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.
1,600
datasets available to search
ShareScore release 0.9.0
Dataset results
1,600 results for “input”
Cholinergic input to mouse visual cortex signals a movement state and acutely enhances layer 5 responsiveness
<div>All raw data and Matlab code necessary to produce the figures of <a href="https://elifesciences.org/reviewed-preprints/89986">https://elifesciences.org/reviewed-preprints/89986</a></div> <div> <div> </div> </div>
Inputs for 2D HEC-RAS model for sensitivity analysis of DEM and mesh grid resolutions over part of the Virginia Tech StREAM Lab
<p>Inputs into a 2D HEC-RAS model used in the article written by Elizabeth M. Prior, Nathan Michaelson, Jonathan A. Czuba, Thomas J. Pingel, Valerie A. Thomas, and W. Cully Hession titled "Lidar DEM and computational mesh grid resolutions modify roughness in 2D hydrodynamic models".</p>
Input files for microbiome database
<p>Files required to complete the setup of microbiome database. The work is presented in a MSc thesis for the DTU Master´s degree in Bioinformatics & System Biology.</p>
Demo of Impatto: A Static Analyzer for Quantitative Input Data Usage
<p><strong>Impatto</strong> is a sound fully-automatic and always-terminating static analysis tool based on the quantitative framework for input data usage properties proposed by Mazzucato (https://hal.science/hal-04339001).<strong>Impatto</strong> leverages an underlying backward analyzer to compute the set of input-output relations of the program under analysis. This backward analyzer is a parameter of the tool, allowing different kind of analyses such as program or neural network analysis. Furthermore, the choice of the impact definition is also a parameter of the tool to better suit several factors, such as the program structure, the environment, and the intuition of the researcher.</p> <p>GitHub repository at https://github.com/denismazzucato/impatto</p>
Organoid tiny dataset for stitching (tiled, tif input)
<p>Dataset acquired on Yokogawa CV8000 on 2023.11.29 by Nicole Repina, Friedrich Miescher Institute for Biomedical Research</p> <p>Dataset contains 3 x 4 (12) tiled fields of view (each 1000 x 1000 pix) acquired with 50 pix overlap. Partial tile (gridded) acquisition.</p> <p>60x water objective, 2x2 binning, zyx spacing (10, 0.21667, 0.21667) um per pixel. Each field of view has 5 z-slices.</p> <p>Mouse small intestinal organoids immunostained with the following dyes (2 fluorescence channels):</p> <p>Channel 1 (C01, 405nm) = DAPI nuclear stain<br>Channel 3 (C03, 568nm) = B-catenin membrane stain</p>
Input event files for the 2014 Pileup Workshop framework
<p>This dataset contains event files for the 2014 workshop on <a href="https://indico.cern.ch/event/306155/">Mitigation of Pileup Effects at the LHC</a>. Code for using the event files is hosted on <a href="https://github.com/PileupWorkshop/2014PileupWorkshop">github</a>.</p>
PatchMAN BSA filtering databases and benchmark input and natives
<p>This repository contains the list of unbound receptors, peptides and natives that was used for PatchMAN BSA filtering paper.</p> <p> </p> <p>It also containts the databases that are used 1) search with MASTER, 2) extraction of fragments with MASTER.</p>
Figure 3. Describing the sequence of input sensations sent by the system.-Designing a Growing Functional Modules "Artificial Brain"
<p>To design the controller, first of all, the user should click the button “new” on the toolbar to<br> create an empty canvas and to assign it a controller's name, presently “pathfinder”. Then, the first<br> convenient step is to describe the feedback given by the system as a sequence of sensations (see<br> figure 3). The first sensation named “free”, indicates if the vehicle has been able or not to cross the<br> range of obstacles. Each of the next sixteen values is associated to its corresponding proximity<br> sensors that points out the presence of an obstacle. They are given a name beginning with “s”<br> followed by a number. The red color of the last sensation's field indicates that the given name is<br> invalid; presently, because it contains a blank character.</p>
Luminoso Input Data for SemEval-2018 Task 10: "Capturing Discriminative Attributes"
<p>This is the data required to run Luminoso's entry to the SemEval-2018 task on Capturing Discriminative Attributes.</p> <p>This data includes:</p> <ul> <li>A recently-computed version of the <a href="https://github.com/commonsense/conceptnet-numberbatch">ConceptNet Numberbatch</a> word embeddings</li> <li>The output of an implementation of Semantic Matching Energy over ConceptNet</li> <li>A SQLite database containing the lead section of all articles on the <a href="http://en.wikipedia.org">English Wikipedia</a> on 2017-12-20</li> <li>The text file that that database is constructed from</li> <li>A SQLite database of words that co-occur in <a href="http://storage.googleapis.com/books/ngrams/books/datasetsv2.html">Google Books 2-grams</a></li> <li>The text file containing total counts of 2-grams in the Google Books data, which that database is constructed from</li> </ul> <p>For more information, see the paper "Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge", by Robyn Speer and Joanna Lowry-Duda, to appear in the proceedings of the SemEval workshop at NAACL 2018.</p>
