Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
49
datasets available to search
ShareScore release 0.9.0
Dataset results
49 results for “File formats”
IPBES Data Management Tutorials - Session 3.4: Data management report details: File formats
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session, <em>Data management report details: File formats</em>, focuses on specific recommended file formats for text, tabular data, images, sound, and geospatial data. </p>
Genomes plasmids MDR B. fragilis ONT sequence read files in fastq format
<p>Supporting data for the manuscript <em>Complete genome assembly of clinical multidrug resistant Bacteroides fragilis isolates enables comprehensive identification of antimicrobial resistance genes and plasmids.</em></p> <p>Oxford Nanopore reads demultiplexed with <a href="https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwjNjqL5tY7iAhUawMQBHZHfDasQFjAAegQIAhAB&url=https%3A%2F%2Fgithub.com%2Frrwick%2FDeepbinner&usg=AOvVaw0wikvIUagLuFV38CwKZtia">Deepbinner</a> v0.2.0 and base-called (with demultiplexing) using Albacore v2.3.3. Barcodes and adapters were removed with <a href="https://github.com/rrwick/Porechop">Porechop</a> v0.2.4 with the --discard_middle option.</p> <p>Data from each isolate was produced from two runs per isolate. Data for the individual runs are included here. They can easily be concatenated eg with cat. Runs are named TVS_01,. TVS_02, TVS_03 and TVS_04.</p> <p>Fast5 (only demultiplexed with deepbinner and basecalled with albacore) as well as illumina reads and genome assemblies can be found via the NCBI bioproject accessions:</p> <p>Isolates, NCBI bioproject accession no:</p> <p>CCUG4856T, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA525024">PRJNA525024</a></p> <p>BFO17, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244943">PRJNA244943</a></p> <p>BFO18, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244944">PRJNA244944</a></p> <p>S01, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244942">PRJNA244942</a></p> <p>BFO42, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA253771">PRJNA253771</a></p> <p>BFO67, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA254401">PRJNA254401</a></p> <p>BFO85, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA254455">PRJNA254455</a></p> <p> </p> <p><strong>md5sum's (also found in the file md5.md5):</strong></p> <p>135d0570a1e49e25c8fde59f321cca68 BFO17_TVS_03_99377.barcode02_trimmed.fastq.gz<br> 3c6ca800a1f735937c0cffccd263fb82 BFO18_TVS_01_97673.barcode03_trimmed.fastq.gz<br> dc37823950f529d3a859b7a7c514af8c BFO18_TVS_03_99377.barcode03_trimmed.fastq.gz<br> abd707404f9ebbc38652e45ed378ca21 BFO42_TVS_02.barcode10_trimmed.fastq.gz<br> 9761e9ab082e276624c5778dcbeaffd2 BFO42_TVS_04.barcode10_trimmed.fastq.gz<br> ffc27009c0f7fead1af84ce045dbe5f3 BFO67_TVS_02.barcode09_trimmed.fastq.gz<br> 06c363a7feeeb88f9d195b3769b37f2b BFO67_TVS_04.barcode09_trimmed.fastq.gz<br> 5553c95cc98f4b9d4cfb38c4f8f8f037 BFO85_TVS_02.barcode08_trimmed.fastq.gz<br> b1a8013cba7a079cee6d3bdd6cd97ff2 BFO85_TVS_04.barcode08_trimmed.fastq.gz<br> 8169225219a5fb20935d5f0304aa80c5 CCUG4856T_TVS_01_97673.barcode01_trimmed.fastq.gz<br> a509b0ae912a798a91e677795198c1c6 CCUG5846T_TVS_03_99377.barcode01_trimmed.fastq.gz<br> 8a9d2eb8b626a6e87ed267d31aa220e3 S01_TVS_01_97673.barcode04_trimmed.fastq.gz<br> 69e239a17becc25a4f103dfc3bf5886a S01_TVS_03_99377.barcode04_trimmed.fastq.gz</p>
Dataset to "Persistent Identifiers for File Formats: enabling preservation and re-use of research data"
<p>This fileset includes a "preprint" and the main dataset <em>fileformatRecognizer</em> (as .xlsx and .csv) to the paper "Persistent identifiers for file formats: enabling preservation and re-use of research data" submitted to iPRES 2019, but subsequently rejected after peer review. For the sake of transparency, permission to make available here the anonymous reviews motivating the rejection (<em>ReviewsPIDs4fileFormats.odt</em>) was asked, but was left without response. Some images (screendumps) and text result files from file identification tools tested are included. Further, a simple xquery command file (BaseX) for <em>fetch:content-type</em>()<em>, </em>used for getting MIME-types for files, is also provided.</p>
