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”
Modelling input for cooking costs
<p>This dataset is meant to provide a concise way of representing all the important associated costs for the clean cooking fuels being considered during the modelling.</p>
FVCOM_Petermann_Discharge_Input_Files
<h2>Overview</h2> <p>This repository contains all the input files that are required to run the subglacial discharge experiments for the Petermann Ice Shelf-fjord system using the Finite Volume Community Ocean Model (FVCOM). You can access the corresponding peer-reviewed, open-access research article here: <a href="https://doi.org/10.1038/s41467-025-59469-9" target="_blank" rel="noopener">Enhanced subglacial discharge amplifies Petermann Ice Shelf melting when ocean thermal forcing saturates</a></p> <h2>Pre-requisites</h2> <p>The open source code Finite Volume Community Ocean Model version 4.0 (FVCOM v4.0) augmented by an ice shelf (Zhou and Hattermann, 2020) and sea ice module (Prakash et al., 2022) is required to conduct the numerical experiments, and is made publicly available at <a href="https://doi.org/10.5281/zenodo.15084570" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.15084570</a>. A detailed description of our high-resolution (200 m) unstructured grid 3-D regional model setup centered on the Petermann ice shelf and fjord is available from Prakash et al. (2022) and Prakash et al. (2023).</p> <h2>File description</h2> <p>The model input files can be classified under 2 broad categories, namely </p> <ul> <li>static, and </li> <li>time-varying.</li> </ul> <p>Static : These include the following files</p> <ol> <li><em>zisf.dat</em> (realistic Petermann ice shelf draft derived from BedMachine v3 (Morlighem et al., 2017)), </li> <li><em>dep.dat</em> (smoothed bathymetry, which has been modified to provide an improved (with respect to BedMachine v3 (Morlighem et al., 2017)) representation of the sub-ice shelf topography), </li> <li><em>cor.dat</em> (Coriolis),</li> <li><em>sigma.dat </em>(terrain-following vertical coordinate system),</li> <li><em>grd.dat</em> (FVCOM Petermann Fjord regional model grid)</li> </ol> <p>Time-varying: These include the following files</p> <p>(a) Atmospheric Forcing</p> <ol> <li><em>ocnice_YYYY_wnd.nc</em> (wind forcing), </li> <li><em>ocnice_YYYY_fwf.nc</em> (freshwater flux), and</li> <li><em>ocnice_YYYY_hf.nc</em> (heat flux), </li> </ol> <p>where YYYY represents the forcing year, e.g. 2014.</p> <p>(b) Sea ice forcing</p> <ol> <li><em>ocnice_YYYY_icenudge.nc</em> (includes sea ice concentration and thickness, bulk ice salinity, and sea ice velocities),</li> </ol> <p>where YYYY represents the forcing year, e.g. 2014.</p> <p>(c) Ocean boundary conditions</p> <ol> <li><em>pf_l_bcorr_climaYY.nc</em> (bias-corrected lateral ocean boundary conditions which includes temperature, salinity, ocean velocities, and sea surface elevation)</li> </ol> <p>where YY represents the forcing year, e.g. 14 (for 2014).</p> <p>In addition to these two broad categories, a restart file (<em>*_restart_*.nc</em>) is included in each run directory, which is used to provide "realistic" (a.k.a. "hotstart") initial ocean conditions. The restart file in the directory <em>control_run_2014</em> includes a "realistic" initial (July 01, 2014 00:00:00 UTC) ocean condition derived from a 4-km pan-Arctic (A4) ROMS model (Hattermann et al., 2016). Thereafter, the FVCOM model solution on January 01, 2015 00:00:00 UTC is used to "hotstart" the 2015 calendar year. Lastly, the stable FVCOM model solution from January 01, 2016 00:00:00 UTC is used to "hotstart" the 2016 calendar year, as well as the discharge experiments. Note that the discharge experiments depart from the control experiment (<em>control_run_2016</em>) by introducing subglacial discharge of varying magnitudes. All other conditions are kept the same, in order to isolate and investigate the impact of subglacial discharge on the basal melting of the Petermann ice shelf. The namelist file (*.nml) for each run is also included in the corresponding directory.