WP5 Input for D5.2
<p>Continuing the work of <a href="http://make-it.io/deliverables/d5-1-report-on-mapping-of-ict-and-maker-technology-and-its-use/">D5.1</a>, deliverable <a href="http://make-it.io/deliverables/d5-2-report-on-forward-scenarios-of-technology-developments-and-technology-use/">D5.2</a> describes how the interactive <a href="http://make-it.io/techradar/">Technology Radar</a> is used to describe future technology developments or trends that will impact how makers will create, communicate, organize and might even do business. This dataset represents the input data from the online spreadsheet used for D5.2.</p> <p>See also: <a href="http://make-it.io/open-data-api/">http://make-it.io/open-data-api/</a></p>
WP5 Input for TechRadar-D5.3
<p>Continuing the work of <a href="http://make-it.io/deliverables/d5-1-report-on-mapping-of-ict-and-maker-technology-and-its-use/">D5.1</a> and <a href="http://make-it.io/deliverables/d5-2-report-on-forward-scenarios-of-technology-developments-and-technology-use/">D5.2</a>, deliverable D5.3 describes how we are using the gained insights into the <strong>Maker movement</strong> and how this project serves information back into the maker community in the shape of practical tools. This dataset represents the data available within the version of the Technology Radar developed with a new layout and features for D5.3, so makers can now access the data produced in previous deliverables – previously used for research only.</p> <p>See also: <a href="http://make-it.io/open-data-api/">http://make-it.io/open-data-api/</a></p>
WP5 Input for D5.1
<p><a href="http://make-it.io/deliverables/d5-1-report-on-mapping-of-ict-and-maker-technology-and-its-use/">Deliverable 5.1</a> provides the initial mapping of both ICT technology and related applications developed and/or used by <strong>CAPs</strong> and technology developed and/or used by makers. Instead of a document an online tool for technology descriptions, the <a href="http://make-it.io/techradar/">Technology Radar</a>, is used. This dataset represents the input data from the online spreadsheet used for D5.1.</p> <p>See also: <a href="http://make-it.io/open-data-api/">http://make-it.io/open-data-api/</a></p>
The role of external inputs and internal cycling in shaping the global ocean cobalt distribution: insights from the first cobalt biogeochemical model
<p>Model output for cobalt biogeochemistry model on ORCA2 grid.</p>
A preliminary study of the allochthonous inputs into tropical streams across a land use gradient in Sabah, Malaysia
<b>Description: </b><p>Leaf and invertebrate biomass in streams</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/174"><b>A preliminary study of the allochthonous inputs into tropical streams across a land use gradient in Sabah, Malaysia</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=19">here</a></p><p><b>Data worksheets: </b>There are 2 data worksheets in this dataset:</p><ol><li><p><b>Insects</b> (Worksheet Insects)</p><p>Dimensions: 23 rows by 11 columns</p><p>Description: Insect capture rates</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project riparian site (Field type: Location)</li><li><b>Stream</b>: SAFE project stream (Field type: ID)</li><li><b>Repeat</b>: sample number for that stream (Field type: ID)</li><li><b>Total Mass of Insects (g)</b>: the total dried mass of insects collected for each of the repeats (Field type: Numeric)</li><li><b>Total Insects</b>: the total number of insects collected in each repeat (Field type: Abundance)</li><li><b>Hymenoptera</b>: the total number of hymenoptera in each repeat (Field type: Abundance)</li><li><b>Diptera</b>: the total number of diptera in each repeat (Field type: Abundance)</li><li><b>Coleoptera</b>: the total number of coleoptera in each repeat (Field type: Abundance)</li><li><b>Other.Insect</b>: the grouped total of Hemiptera, Thysanoptera, Orthoptera, Blattodea, Trichoptera, Mantodea, Ephemeroptera, Dermaptera for each repeat (Field type: Abundance)</li><li><b>Other</b>: the grouped total of Arachnida, Entognatha, Diplopoda, Chilopoda for each repeat (Field type: Abundance)</li></ul><br></li><li><p><b>Hydrology</b> (Worksheet Hydrology)</p><p>Dimensions: 60 rows by 17 columns</p><p>Description: River characteristics and litter quantities</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project riparian site (Field type: Location)</li><li><b>Stream Code</b>: The stream from which the sample was taken (LFE, 15m, 30m, VJR or OP) (Field type: ID)</li><li><b>Transect No.