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RCS-Image Dataset
<p>Note: There is a single zip file of the project available under the name "<a href="https://zenodo.org/api/files/30cdde03-6e96-4019-9a1a-bef54f3d2737/RCS%20Image%20Dataset.zip">RCS Image Dataset.zip </a>"</p> <p>A synthetic dataset for object detection, semantic segmentation and depth recognition was created using images from a virtual environment in Unreal Engine. This dataset that could be used to retrain neural networks on virtual aerial images, for object detection, segmentation and depth planning.</p> <p>There is ground truth for full pixel level semantic segmentation and object detction by way of bounding box coordinates in .exif files. The main labels present in the dataset are train truck and gas cylinders The size of the data is currently more than 10GB, with 1000 aerial images, from 100 different waypoints at 10 different heights. For each raw image, there is also an associated depth image, a segmented image, object ground truths with bounding box, and the metadata .exif file.</p> <p>If you found this dataset useful for your reserach, please cite as,</p> <pre>@article{smyth2018virtual, title={A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation}, author={Smyth, David L and Fennell, James and Abinesh, Sai and Karimi, Nazli B and Glavin, Frank G and Ullah, Ihsan and Drury, Brett and Madden, Michael G}, journal={arXiv preprint arXiv:1806.04497}, year={2018} }</pre> <p> </p>
Dataset for the tutorial "pinpoint key pathways with Heinz"
<p> The dataset for the tutorial "pinpoint key pathways with Heinz" in Galaxy training network. </p>
Datasets for practical model selection for prospective virtual screening
<p>This repository contains datasets for the manuscript "Practical model selection for prospective virtual screening":</p> <ul> <li><strong>pria_rmi_cv.tar.gz</strong>: A compressed directory containing chemical screening data for the <strong>PriA-SSB AS</strong>, <strong>PriA-SSB FP</strong>, and <strong>RMI-FANCM FP</strong> binary datasets. The files also contain the associated continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints. The dataset has been split into five folds for cross validation.</li> <li><strong>pria_rmi_pcba_cv.tar.gz</strong>: A compressed directory containing chemical screening data for the <strong>PriA-SSB AS</strong>, <strong>PriA-SSB FP</strong>, and <strong>RMI-FANCM FP</strong> binary datasets as well as public PubChem BioAssay datasets. The files also contain the PriA-SSB and RMI-FANCM continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints. The dataset has been split into five folds for cross validation. Missing values are left blank.</li> <li><strong>pria_prospective.csv.gz</strong>: A compressed file containing chemical screening data for the binary dataset <strong>PriA-SSB prospective</strong>. The file also contains the continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints.</li> </ul> <p>If you use these data in a publication, please cite:</p> <p>Shengchao Liu<sup>+</sup>, Moayad Alnammi<sup>+</sup>, Spencer S. Ericksen, Andrew F. Voter, Gene E. Ananiev, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. Practical Model Selection for Prospective Virtual Screening. Journal of Chemical Information and Modeling. 2018 <a href="https://doi.org/10.1021/acs.jcim.8b00363">doi:10.1021/acs.jcim.8b00363</a></p> <p>PubChem data were provided by the <a href="https://pubchem.ncbi.nlm.nih.gov/">PubChem database</a>. Follow the <a href="https://pubchemdocs.ncbi.nlm.nih.gov/citation-guidelines">PubChem citation guidelines</a> if you use the PubChem data. See <a href="https://doi.org/10.1177/2472555217712001">Voter et al. 2017</a> (PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1272365">1272365</a>) for the PriA-SSB screening data and <a href="https://doi.org/10.1177/1087057116635503">Voter et al. 2016</a> (PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1159607">1159607</a>) for RMI-FANCM.</p> <p>Version 1.1.0 updates all of the data files. We standardized the SMILES in all files by generating canonical SMILES with RDKit version 2016.03.4. In addition, we removed 2845 chemicals from pria_prospective.csv.gz that were duplicates of compounds in pria_rmi_cv.tar.gz.</p>
The behavioral phenotype of early life adversity
<p>In this dataset, we categorized studies investigating the effects of early life adversity on behavior in mice and rats. The dataset is ideal for meta-analyses. For more information about the dataset and the project, see https://osf.io/ra947/</p>
Frøya wind data