</p> <p>To minimize storage space, each run directory is populated with only the input files that are unique to that run. For e.g., <em>control_run_2015/2016</em> and the <em>discharge_experiments</em> do not include the static files from <em>control_run_2014</em>, however, they are required to conduct each of these runs. Likewise, <em>discharge_experiments</em> do not include the time-varying forcing files, since they are identical to the ones contained in <em>control_run_2016</em>. The <em>discharge_experiments</em> do however include the discharge forcings for the present, RCP 8.5, and median(present,RCP 8.5) scenarios. These are the riverdata (riverdata.nc (present), riverdata_expC.nc (median), and riverdata_expD.nc (RCP 8.5)) and the corresponding rivernamelist (riverNamelist.nml, riverNamelist_expC.nml, riverNamelist_expD.nml) files. Note that a rivernamelist is different from a namelist file, in that it only contains information about the subglacial discharge.</p> <p>Development of the smoothed model topography datasets for our unstructured grid (horizontal resolutions of 200 m, 2km, and 4km) regional model domain, including localized improvements made within the Petermann Fjord; the ocean nesting and surface forcing methodologies (models used, downscaling procedure etc.); bias-correction, among other technical details, are detailed in Prakash et al. (2022) and Prakash et al. (2023).</p>
Glaide.jl input data for the Aletsch setup
<p>This repository provides the source data one cannot automatically download needed to generate the input data for the Aletsch glacier setup in the Glaide.jl model.</p> <p>The repository contains following 4 datasets:</p> <table> <tbody> <tr> <td> <p><strong>aletsch_fix.dat</strong></p> </td> <td> <p>The surface mass balance (SMB) data is provided in the form of annual mass balance per elevation band. The data set covers time period from 1914 to 2022. We extract and use the SMB data for 2016-2017 hydrological year.</p> <p><em>Source: GLAMOS (2023). Swiss Glacier Mass Balance, release 2023, Glacier Monitoring Switzerland, <a href="https://doi.org/10.18750/massbalance.2023.r2023" target="_blank" rel="noopener">https://doi.org/10.18750/massbalance.2023.r2023</a>.</em></p> </td> </tr> <tr> <td> <p><strong>aletsch2009.asc</strong></p> <p><strong>aletsch2017.asc</strong></p> </td> <td> <p>The surface elevation dataset from <a href="https://www.swisstopo.admin.ch/en/height-model-swissalti3d" target="_blank" rel="noopener">swissALTI3D</a> (swisstopo) provides data to replace the missing points in the bedrock dataset for the years 2009 and 2017. </p> <p><em>Source: swissALTI3D - Das hoch aufgelöste Terrainmodell der Schweiz (2022), Swiss Federal Office of Topography swisstopo, <a href="https://backend.swisstopo.admin.ch/fileservice/sdweb-docs-prod-swisstopoch-files/files/2023/11/14/6d40e558-c3df-483a-bd88-99ab93b88f16.pdf" target="_blank" rel="noopener">https://backend.swisstopo.admin.ch/fileservice/sdweb-docs-prod-swisstopoch-files/files/2023/11/14/6d40e558-c3df-483a-bd88-99ab93b88f16.pdf</a>.</em></p> </td> </tr> <tr> <td> <p><strong>ALPES_wFLAG_wKT_ANNUALv2016-2021.nc</strong></p> </td> <td> <p>Annual glacier surface flow velocity product from Sentinel-2 data for the European Alps.</p> <p><em>Source: Rabatel, A., Ducasse, E., Millan, R., Mouginot, J. (2023). Annual glacier surface flow velocity product from Sentinel-2 data for the European Alps, <a href="https://doi.org/10.57745/XHQ7TL" target="_blank" rel="noopener">https://doi.org/10.57745/XHQ7TL</a>, Recherche Data Gouv, V1.</em></p> </td> </tr> </tbody> </table> <p> </p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor
<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 15. Input Sources of an Affective Neuro-Symbol Representing an Emotion
