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7,195 results for “SET”
Gone with the wind data sets
<p>The data used for the research article Gone with the wind: Effects of wind on honey bee visitation rate and foraging behaviour. These include data for the direct effect of wind on all flower spacings and behaviour as well as the data sheets used for analysing data on only one or two spacings. A separate spreadsheet for the data on the indirect effects is also attached.</p>
Survey Data Set Part 1 - Attitudes Towards Videos as a Documentation Option for Communication in Requirements Engineering
<p>In 2017, we conducted an online survey to explore software professionals' attitudes towards videos as a documentation option for communication in requirements engineering. The survey covered the following topics:</p> <ul> <li>Demographics</li> <li>Attitude towards videos as a medium in RE including its strengths, weaknesses, opportunities, and threats</li> <li>Current production and use of videos in RE, respectively the obstacles that prevent the production and use of videos</li> </ul> <p>64 out of 106 software professionals from industry and academia completed the survey. The survey was implemented in LimeSurvey and distributed across several communication channels such as LinkedIn, ResearchGate, and a mailing list of a German RE professionals group.</p> <p>This dataset includes the following files:</p> <ul> <li>"Raw and analyzed data.xlsx" contains the raw and analyzed survey responses which are anonymized <ul> <li>This data includes <em>demographics </em>and <em>attitude</em>.</li> <li>The data on <em>video production and use</em> are included in: <a href="https://zenodo.org/record/4064741">Survey Data Set Part 2 - Attitudes Towards Videos as a Documentation Option for Communication in Requirements Engineering</a>.</li> </ul> </li> <li>"Survey - Offline version.docx" contains the questions and possible answers of the survey</li> <li>"Survey - Offline version.pdf" contains the questions and possible answers of the survey</li> </ul> <p>This survey was designed, conducted, and analyzed by Oliver Karras (<a href="https://twitter.com/KarrasOliver">@KarrasOliver</a>).</p>
Raw data set: One-Step Fabrication of GeSn Branched Nanowires
<p>This is the raw data set for the publication titled " One-Step Fabrication of GeSn Branched Nanowires" in the journal Chemistry of Materials (<em>Chem. Mater.</em>201931114016-4024). Below is the abstract of the publicaiton.</p> <p>Abstract: We report for the first time the self-catalysed, single step growth of branched GeSn nanostructures by a catalytic vapour-liquid-solid (VLS) mechanism. These typical GeSn nanostructures consist of <111> oriented Sn rich (~8 at. %) GeSn “branches” grown epitaxially on GeSn “trunks”, with a Sn content of ~ 4 at. %. The trunks are seeded from Au<sub>0.80</sub>Ag<sub>0.20</sub> nanoparticles followed by the catalytic growth of secondary branches (diameter ~ 50 nm) from the excess of Sn on the sidewalls of the trunks, as determined by high resolution electron microscopy and energy dispersive X-ray (EDX) analysis. The nanowires, with <111> directed GeSn branches oriented at ~ 70 ° to the trunks, have no apparent defects or change in crystal structure at the trunk-branch interface; structural quality is retained at the interface with epitaxial crystallographic relation. Electrochemical performance of these highly ordered GeSn nanostructures were explored as a potential anode material for Li-ion batteries, due to their high surface to volume ratio and increased charge carrier pathways. The unique structure of branched nanowires led to high specific capacities comparable to, or greater than, conventional Ge nanowire anode materials and Ge<sub>1-<em>x</em>­</sub>Sn<em><sub>x</sub></em><sub>­</sub> nanocrystals.We report for the first time the self-catalysed, single step growth of branched GeSn nanostructures by a catalytic vapour-liquid-solid (VLS) mechanism. These typical GeSn nanostructures consist of <111> oriented Sn rich (~8 at. %) GeSn “branches” grown epitaxially on GeSn “trunks”, with a Sn content of ~ 4 at. %. The trunks are seeded from Au<sub>0.80</sub>Ag<sub>0.20</sub> nanoparticles followed by the catalytic growth of secondary branches (diameter ~ 50 nm) from the excess of Sn on the sidewalls of the trunks, as determined by high resolution electron microscopy and energy dispersive X-ray (EDX) analysis. The nanowires, with <111> directed GeSn branches oriented at ~ 70 ° to the trunks, have no apparent defects or change in crystal structure at the trunk-branch interface; structural quality is retained at the interface with epitaxial crystallographic relation. Electrochemical performance of these highly ordered GeSn nanostructures were explored as a potential anode material for Li-ion batteries, due to their high surface to volume ratio and increased charge carrier pathways. The unique structure of branched nanowires led to high specific capacities comparable to, or greater than, conventional Ge nanowire anode materials and Ge<sub>1-<em>x</em>­</sub>Sn<em><sub>x</sub></em><sub>­</sub> nanocrystals.</p>