<p>Herewith we present the dataset of wind measurements from a Skipheia meteorological station on the island of Frøya on the western coast of Norway, Trondelag.</p> <p>The site represents an exposed coastal wind climate with open sea, land and mixed fetch from various directions. UTM-coordinates of the Met-mast: 8.34251 E and 63.66638 N.</p> <p>Presented data were gathered between years 2009-2015;</p> <p>Hardware summary: 6 pairs of 2D sonic anemometers at 10, 16, 25, 40, 70, 100 m above the ground, independent temperature measurements at the same heights and near the ground; pressure and relative humidity from local meteostation (Sula, 20 km away).</p> <p>Database summary: approx. 180 000 of 10 min data samples of full data recovery. Wind speed and direction, temperature, pressure & relative humidity (from a nearby meteostation).</p> <p>Data description: Two data files of different formats are available: a ‘*.txt’ comma-separated values file and a native MATLAB ‘*.mat’ file. Both contain the same data, starting with the first column: timestamp, wind speed (m/s, columns WS1-WS12) for 6 anemometers pairs, wind direction (360 deg, columns WD1-WD12) for 6 anemometers pairs, temperature at 0.2 m (AT0), temperatures at levels of wind measurement (deg C, AT1-AT6), data from nearby meteostation Sula, pressure (hPa, PressureSula), relative humidity (%, RelHumSula), temperature (deg C, TempSula), wind direction (360 deg, WDSula) and wind speed (m/s, WSSula). Columns have headers describing the data (first row).</p> <p>Detailed site description with wind climate description can be found in attached analysis: Site analysys.pdf.</p> <p>Additional information and analysis can be found in listed below works, using data from Frøya site, or nearby sites:</p> <p><strong> </strong>Møller, M., Domagalski, P., and Sætran, L. R.: Comparing Abnormalities in Onshore and Offshore Vertical Wind Profiles, Wind Energ. Sci. https://wes.copernicus.org/articles/5/391/2020/ </p> <p>IEA Wind TCP Task 27 Compendium of IEA Wind TCP Task 27 Case Studies, Technical Report, Prepared by Ignacio Cruz Cruz, CIEMAT, Spain Trudy Forsyth, WAT, United States, October 2018; Chapter 1.8. <a href="https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0">https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0</a></p> <p>Domagalski, P., Bardal, L. M., & Satran, L. Vertical Wind Profiles in Non-neutral Conditions-Comparison of Models and Measurements from Froya. <em>Journal of Offshore Mechanics and Arctic Engineering,</em> doi: 10.1115/1.4041816, <a href="http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3">http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3</a></p> <p>Mathias Møller , Piotr Domagalski and Lars Roar Sætran, Characteristics of abnormal vertical wind profiles at a coastal site, <em>Journal of Physics: Conference Series</em>, IOPscience, under review (Feb 2019), DeepWind2019 conference poster available at: <a href="https://www.sintef.no/globalassets/project/eera-deepwind-2019/posters/c_moller_a4.pdf">https://www.sintef.no/globalassets/project/eera-deepwind-2019/posters/c_moller_a4.pdf</a></p> <p>Bardal, L. M., Onstad, A. E., Sætran, L. R., & Lund, J. A. (2018). Evaluation of methods for estimating atmospheric stability at two coastal sites. <em>Wind Engineering</em>, 0309524X18780378, <a href="https://doi.org/10.1177%2F0309524X18780378">https://doi.org/10.1177/0309524X18780378</a></p> <p>Bardal, L. M., & Sætran, L. R. (2016, September). Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines. In <em>Journal of Physics: Conference Series</em> (Vol. 753, No. 3, p. 032033). IOP Publishing, doi:10.1088/1742-6596/753/3/032033, <a href="https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf">https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf</a></p> <p>Bardal, L. M., & Sætran, L. R. (2016). Wind gust factors in a coastal wind climate. <em>Energy Procedia,</em> 94, 417-424, <a href="https://doi.org/10.1016/j.egypro.2016.09.207">https://doi.org/10.1016/j.egypro.2016.09.207</a></p>
GESIS - Leibniz Institute for the Social Sciences data access categories
<p>Replication code for extracting and analysing data access categories from the oai-pmh feed provided by the GESIS - Leibniz Institute for the Social Sciences DBK data catalogue. The code utilises the dc_oai-de feed to extract metadata about objects in the data catalogue, this is then edited to retain and summarise information on the four data access categories used by the archive. The oai-pmh metadata is available from GESIS under a CC0 licence.</p> <p>The .csv files extracted from the oai-pmh feed and edited to correct for missing records is also included for replication.</p>
BiVib - Audio-Tactile Piano Sample Library