<p>Based on the descriptions given above and the concept of neuro-symbolic information<br> processing outlined in Section 4.2, so-called “affective neuro-symbols” were defined for the<br> affective situation assessment architecture (see Figure 15). These affective neuro-symbols can<br> principally receive information from four different sources: (1) body states, (2) objects and events<br> perceived in the environment (external perception), (3) from other emotions and (4) cognitive<br> (reasoning) processes. An input from one of these sources can in certain circumstances already be<br> sufficient to activate an affective neuro-symbol. Different sources can either have an exhibitory or<br> inhibitory effect on the activation of an affective neuro-symbol.</p>
Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for<br> training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate<br> count, yeast & mould count, and spore count were taken as input parameters, and sensory score as<br> output parameter for developing TDNN single and multilayer models (Fig.1).</p>
Figure1. Static stimulation of a single TCR- The kinetic proofreading by the receptor on input x Є X forwards the receptor position p toward l. The receptor will generate negative feedback if p > β. The receptor will generate success signal when p== l.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
Figure 7. Adding an Acting Module, its configuration values and its input connections-Designing a Growing Functional Modules "Artificial Brain"
<p>The fourth step consists of adding an Acting Module, its configuration values and input<br> connection as shown in figure 7. A type “CI” is assigned because it functionality will consist of<br> triggering a steering command in accordance with the perception from the Sensing Module and in<br> order to satisfy the input request from the Global Goal. Consequently, the feedback is set to “1 18”<br> where “1” is the reference to the Sensation “free” and “18” to the perception in output of the<br> Sensing Module. The identifier “18” for this perception is computed as at the total number of<br> Sensation plus one (first sensing module). Identifiers and their references are automatically updated<br> when a Sensation is added or deleted.</p>
Figure 8. Knowledge input dimension of microlearning-Micro Learning: A Modernized Education System
<p>Figure 8 portrays the research directing the studies on the knowledge input dimension of microlearning. Almost 81% of the respondents believed that microlearning is the best learning system for integrated mashup PLE, followed by 66% of the respondents whose opinion was that dynamic applications of microlearning enhances knowledge. Almost 72% of the respondents believed that microlearning is suitable for diverse subjects, whereas 67% of the respondents believed that the use of digital artifacts maximizes the process of aggregation.</p>
BRAIN Journal-A Repeated Signal Difference for Recognising Patterns-Figure 3. Binary input stimulus dataset
<p>If the input value is 1, then the neuron is likely to be part of the cohesive set and would update the weight and also the global and local counts each event. If the input value is 0, then the neuron is not likely to be part of the cohesive set. For this case, it would not update the weight value as it is 0. It does update the global count as that uses unit increments, but only updates the local count when an earlier trigger switch tells it to. Each update event is also counted, so that averaged totals can be produced. </p>
Input data of MaTrace Global model
<p>Complete set of model data for the MaTrace Global model of metal cycles, published in Resources Conservation and Recycling with the DOI 10.1016/j.resconrec.2016.09.029</p>
Hydro-geo-chemo-mechanical facies modelling input
<p>Summary information of the geomechanical facies for the EU gas shale basins</p>
Inputs for the publication "A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs"
<p>This file contains the main inputs for the publication "A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs".</p>
SUEWS Sample Input Dataset
<p>Sample input dataset for SUEWS model v2018b</p>
Annex A to the technical report on the raw primary commodity (RPC) model - Input data