Data set of Ultrathin Eu- and Er-Doped Y2O3 Films with Optimized Optical Properties for Quantum Technologies
<p>Data corresponding to the figures of the publication " Ultrathin Eu- and Er-Doped Y2O3 Films with Optimized Optical<br> Properties for Quantum Technologies " by M. Scarafagio et al. J. Phys. Chem. C 2019, 123, 13354-13364<br> (<a href="https://doi.org/10.1021/acs.jpcc.9b02597">https://doi.org/10.1021/acs.jpcc.9b02597</a>). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
VRTogether Pilot 2 TV Set Geommetry Unity package
<p>Unity package containing the TV set scene where all the pilot 2 happens.</p>
Eye on Core Trust Seal - Data Set
<p>This data set includes extracted information from 40 CoreTrustSeal (CTS) self-assessment reports, which were publicly available by January 15th 2019 at https://www.coretrustseal.org/why-certification/certified-repositories/. The authors extracted general information from the title pages of the self-assessment reports, as well as responses to criterion "R0 - Background Information / Context".<br> Sheet "Extracted_Information" includes all extracted info, whereas "Level_of_Curation", "Designated_Community" and "Repository_Type" include normalized and categorized data. The data cleansing / normalization and analysis processes are described in the paper "Eye on Core Trust Seal - Recommendations for Criterion R0 from Digital Preservation and Research Data Management Perspectives" to be released as part of the Proceedings of iPRES2019, the 16th International Conference on Digital Preservation. The data set forms the basis for an extended analysis of Core Trust Seal criterion R0 from the perspectives of the digital preservation and research data management domain.</p> <p><strong>Abstract of the accompanying paper:</strong></p> <p>The CoreTrustSeal (CTS) is an accepted trustworthy digital repository certification process for both, research data management and digital preservation communities alike. But does it build on concepts known and understood by both of these communities? We take an in-depth look at the CTS requirement <em>R0 - Background Information / Context</em>, in which the applicants are asked to define their repository type, designated community and level of curation performed. By extracting information from the publicly available assessment reports and cross-checking these against available supporting information, we reflect on CTS from three viewpoints: the process, the institutional, and the community view. We distill concrete recommendations, which will be fed back to the CTS Board as part of the 2019 public call for review.</p>
Test data set for macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins
<p>This a bundle of test data can be used to run the macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins.</p> <p>These data sets can be used to run the following macros that can be found on GitHub:</p> <ol> <li><a href="https://github.com/molcyto/MC-Ratio-96-wells">https://github.com/molcyto/MC-Ratio-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Ratio-Petri-dish">https://github.com/molcyto/MC-Ratio-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-Petri-dish">https://github.com/molcyto/MC-FLIM-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-Bleach-96-wells">https://github.com/molcyto/MC-Bleach-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Scatter5D">https://github.com/molcyto/MC-Scatter5D</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-96-wells">https://github.com/molcyto/MC-FLIM-96-wells</a></li> </ol> <p>Funding:<br> This work was supported by the NWO CW-Echo grant 711.011.018 (M.A.H. and T.W.J.G.), grant 12149 (T.W.J.G.) from the Foundation for Technological Sciences (STW) from the Netherlands</p> <p> </p>
3D-structured Supports create complete Data Sets for Electron Crystallography