<p><strong>BiVib</strong> is an extensive piano sample library consisting of <strong>bi</strong>naural sounds and keyboard <strong>vib</strong>ration signals.<br>Samples were acquired with high-quality audio and vibration measurement equipment on two <a href="https://en.wikipedia.org/wiki/Disklavier">Yamaha Disklavier pianos</a> (one grand and one upright model) by means of computer-controlled playback of each key at ten different MIDI velocity values.<br>Project files (<em>instruments</em> and <em>multis</em>) are provided for use with the software sampler <a href="https://www.native-instruments.com/en/products/komplete/samplers/kontakt-6/">Native Instruments Kontakt</a> (version 5 and above, available for Windows and Mac OS).<br>The nominal specifications of the equipment used in the acquisition chain are reported in a companion document, allowing researchers to calculate physical quantities (e.g. acoustic pressure, vibration acceleration) from the recordings.<br>The library is especially suited for acoustic and vibration research on the piano, as well as for research on multimodal interaction with musical instruments.</p>
Seawater stable isotope sample measurements from the Antarctic Circumnavigation Expedition (ACE)
<p><strong>Dataset abstract</strong></p> <p>This data set contains oxygen and hydrogen isotope measurements from discrete seawater samples that were collected in the Southern Ocean (south of 30 deg S) during the Antarctic Circumnavigation Expedition (ACE). 637 samples were collected during the period December 24th, 2016 and March 18th, 2017 in the Southern Ocean from the surface ocean using the ship's underway line (UW; 338 samples) and in vertical profiles using Niskin bottles mounted on the CTD rosette (287 samples). A few additional samples were collected from a parallel cast with a trace-metal rosette, with a bucket, and from the surface of a tabular iceberg. All samples were analyzed for their oxygen isotopic composition (reported as permille deviation of the oxygen-18 to oxygen-16 ratio from VSMOW2: DEL18O) and a few samples (80) from the Pacific sector were analyzed for their hydrogen isotopic composition (reported as permille deviation of the hydrogen to deuterium ratio from VSMOW2: DELD) by mass spectrometry at the British Geological Survey. This circumpolar data set provides insights into the hydrological cycle of the Southern Ocean and the processes (precipitation, evaporation, sea-ice melting and freezing, iceberg and land-ice melting) that determine the salinity of a certain water mass.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_18_data_d18o_dd_ctd_20190219.csv, data file, comma-separated values</li> <li>ace_18_data_d18o_dd_other_20190219.csv, data file, comma-separated values</li> <li>ace_18_data_d18o_dd_uw_20190219.csv, data file, comma-separated values</li> <li>README.md, metadata, markdown</li> </ul>
Simulated metagenomes with quality and abundance distributions derived from real samples
<p>Species abundances and quality values were derived from the following list of samples:</p> <pre><code>SAMEA2466896 SAMEA2466916 SAMEA2466952 SAMEA2466953 SAMEA2466965 SAMEA2466996 SAMEA2467015 SAMEA2467039 SAMEA2621010 SAMEA2621033 SAMEA2621107 SAMEA2621155 SAMEA2621229 SAMEA2621247 SAMEA2621300 SAMEA2622357 </code></pre> <p>Reference abundances (.abund files) were generated using <a href="https://github.com/motu-tool/mOTUs_v2">mOTUs profiler</a>.<br> Metagenomes were simulated with <a href="https://sourceforge.net/projects/cmessi/">cMESSi</a> using <a href="http://progenomes.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> for species and the aforementioned abundances. In cases where a <em>ref_mOTU_v2</em> corresponded to more than one genome, the abundance of said <em>ref_mOTU</em> was distributed equally over all genomes.<br> GFF location files were produced using location information generated by cMESSi.<br> Two variants of truth values were obtained by intersecting coordinates of simulated reads with coordinates of <a href="http://eggnogdb.embl.de">eggNOG</a> orthologous groups (OG at NOG level) as predicted by <a href="https://github.com/jhcepas/eggnog-mapper">eggNOG-mapper</a>.</p> <ol> <li>.cog-simulated files contain the NOG distribution that was effectively simulated, <em>i.e.</em> a count of the number of reads overlapping with genes annotated with each NOG. A read overlapping multiple genes is considered for each gene. If a gene possesses multiple NOG annotations, each annotation gets assigned the total number of overlapping reads. Longer genes will (in expectation) generate more reads, all else being equal.</li> <li>.cog-distribution file contains the expected distribution for every NOG on all samples. The number of genes annotated with each NOG is multiplied by the abundance of the corresponding species. Length of the gene is not taken into account.</li> </ol> <p>If you use this dataset, please cite: <a href="https://www.biorxiv.org/node/111718.full">NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language</a></p>