<p><strong>The raw primary commodity model</strong>:</p> <p>Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs.</p> <p><strong>Annex A to the technical report on the raw primary commodity model:</strong></p> <p>Annex A is an excel file which presents input data tables used by the RPC model. The annex contains the following tables:</p> <p>Table A.1 (Survey table) - An overview of the food consumption surveys incorporated in the RPC model</p> <p>Table A.2 (FoodEx table) - An outline of the food classification system used in the RPC model (EFSA's FoodEx classification system with additional codes)</p> <p>Table A.3 (Probability table) - Manages foods coded at food group level (example, breakfast cereals)</p> <p>Table A.4 (Disaggregation table) - Disassembles composite foods into their single components (RPC derivatives and/or RPCs)</p> <p>Table A.5 (Conversion table) - Converts amounts of RPC derivatives into corresponding amounts of RPC</p> <p>Table A.6 (Component table) - Overview of the search strings used for the probability analysis of components</p>
Hacat H3K4me3, H3K27me3 and Input bed files
<p>Cite </p> <p>https://academic.oup.com/nar/article/41/5/2846/2414471</p> <p>Aligned with: hisat2 --threads 24 -x /mnt/cargo/genomes/hisat2/hg38/genome --no-spliced-alignment -k 1 --no-discordant --no-mixed -U Satrom-chIP-05-Input_TGACCA_L002_R1_001.fastq</p> <p> </p>
Illustrative Darwin core archive to input data on a citizen science platform from a collection management system
<p>Illustrative DwC archive to send data from a collection management system to a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG.</p> <p>Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Results of TIMES model & inputs and outputs of the multicriteria and portfolio analysis
<p>These datasets contain the underlying data for the following publication: <strong>Energy efficiency promotion in Greece in light of risk: Evaluating policies as portfolio assets, Energy, https://doi.org/10.1016/j.energy.2018.12.180</strong></p>
Cellulose test input dataset for simulations with Galaxy and BRIDGE
<p>This coordinate and protein structure file dataset is that of cellulase and octaose substrate <em>in vacuo</em>. It has been derived from the <a href="https://www.rcsb.org/structure/7cel">7CEL PDB</a> structure of a fungal cellobiohydrolase. </p> <p>The original enzyme has been modified to revert the mutation at position 217 and to include disulfide bonds. The octaose substrate is an oligosaccharide consisting of 8 beta 1-4 linked glucose monomers. </p> <p>The files includes are: </p> <ul> <li><strong>cbh1test.crd</strong>: the coordinates of the entire system (protein and substrate) in CHARMM coordinate format. </li> <li><strong>cbh1test.psf</strong>: the CHARMM protein structure file which contains lists of molecular information including atomic masses, all bond pairs, angle triples and so on.</li> </ul>
hscScore github repository input files
<p><strong>Summary:</strong> Data and annotation files for input to the hscScore github repository https://github.com/fionahamey/hscScore</p> <p><strong>File descriptions</strong><strong>:</strong></p> <ul> <li>ensembl_gene_table_81.txt - Table downloaded from http://jul2015.archive.ensembl.org/index.html with gene name, gene ID and gene type information for mouse genes</li> <li>gene_conversion_10x.csv - Table mapping gene name to ensembl ID for 10x genomics single-cell RNA-sequencing data</li> <li>nestorowa_htseq_counts_all_cells_gene_names_renamed.txt - Gene expression counts for cells from Nestorowa et al (2016) (doi: https://doi.org/10.1182/blood-2016-05-716480)</li> <li>table_S3_model_selection_cv_scores.txt - Results of GridSearchCV for https://github.com/fionahamey/hscScore/blob/master/hsc_score_parameter_search.py</li> <li>wilson_HTSEQ_results.txt - HTSeq count results for data from Wilson et al (2015) (https://doi.org/10.1016/j.stem.2015.04.004)</li> <li>wilson_nomo_molo_genes.csv - List of MolO and NoMO genes from Wilson et al (2015) (https://doi.org/10.1016/j.stem.2015.04.004)</li> <li>wilson_rna_seq_hsc_scores.csv - MolO score prediction from Wilson et al (2015) (https://doi.org/10.1016/j.stem.2015.04.004) using random forest classifier</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.