr> <td>NFIInd_15</td> <td>Use of NFI for forest monitoring 2015 Indicator</td> </tr> <tr> <td>NFISc _15</td> <td>Use of NFI for forest monitoring 2015 Score</td> </tr> <tr> <td>NFIInd_20</td> <td>Use of NFI for forest monitoring 2020 Indicator</td> </tr> <tr> <td>NFISc _20</td> <td>Use of NFI for forest monitoring 2020 Score</td> </tr> <tr> <td>DNFI05_20</td> <td>Difference ‘Use of NFI’ 2005-2020</td> </tr> </tbody> </table> <p>Indicators and Scores in the above Table for showing the use of RS and NFI data for forest monitoring in Figure 1 (1a, 1b, and 2a, 2b) are related in the following way.</p> <table> <caption><strong>The indicator values and scores of the country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Indicator</strong></td> <td><strong>Score</strong></td> </tr> <tr> <td>Low</td> <td>0</td> </tr> <tr> <td>Limited</td> <td>1</td> </tr> <tr> <td>Intermediate</td> <td>2</td> </tr> <tr> <td>Good</td> <td>3</td> </tr> <tr> <td>Very Good</td> <td>4</td> </tr> </tbody> </table> <p>The capacity changes from 2005 to 2020 in Figure 1 (1c & 2c) are related in the following way.</p> <table> <caption><strong>The indicator values and levels for country capacity changes</strong></caption> <tbody> <tr> <td><strong>Capacity change values</strong></td> <td><strong>Capacity change levels</strong></td> </tr> <tr> <td>1,2,3,4</td> <td>Increase</td> </tr> <tr> <td>0</td> <td>No change</td> </tr> <tr> <td>-1,-2,-3,-4</td> <td>Decrease</td> </tr> </tbody> </table> <p> </p>
Accompanying data for the open-source book Modeling of Hydrological Systems in Semi-Arid Central Asia
<p>This data set is used to reproduce examples in the open-source book <a href="https://hydrosolutions.github.io/caham_book/">"Modeling of Hydrological Systems in Semi-Arid Central Asia"</a> which is part of a free course on hydrological modeling in Central Asia. The course teaches how to use publicly available data to implement a hydrological model for climate impact studies (Marti et al., 2023). </p> <p>To use the data set to reproduce the examples in the book: Download the book from https://doi.org/10.5281/zenodo.6350042 and this data set to the same hierarchical level in your file system: </p> <p>|- caham_book<br> |- caham_data<br> |- AmuDarya<br> |- central_asia_domain<br> |- student_case_study_basins<br> |- SyrDarya</p> <p>You will need a working installation of R (https://www.r-project.org/) and a GUI (e.g. Posit, formerly RStudio https://posit.co/) to reproduce the scripted examples in the book. Once your software is set up, you can proceed to run the examples. </p> <p> </p>
Data on the actual use of open data/ open source on pre-prints at arXiv/bioRxiv
<p>Articles submitted (1st edition) to the preprint server arXiv/bioRxiv were converted to text and analysed as follows :</p> <ul> <li>For arXiv articles, nationality was assigned to the manuscript using the first occurrence of the email address in the manuscript.</li> <li>For bioRxiv articles, we assigned nationality using the country tag information in the metadata about the first author.</li> <li>We listed the URLs that appeared in each manuscript.</li> <li>We checked how many articles contained a particular URL (e.g. github; https://github.com ) by year, month and nationality.</li> </ul> <p>This dataset describes the results of the above work.</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.