GPR data used to test the efficient deconvolution method of Schmelzbach and Huber (2015)
<p>GPR data recorded with Pulse Ekko Pro from Sensors & Software on the river bed of the Tagliamento River (NE Italy).</p> <p>This data was used to test the efficient deconvolution scheme of Schmelzbar and Huber (2015):</p> <p>C. Schmelzbach, E. Huber (2015) Efficient Deconvolution of Ground-Penetrating Radar Data. IEEE Transactions on Geoscience and Remote Sensing, 53(9): 5209 - 5217<br> doi: <a href="http://dx.doi.org/10.1109/TGRS.2015.2419235">10.1109/TGRS.2015.2419235</a></p>
Replication data for: Reconciliation k-median: Clustering with non-polarized representatives
<p># Description<br> These files contain the data employed in the experiments described in Bruno Ordozgoiti and Aristides Gionis. 2019. Reconciliation k-median: Clustering with Non-Polarized Representatives. In Proceedings of the 2019 World Wide Web Conference (WWW’19), May 13–17, 2019, San Francisco, CA, USA.</p> <p>Twitter ID's have been anonymized.</p> <p># Contents<br> domain_mentions.txt: Each line contains a domain name, a user ID and the number of times this user has mentioned this domain name in a tweet.<br> format: domain_name <TAB> user_id <TAB> mention_count</p> <p>domains_ideology_score.txt: Domain names and their ideology score, estimated as described in (Lahoti et al. WSDM 2018). Note: missing scores can be retrieved from supplementary data in https://doi.org/10.1093/poq/nfw006<br> format: domain_name <TAB> ideology_score</p> <p>follow_graph.txt: The Twitter follower graph. Each line contains a user id and the user id of one of its followers.<br> format: user_id <TAB> follower_user_id</p> <p>representatives.txt: US Congress representatives, each with Twitter handle and polarity score computed using Barbera's method (Barbera, 2015).<br> format: rep_name <TAB> website_url <TAB> district <TAB> twitter_handle <TAB> party <TAB> barbera_polarity_score</p> <p>user_polarity.txt: User ID's and polarity score computed using Barbera's method (Barbera, 2015).<br> format: user_id <TAB> barbera_polarity_score</p>
CoHERE Work Package 5 Survey of free time activities amongst Latvian schoolchildren
<p>The quantitative survey «Youth and leisure time activities, informal education and cultural heritage» was carried out as part of Work Package 5 (Education, heritage and identities) of 'Critical Heritages: Performing and presenting identities in Europe' (https://research.ncl.ac.uk/cohere/researchstrands/). This Work Package develops best practices in the production and transmission of European heritages and identities within two sectors that face challenges in an age of immigration and globalization, namely education and cultural heritage production. It explores how European identity is shaped through formal and informal learning situations both in and outside the classroom with the purpose of enhancing school curricula and informal learning at heritage sites by integrating innovative technologies and including multicultural perspectives.</p> <p>The target group: youth (age 16 to 19) from secondary schools and professional education schools in Latvia </p> <p>Sample size: 1047. Time period: December 2017 – March 2018.</p> <p>Method: a self-administered questionnaire.</p> <p>The aim of the survey is to examine how cultural heritage shape different identities in Europe, representing ideas of place, history, traditions and sense of belonging.</p> <p>Tasks of the survey:<br> 1) to get information about leisure time activities of the youth and their participation in different informal education activities;<br> 2) to examine youth opinion about the role of cultural heritage and their involvement in safeguarding cultural heritage;<br> 3) to analyse the role of cultural heritage in formation of local, national and European identities of the young people.</p>
CoHERE Work Package 3 Survey of Inhabitants of Baltic Countries on Song and Dance Celebrations
<p>Part of Work Package 3 for the 'Critical Heritages' research project ( https://research.ncl.ac.uk/cohere/researchstrands/#WP3%20Cultural%20forms%20and%20expressions%20of%20identity%20in%20Europe ).</p> <p>One of the key case studies in CoHERE Work Package 3 has been the Song and Dance Celebration tradition in the Baltic states (included in the UNESCO list as a masterpiece of the oral and intangible heritage of humanity in 2003). The case study reveals several aspects of this festival: cultural, economic, social dimensions and governance. Through examining different aspects of this festival tradition and everyday practices it responds to several objectives of the WP3. Being a key social and cultural event in three Baltic countries, it provides a ground for debates on how performative practices and festivals can contribute to identity construction and transformation, developing sense of belonging, serve as platform for where heritage practices of different social groups can meet.</p>