68 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file
<p>Data provided by Meitian Wang at PSI and master file revised May 2020 for full NXmx compliance.</p> <p>To create a new NeXus master file, assuming DIALS is installed in the folder $DIALS, use this command:</p> <p>libtbx.python $DIALS/modules/cctbx_project/xfel/swissfel/jf16m_cxigeom2nexus.py unassembled_file=lyso009a_0087.JF07T32V01.h5 geom_file=16M_bernina_backview_optimized_adu_quads.geom wavelength=1.368479 detector_distance=97.830 mask_file=lyso009a_0087.JF07T32V01.mask.h5 nexus_details.start_time=2018-01-00T00:00:00.000 nexus_details.end_time=2018-01-00T00:00:02.720Z nexus_details.end_time_estimated=2018-01-00T00:00:02.720Z nexus_details.sample_name=Lysozyme nexus_details.total_flux=1000000000000</p> <p>Some notes about the parameters:<br> - Geometry file is in CrystFEL format but has been realigned to group the modules hierarchically into quadrants.<br> - Wavelength is a single wavelength for the whole dataset, but options exist to do 1 wavelength per image, or a whole spectrum per image.<br> - Start and end times are example timestamps for illustration. End times are estimated for 68 frames using a 25 Hz recording rate.<br> - Total flux of 1e12 photons is an estimate.</p> <p>View the data using DIALS: dials.image_viewer lyso009a_0087.JF07T32V01_master.h5</p> <p>Process the data using DIALS, treating the images as stills, assuming 64 cores available on the system:<br> dials.stills_process mp.nproc=64 lyso009a_0087.JF07T32V01_master.h5 dispersion.gain=10 known_symmetry.space_group=P43212 known_symmetry.unit_cell=77,77,37,90,90,90 refinement_protocol.d_min_start=2.5</p> <p>Download DIALS at dials.github.io.</p>
Supplementary files for "Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes"
<p>Databases containing DFT optimized structures used for the paper: ''Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes". For further details of the computational setup we refer to this paper.</p> <p>Each database contains structures for one carbon substrate. The structures can be retrieved using the Atomic Simulation Environment (ASE).</p>
4 image lysozyme dataset recorded on the Jungfrau 16M detector at SwissFEL and formatted as a NeXus file
<p>This is a 4 image lysozyme datasets derived from https://doi.org/10.5281/zenodo.3352357. The specific 4 images are able to be processed by the software package DIALS using commands in the linked dataset above. The images were rounded to integer and compressed to save file space using this script:</p> <pre><code class="language-python">import shutil, h5py import numpy as np shutil.copyfile('../lyso009a_0087.JF07T32V01_master.h5', 'lyso009a_0087.JF07T32V01_master_4img.h5') h5 = h5py.File('lyso009a_0087.JF07T32V01_master_4img.h5', 'r+') data = h5["entry/data/data"][()] del h5["entry/data/data"] h = h5["entry/data"] subset = data[5:9].astype(np.int32) h.create_dataset("data", subset.shape, subset.dtype, subset, compression="gzip", compression_opts=9) h5.close()</code></pre> <p>The .expt file was created by dials.import and is useful for regression testing in DIALS.</p>
Koios benchmarks' netlist files (BLIF format)
<p>Koios is a benchmark suite for FPGA architecture and CAD exploration. It contains circuits from the Deep Learning domain.</p> <p>Here we are uploading the netlist files for these designs obtained by synthesizing the Verilog designs using Intel Quartus for Intel Stratix IV architecture. </p> <p>For more details see: </p> <p>https://docs.verilogtorouting.org/en/latest/vtr/benchmarks/#koios-benchmarks</p> <p>https://github.com/verilog-to-routing/vtr-verilog-to-routing/tree/master/vtr_flow/benchmarks/verilog/koios</p>
Conversion of measurements of tree ring gains: Canada, Africa, Mexico, South America from RWL- files to JSON format.