<p>Each tar file contains the raw files in HDF5 format, together with the XDS.INP file used for data integration.</p> <p>NB: The meta-data in the HDF5 files have no meaning, please refer to the respective XDS.INP file for respective information.</p>
PCA SUB ELITE YOUTH Handball DATA_SET
<p>The presented data base refers to results of study about PCA in Handball locomotion analysis of youth sub-elite handball players.</p>
Inferelator Saccharomyces Cerevisiae Single-Cell Data Set
<p>This data is associated with the Inferelator package. It has an expression data set (103118_SS_Data.tsv.gz), which is a [Cells x Genes] TSV file which has 5 included metadata columns [Genotype, Genotype_Group, Replicate, Condition, tenXBarcode]. It also contains a prior data matrix generated from the YEASTRACT database (YEASTRACT_Both_20181118.tsv), a gold standard derived from the YEASTRACT database (gold_standard.tsv), a list of transcription factors (tf_names_restrict.tsv), and a list of protein-coding genes (orfs.tsv). It was initially used in Jackson, C.A., Castro, D.M., Saldi, G.-A., Bonneau, R., and Gresham, D. (2019). Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments. BioRxiv 581678.</p>
Data set supporting journal article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019
<p>This data set contains evapotranspiration data obtained by a conventional eddy covariance set-up and a low-cost eddy covariance set-up as described in the research article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019.</p> <p>The data set contains all necessary data needed to replicate figures and analysis presented in the research article. The data sets are sorted and named according to the figure the data were used for. </p>
Text-fig. 10.—A possible hunting set of Hell Creek theropods, drawn to scale. A, Tyrannosaurus rex. B, Albertosaurus lancensis. C, the Jordan theropod. D, Saurornithoides mongoliensis. S. mongoliensis is not present in the Hell Creek, but is used to represent those saurornithoidids and dromaeosaurids present and represented by isolated teeth. A fifth form, Paronychodon lacustris, also represented only by isolated teeth has not been included but was probably intermediate between the saurornithoidids and the Jordan theropod. in A new Theropod Dinosaur from the Upper Cretaceous of Central Montana
Text-fig. 10.—A possible hunting set of Hell Creek theropods, drawn to scale. A, Tyrannosaurus rex. B, Albertosaurus lancensis. C, the Jordan theropod. D, Saurornithoides mongoliensis. S. mongoliensis is not present in the Hell Creek, but is used to represent those saurornithoidids and dromaeosaurids present and represented by isolated teeth. A fifth form, Paronychodon lacustris, also represented only by isolated teeth has not been included but was probably intermediate between the saurornithoidids and the Jordan theropod.
"A geothermal application for GOCE..." accompanying data set
<p>Accompanying data to the paper</p> <blockquote> <p>Pastorutti, A., & Braitenberg, C., 2019. <strong>A geothermal application for GOCE satellite gravity data: modelling the crustal heat production and lithospheric temperature field in Central Europe</strong>, Geophysical Journal International, <a href="https://doi.org/10.1093/gji/ggz344">doi:10.1093/gji/ggz344</a></p> </blockquote> <p>which has been accepted for publication in <em>Geophysical Journal International</em> following peer review.</p> <p><strong>The paper version of record is available online at: <a href="https://doi.org/10.1093/gji/ggz344">doi.org/10.1093/gji/ggz344</a>.</strong></p>
Mowgli: DBMS Elasticity Evaluation Data Sets
<p>These data sets contain the complete DBMS evaluation data created by the <a href="https://omi-gitlab.e-technik.uni-ulm.de/mowgli/getting-started">Mowgli</a> framework for the <em>calibration</em> and <em>elasticity</em> phases.</p> <p><em>Calibration phase:</em> performance/latency metrics for growing workload intensities issued against a fixed three node DBMS cluster.</p> <p><em>Elasticity phase:</em> performance/latency metrics during an elastic scale-out adaptation (i.e. adding one additional DBMS node to the cluster at runtime) for different workload intensities.</p>
The demo data set for the meta16S-Seq workflow using Qiime2
<p>The demo data used in the introduction of meta16S-Seq workflow using Qiime2. The original manuscript of the workflow introduction is written by Yuh Shiwa. The workflow is translated in Common Workflow Language by Tazro Ohta.</p>