MOD-LSP: MODIS-Based Parameters for Variable Infiltration Capacity (VIC) Model over the Continental US, Mexico, and Southern Canada
<p>The MOD-LSP project contains MODIS-based land and surface (soil and vegetation) parameters for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994), release 5.0 and later (Hamman et al., 2018). The MOD-LSP spatial domain covers the continental United States, Mexico, and southern Canada; the associated domain files can be found in the <a href="https://zenodo.org/record/2564019">PITRI archive</a> (Bohn et al. 2018). This spatial domain and 0.625° (6 km) grid resolution are compatible with the gridded daily meteorological forcings of Livneh et al. (2015) ("L2015" hereafter) (http://ciresgroups.colorado.edu/livneh/data/daily-observational-hydrometeorology-data-set-north-american-extent), which can be disaggregated to hourly time step via the MetSim tool (Bennett et al. 2018) using the <a href="https://zenodo.org/record/2564019">aforementioned PITRI domain files</a> (Bohn et al. 2018).</p> <p>These parameters have two main purposes: (1) to improve upon previous widely-used parameters over the region (e.g., L2015) with updated, higher-resolution land cover maps and spatially explicit observations of surface properties; and (2) to expand from a single parameter set corresponding to one point in time to a series of parameter sets that account for temporal variability at seasonal to decadal scales.</p> <p>A detailed description of methods, the data sources and purposes of different VIC parameter sets within MOD-LSP, and how to use them with VIC, can be found in the MOD-LSP User Guide.pdf, included here. The scripts that were used to create the MOD-LSP parameters are archived on <a href="https://zenodo.org/record/3364149">Zenodo and GitHub</a> (Bohn 2019).</p> <p>If you wish to present or publish results that use these parameter sets, please cite the following paper:</p> <p>Bohn, T. J., and E. R. Vivoni, 2019b: MOD-LSP, MODIS-based land surface properties for assessing land cover variability and change over North America. Sci. Data, 6, 144, doi: 10.1038/s41597-019-0150-2.</p> <p>In addition, if you use the domain files associated with the PITRI precipitation disaggregation to accompany the MOD-LSP parameter files in VIC simulations, please cite the following paper:</p> <p>Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019: A deterministic approach for approximating the diurnal cycle of precipitation for use in large-scale hydrological modeling. J. Hydrometeorol., 20, 297–317, doi:10.1175/JHM-D-18-0203.1.</p> <p>Contents:</p> <ul> <li>MOD-LSP User Guide v1.0.pdf - Explains how parameters were generated and how to set up the files for input in VIC simulations.</li> <li>global_param.template - Template for global_parameter file, which lists the locations of the other input files and sets various simulation options. The template contains placeholders for some filenames and simulation options, which must be replaced with real values by the user.</li> <li>params.$DOMAIN.L2015.nc - VIC-5 compliant NetCDF parameter files with values taken from the L2015 project for domain $DOMAIN (which is one of "CONUS_MX" or "USMX").</li> <li>params.CONUS_MX.MOD_IGBP.mode.2000_2016.nc - VIC-5 compliant NetCDF parameter file over the CONUS_MX domain, with land cover fractions taken from the MODIS MCD12Q1.006 product and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the climatological mean of MODIS observations over the period 2000-2016.</li> <li>params.USMX.NLCD_INEGI.$LCID.2000_2016.nc - VIC-5 compliant NetCDF parameter file over the USMX domain, with land cover fractions taken from the NLCD_INEGI dataset, from year = $LCID, and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the climatological mean of MODIS observations over the period 2000-2016.</li> <li>params.USMX.NLCD_INEGI.$LCID.$YYYY_$YYYY.nc - VIC-5 compliant NetCDF parameter file over the USMX domain, with land cover fractions taken from the NLCD_INEGI dataset, from year = $LCID, and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the MODIS observations from a single year $YYYY.</li> <li>veg_hist.$DOMAIN.$LCTYPE.$LCID.2000_2016.nc - timeseries of monthly land surface properties (LAI, Fcanopy, albedo) from MODIS observations spanning years 2000-2016, over domain $DOMAIN, aggregated over land cover classification $LCTYPE from year $LCID.