<p>The International Tree Rings Data Bank (ITRDB) is the most comprehensive tree growth database (https://www1.ncdc.noaa.gov/pub/data/paleo/treering).</p> <p>Shoudong Zhao, et al. (2019, 2018) analyzes the representativity of dendrochronological data (ITRDB) and proposes a corrected database with error indications. One of the bottlenecks of data use (ITRDB) is that the data is loaded as a collection of separate files in the Tucson positional format.</p> <p>The purpose of our data presentation is to change the Tucson data format to JSON format and combine the separate files into one.</p> <p>We convert the initial data for the <strong>Canada</strong>, <strong>Africa</strong>, <strong>Mexico</strong> and <strong>Southamerica</strong> rwl-files into Json format of data on tree growth in four files: <strong>canada.json</strong>, <strong>africa.json</strong>, <strong>mexico.json</strong> and <strong>southamerica.json</strong>. The data was converted using the R programming language and the dplR program library Bunn, A. (2008)</p> <p>The experience of developing the structure of dendroclimatic data in JSON format is described in the works of Kachaev A. (2016, 2017, 2020).</p> <p>Description of the structure of JSON data format is attached in the files ReadMe.pdf</p> <p> </p> <p>References</p> <p>Bunn, A. G. (2008). A dendrochronology program library in R (dplR). Dendrochronologia, 26, 115-124. https://doi.org/10.1016/j.dendro.2008.01.002</p> <p>Kachaev, Alexander (2020), "Compact dataset of dendrochronological data of pri-mary metric characteristics of tree rings of Asia.", Mendeley Data, V1, doi: 10.17632 / p9zhpmzgtk.1</p> <p>Kachaev A. V. (2017) Model for describing the structure of dendroclimatic data In the collection: Regional problems of remote sensing of the Earth Materials of the IV international scientific conference. Siberian Federal University, Institute of Space and Information Technologies. p. 120-122. (Russia)</p> <p>Kachaev A. V. (2016) NOSQL Approach for Development of Dendroclimatic Data Bank. In the collection: Regional problems of remote sensing of the Earth. Materials of the III International Scientific Conference. p. 89-91. (Russia)</p> <p>Shoudong Zhao, et al. (2019). The International Tree-Ring Data Bank (ITRDB) revisited: Data availability and global ecological representativity. Journal of Biogeography, 46 (2), 355-368. doi: 10.1111 / jbi.13488</p> <p>Zhao, Shoudong et al. (2018), Data from: The International Tree-Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity, Dryad, Dataset, https://doi.org/10.5061/dryad.kh0qh06</p>
Raw data used for COI delineation of the Eupolybothrus species: Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
<p>Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar</p>
The NGDEEP NIRIS calibration files for 'The Next Generation Deep Extragalactic Exploratory Public Near-Infrared Slitless Survey Epoch 1 (NGDEEP-NISS1): Extra-Galactic Star-formation and Active Galactic Nuclei at 0.5 < z < 3.6
<p>GRISMCONF configurations files used in Pirzkal et al. 2024. These contain the full field calibrated solution for the dispersion solution, trace as well as wavelength calibration. They provide a mean to extract NIRISS WFSS spectra obtained using the F115W, F150W, or F200W to within an acccuracy better than 0.25 pixel over most of the field of view. Wavelength calibration of both grism was verified to be accurate to within 15A over most of the field of view. Details can be found in Appendix A of Pirzkal et al. 2024.</p>
Dataset related to aticle "Additive Fabrication of a Vascular 3D Phantom for Stereotactic Radiosurgery of Arteriovenous Malformations"The database contains 3D models in STL file format of a patient-specific brain arteriovenous malformation phantom reconstructed from computed tomography scans.
<p><em>The database contains 3D models in STL file format of a patient-specific brain arteriovenous malformation phantom reconstructed from computed tomography scans.</em></p>
Files and dataset for Jenkins & Keppens 2022: 3D prominence formation and evolution
<p>The MPI-AMRVAC files necessary to run the presented simulation from the start, including calculations necessary to output additional variables to the .dat file. Current setup is to save all additional variables - this is not recommended as it will exceed 60gb for a single file. It is recommended to only save one or two additional variables each time. Final snapshot for t = 668 is included - this is the snapshot presented and analysed within the manuscript. Analysis completed using yt-project package in Python; EUV and Hydrogen H-α visualisation routines are integrated into a modified frontend for this package but are immediately available on request: jack.jenkins@esa.int .</p>