Data set of the article: Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus
<p>Data of investigation published in the article "Ranking by relevance and citation counts, a comparative study: Google Scholar, Microsoft Academic, WoS and Scopus".</p> <p>Abstract of the article:</p> <p>Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyse and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyse two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, WoS used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.</p>
Proteomic data set of the analysis of black poplar (Populus nigra L.) seed storability
<p>Proteomic data set containing protein identification parameters (ESI MS/MS) and GO annotation functional classification (UniProt and QuickGO). Identification parameters of differentially abundant proteins of black poplar (<em>Populus nigra</em> L.) seeds stored in different temperature (3, -3, -20 and -196°C) and time (12 and 24 months) conditions. Proteins were extracted and separated according to their isoelectric point (pI) and mass using 2-dimensional electrophoresis. Proteins that varied in abundance for temperature and time of storage were identified by mass spectrometry (ESI MS/MS). The mascot search algorithm (http://www.matrixscience.com) was used for protein identification against the NCBInr (http://www.ncbi.nig.gov) databases.Identified proteins were grouped due to biological process, molecular function and subcellular localization according to the gene ontology (GO) annotation using UniProt database and QuickGO search (https://www.ebi.ac.uk/QuickGO/).</p>
Data set for the PLOS ONE paper Expecto transitio: Exploring non-experts' techno-economic expectations of the energy future
<p>Raw data file (SPSS and .cv versions) consisting of all data used for the Plos One publication.</p> <p> </p>
Sensor data set radial forging at AFRC testbed v2
<p><strong>Sensor data set, radial forging at AFRC testbed</strong></p> <p><strong>General information on the data set</strong></p> <p>Radial forging is widely used in industry to manufacture components for a broad range of sectors including automotive, medical, aerospace, rail and industrial. The Advanced Forming Research Centre (AFRC) at the University of Strathclyde, Glasgow, houses a GFM SKK10/R radial forge that has been used as a testbed for this project. Using two pairs of hammers operating at 1200 strokes/min, and providing a maximum forging force per hammer of 150 tons, the radial forge is capable of processing a range of metals, including steel, titanium and inconel. Both hollow and solid material can be formed with the added benefit of creating internal features on hollow parts using a mandrel. Parts can be formed at a range of temperatures from ambient temperature to 1200 °C.</p> <p>For the provided data set, a total of 81 parts were forged over one day of operation. A machine failure occurred during the forging of part number 70, and this part was re-run once the malfunction had been fixed. Each forged part was then measured using a CMM to provide dimensional output relative to a target specification and tolerances. The CMM records 18 dimensional measurements.</p> <p>The aim of the measurement setup is to predict the quality (in terms of dimensional properties) of the forged part from the sensor measurements during the forging process.</p> <p><strong>Structure of the data</strong></p> <ul> <li>The sensor readings for the forging of the parts are provided in 81 csv files in the folder “Scope Traces”, named “Scope0001.csv” to “Scope0081.csv”. Each file contains the readings (columns) against time (rows). The first column displays the clock times (in milliseconds).</li> <li>A commentary on the sensors is provided in the file “ForgedPartDataStructureSummaryv3.xlsx” <strong>(NOTE: Some columns do not have sensor descriptions as this information is not available).</strong></li> <li>The CMM data is provided in the file “CMMData.xlsx”.</li> </ul> <p><strong>Further Information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available here:</p> <p><a href="https://github.com/harislulic/Strathcylde_AFRC_machine_learning_tutorials/releases/tag/v2.0">https://github.com/harislulic/Strathcylde_AFRC_machine_learning_tutorials/releases/tag/v2.0</a></p> <p>These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set.</p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </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.