</li> </ul>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 35 known metabolites(all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in one Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and one organism part (annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable STATO terms. The measurements over these metabolites, which were made in 2 distinct experiments, were extracted from: a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018 a supplementary material table available as a pdf from 'Biosynthesis of monoterpene scent compounds in roses' by Magnard et al, Science 03 Jul 2015 identified by the following doi: https://doi.org/10.1126/science.aab0696. 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.It is associated to the following project: https://github.com/proccaserra/rose2018ng-notebook with all the necessaryinformation, executable code and tutorials in the form of Jupyter notebooks.</p>
102 hpf medaka embryos in 96 well plate (4 embryo/well) - brightfield - 2X magnification - ACQUIFER Imaging Machine
<p>Dataset originates from:</p> <p>Gierten, J., Pylatiuk, C., Hammouda, O. T., Schock, C., Stegmaier, J., Wittbrodt, J., Gehrig, J. and Loosli, F. (2020). <strong>Automated high-throughput heartbeat quantification in medaka and zebrafish embryos under physiological conditions</strong>. Sci Rep <em>10</em>, 2046, doi:<a href="https://doi.org/10.1038/s41598-020-58563-w">10.1038/s41598-020-58563-w</a>.</p> <p>Used as benchmark dataset for Multi-Template-Matching by Thomas and Gehrig </p> <p>See implementation in Fiji <a href="https://github.com/LauLauThom/MultipleTemplateMatching">https://github.com/LauLauThom/MultipleTemplateMatching</a></p> <p>and in KNIME <a href="https://github.com/LauLauThom/MultipleTemplateMatching-KNIME">https://github.com/LauLauThom/MultipleTemplateMatching-KNIME</a></p> <p>Contacts: j.gehrig(at)acquifer.de, l.thomas(at)acquifer.de, jakob.gierten(at)cos.uni-heidelberg.de</p>
Replication Data for: Replication for: How Much Do Startups Impact Employment Growth in the U.S.?
<p>These are data files to support the replication of the blog post "How Much Do Startups Impact Employment Growth in the U.S.?" The replication is not a complete replication attempt. Files were downloaded from the U.S. Census Bureau at https://www.census.gov/ces/dataproducts/bds/data_firm.html on 2019-04-23. A codebook, as provided by the U.S. Census Bureau on the same date, is provided.</p>
Pileup Jet Dataset for PUMML
<p>Dataset of events used in the PUMML paper. One file contains datasets with 2k events per pileup vertex number (mu) for mu=0-180 and the other contains 50k events for different signal processes (specified by the mass of a scalar particle decaying to quarks) all with mu=140.</p> <p>A notebook demonstrating how to use the files can be found at <a href="https://github.com/pkomiske/PUMML/blob/master/PUMML%20Events.ipynb">https://github.com/pkomiske/PUMML/blob/master/PUMML%20Events.ipynb</a>.</p>
Data for "Measurement of the atom-surface van der Waals interaction by transmission spectroscopy in a wedged nano-cell"
<p>The data presented in publication <a href="http://arxiv.org/abs/1905.02783">"Measurement of the atom-surface van der Waals interaction by transmission spectroscopy in a wedged nano-cell"</a> . Published version: <a href="https://doi.org/10.1103/PhysRevA.100.022503">https://doi.org/10.1103/PhysRevA.100.022503</a></p> <p>The data are in HDF5 format, with associated metadata.</p> <p>To see examples of how to use the data, and the theoretical model for analysis, see <a href="https://github.com/thermal-vapours/TAS-Transmission-Atom-Surface">https://github.com/thermal-vapours/TAS-Transmission-Atom-Surface </a></p>
S42 | HDXNOEX | Hydrogen Deuterium Exchange (HDX) Standard Set
<p>This is the collection associated with list S42 HDXNOEX on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S42</p> <p>HDXNOEX</p> <p><strong>Hydrogen Deuterium Exchange (HDX) Standard Set</strong></p> <p>HDXNOEX <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/HDXNOEX_14022019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/HDXNOEX_14022019.csv">CSV</a> (14/02/2019)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/hdxnoex">HDXNOEX List</a><br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/hdxexch">HDXEXCH List</a></p> <p>HDXNOEX <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/HDXNOEX_InChIKeys_14022019.txt">InChIKeys</a> (14/02/2019)</p> <p>Environmental standard set used to investigate hydrogen deuterium exchange in small molecule HRMS (Ruttkies et al. accepted). <a href="https://comptox.epa.gov/dashboard/chemical_lists/hdxexch">HDXEXCH</a> list also contains observed deuterated species. </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.