Turbulence pattern files used for star cluster formation in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** these files are automatically downloaded by Phantom on running the code **</p> <p>The files here are sample cubes containing turbulent driving patterns for the velocity field (vx, vy and vz) used to initiate star cluster formation simulations in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code</p> <p>These can be used to set up initial conditions for a set of simulations similar to those shown in <a href="http://adsabs.harvard.edu/abs/2003MNRAS.339..577B">Bate, Bonnell & Bromm (2003)</a>. The files here are not the original driving patterns used in the BBB03 simulations, but have the same structure, and give a default driving pattern that can be used without having to re-generate the files. A similar set of files was used for the simulations published in <a href="https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..105L">Liptai et al. (2017)</a>.</p> <p>The files were generated with a piece of code written by Volker Bromm, which was originally part of Matthew Bate's sphNG simulation code.</p> <p>For details of how to read these files, see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/setup/velfield_fromcubes.f90">src/setup/velfield_fromcubes.f90</a>)</p>
Example of formatted files for a SynNet pipeline
<p>This is a collection of formatted .fasta (proteins) and .bed (gff) files for a SynNet pipeline.</p>
Ontology files in .owl, rdf and xml format demonstrating the texts, documents and works (=entities) ontology
<p>See various writings by Robinson concerning this ontology (e.g. , <a href="https://wiki.usask.ca/pages/viewpage.action?pageId=1324745355">Creating and Implementing an Ontology of Documents and Texts (ADHO 2018)</a>.</p> <p>Note revision of 10/21: removal of parts of document, work, text. dc:hasPart and dc:isPartOf make this redundant.</p>
UPLC-QTOF data from a river sample extracted with various solid phase extraction phases - mz5 files with scans in centroid spectrum format
<p>This dataset has been acquired from a river sample (Marne River, France) collected as part of the Screenatm'eau project (Observatoire des Sciences de l’Univers - Enveloppes FLUides de la Ville à l’Exobiologie - OSU-EFLUVE, Université Paris-Est Créteil). The sample was processed by solid-phase extraction using multiple cartridges and phases, in triplicates (see the list of samples in the samplemetadata.csv file).</p> <p>Data was acquired with a SYNAPT HDMS QTOF (Waters) coupled with a Nano ACQUITY UPLC System (Waters), in ESI positive mode. Raw data was converted using Proteowizard MSConvert version 3.0.21288, using zlib compression, with the CWT peakpicking algorithm (snr=0, peakSpace=0) to transform profile to centroid data, and the zeroSamples option (removeExtra 1-). The chosen output formats were .mzML and .mz5.</p> <p>A list of markers was obtained after peak picking and alignment across all samples using the PatRoon R package with the OpenMS algorithm, and exported as a markertable.csv file. Each detected marker has m/z and retention time values (see the variablemetadata.csv file), and intensity values in all samples (markertable.csv).</p> <p>For file naming explanation and details about each SPE phase, see the readme.txt file.</p>
RDF2Vec DBpedia uniform embeddings in HDF5 file format
<p>This dataset contains the vectors from computing RDF2vec embeddings from a uniformly weighted DBpedia 2016-04 graph.</p> <p>The file has a group called "Vectors" which contains a dataset for each entity in the graph. The dataset name is the entity name and the dataset content is the embedded vector (length 200).</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In <em>Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics</em> (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
MALDI-IMS files of 53 experiments over sections of nevus, primary melanoma and melanoma metastasis in imzml format
<p>MALDI-IMS files of 53 experiments over sections of nevus, primary melanoma and melanoma metastasis in imzml format. The experiments were recorded in negative-ion mode with a MALDI LTQ-orbitrap XL (ThermoFisher) at 25 um/pixel of spatial resolution. We also uploaded three .ppt with a comparison between the eosin-hematoxilin images, the IHC (MelanA and HMB45) and the segmentation images</p>
The raw EEG data, 4 files (EEG_A to D), in European data format (.edf)
<p>EEG data for comparison to PIR-estimated sleep in the Wellcome Open Research article:</p> <p>'COMPASS: Continuous Open Mouse Phenotyping of Activity and Sleep Status'</p>
ENTSO-E ERAA 2023 files in Parquet format
<p>demand time-series, PECD (renewables) and hydropower data from the ERAA 2023 (<a href="https://www.entsoe.eu/outlooks/eraa/2023/eraa-downloads/">ERAA Downloads | ENTSO-E – ERAA 2023 (entsoe.eu)</a>). </p> <p>Original data (in CSV and Excel format) is converted in Parquet in a format more suitable for modelling and analysis.</p> <p>This dataset includes:</p> <ul> <li>Demand time-series for all the modelled zones for the years 2025, 2028, 2030, 2033</li> <li>Capacity factors for all the modelled zones for the following technologies: wind onshore, wind offshore, solar PV rooftop, solar PV utility-scale, CSP with storage and without storage. All the capacity factors are available for the year 2025, 2028, 2030, 2033. All the files are available in the zipped file PECD.zip</li> <li>Hydropower data (installed capacity and time-series of daily/weekly inflow, min/max generating power, min/max reservoir levels) for run-of-river, pondage, reservoir-based plants, pumped-storage open- and closed-loop.</li> </ul>
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.