</b>: The point of each sample within the 100m transect at each stream (Field type: ID)</li><li><b>Channel Width</b>: The bank full width of the channel at this point (Field type: Numeric)</li><li><b>Wetted Width</b>: The width of the runnin water at this point (Field type: Numeric)</li><li><b>SAFE Habitat Quality Right</b>: the SAFE Habitat quality on the right of the channel when looking upstream (Field type: Ordered Categorical)</li><li><b>SAFE Habitat Quality Centre</b>: the SAFE Habitat quality in the centre of the channel when looking upstream (Field type: Ordered Categorical)</li><li><b>SAFE Habitat Quality Left</b>: the SAFE Habitat quality on the left of the channel when looking upstream (Field type: Ordered Categorical)</li><li><b>Flow Rate Right (s)</b>: the time taken for a tennis ball to travel 10m in the water on the right of the channel when looking upstream (Field type: Numeric)</li><li><b>Flow Rate Centre (s)</b>: the time taken for a tennis ball to travel 10m in the water in the centre of the channel when looking upstream (Field type: Numeric)</li><li><b>Flow Rate Left (s)</b>: the time taken for a tennis ball to travel 10m in the water on the left of the channel when looking upstream (Field type: Numeric)</li><li><b>Average Flow Rate (s)</b>: an average of flow rate centre, flow rate left and flow rate right (Field type: Numeric)</li><li><b>Leaf Litter Retention (g)</b>: the dried mass of leaf litter retained across the wetted width of the stream at each point (Field type: Numeric)</li><li><b>Average Substrate Size</b>: the average size of the substrate across the channel width of the stream at each point (Field type: Numeric)</li><li><b>Leaf Litter Trap Position</b>: the position where the leaf litter trap was placed relative to the stream when looking upstream (left, right or centre) (Field type: Categorical)</li><li><b>Leaf Litter Mass</b>: the dried mass of leaf litter collected in the leaf litter trap at each point (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2017-02-06 to 2017-07-06</p><p><b>Latitudinal extent: </b>4.6314 to 4.7273</p><p><b>Longitudinal extent: </b>117.4556 to 117.6233</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Coleoptera<br> -  -  - Diptera<br> -  -  - Hymenoptera<br> -  - [Other.Insect]<br></div><p></p>
Input data from the paper: Small room for compromise between oil palm cultivation and primate conservation in Africa
<p>All the raw input data needed to replicate the analyses from the paper:</p> <p><strong>Strona G., S. D. Stringer, G. Vieilledent, Z. Szantoi, J. Garcia-Ulloa, S. Wich.</strong> Small room for compromise between oil palm cultivation and primate conservation in Africa.</p>
ConceptNet Vector Ensemble 16.04 input data
<p>This is the data required to build the paper "An Ensemble Method to Build High-Quality Word Embeddings", by Robyn Speer and Joshua Chin.</p> <p>The input data itself comes from:</p> <ul> <li> <p><a href="http://conceptnet5.media.mit.edu/">ConceptNet 5.4</a>, which contains data from Wiktionary, WordNet, and many contributors to Open Mind Common Sense projects, edited by Robyn Speer</p> </li> <li> <p><a href="http://nlp.stanford.edu/projects/glove/">GloVe</a>, by Jeffrey Pennington, Richard Socher, and Christopher Manning</p> </li> <li> <p><a href="https://code.google.com/archive/p/word2vec/">word2vec</a>, by Tomas Mikolov and Google Research</p> </li> <li> <p><a href="http://www.cis.upenn.edu/~ccb/ppdb/">PPDB</a>, by Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch</p> </li> </ul>
Input Data for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary information accompanying "Energy Storage Profit Risk under Stochastic Fuel Prices" paper submitted to <a href="https://www.journals.elsevier.com/energy-economics/">Energy Economics</a>.</p>
Input and output datasets for the testing of joint inversion code(s) and sensitivity analysis.
<p>Dataset associated to the paper: "Sensitivity of constrained joint inversions to geological and petrophysical input data uncertainties with posterior geological analysis" by Giraud J., Ogarko V., Pakyuz-Charrier E., Jessell M., Lindsay M., and Martin R. <br><br>This paper is under review for publication in Geophysical Journal International. The complete reference will be updated upon publication.</p><p>This dataset comprises:<br>- petrophysical model (x3)<br>- reference models for density contrast and magnetic susceptibility <br>- density contrast and magnetic susceptibility starting models (x3)<br>- inverted density contrast and magnetic susceptibility models (x3)<br>- three probabilistic geological models (x3)</p>
Input Data for "Distinguishing attributes using ConceptNet Numberbatch"
<p>In a post on blog.conceptnet.io, we're showing how to use ConceptNet Numberbatch alone to create a good solution to SemEval-2018 Task 10, Capturing Discriminative Attributes. This is an alternative, simplified implementation of a result presented in the SemEval paper <a href="http://aclweb.org/anthology/S18-1162">Distinguishing Attributes Using Text Corpora and Relational Knowledge</a>, by Robyn Speer and Joanna Lowry-Duda.</p> <p>This data repository contains the data necessary to make the simplified implementation work.</p>
Proteogenomics_input_files
<p>Proteogenomics tutorial input datasets</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.