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.
54
datasets available to search
ShareScore release 0.9.0
Dataset results
54 results for “NPI”
Hard Rod Fluid Analytical Solution in .npy (For Operator learning)
<p>Hard rod fluid with diameter of 1.0 between 2 walls with distance L+2d=10.0,<br>under different chemical potential (mu) and external potential (V_ext(x)), with a sampling distance dz=0.1.</p> <p>Data are stored as numpy structured ndarrays, each npy file store one type of data. <br>The ndarrays has the shape of (n_sample,n_grid), with 4 fields:<br>the position "z", <br>the density profile "rho", <br>the local chemical potential "muloc"<br>the one-body direct correlation function "c1".<br>Where muloc(z)=mu-V_ext(z)</p> <p>In Operator Learning, I've tried the mapping of rho to c1 as well as muloc to rho.</p> <p>dataload.py contains some functions used for loading data<br>data_model_train.py contains the read-in function for model-training data.</p> <p>data_4groups.npy saves 8000 data, with 2000 in each group, generated from the following external potentials:<br>group 1: V_ext=0 <br>group 2: V_binary[z] = -epsilon*((a/(z+a/2))^3+(a/(L+a/2-z))^3)<br>group 3: V_binary[z] = -mg*z<br>group 4: V_binary[z] = -u0*(abs(z/z0))^a0<br>with mu, epsilon, a, mg, u0, z0, a0 are adjustable constants of the potential </p> <p>data_mixed23.npy saves 2000 data, generated from the following external potentials:<br> V_binary[z] = -epsilon*((a/(z+a/2))^3+(a/(L+a/2-z))^3) -mg*z</p> <p>Folders are taped in .zip files.</p> <p>Folder "~/R_data_generator" stores the generator of the data, analytical solution of 1D hard rod fluid.<br>Folder "~/Rdata" contains same data in .Rdata file, as R matrixs<br>Folder "~/R2py" contains the script to generate .npy file from .Rdata file</p>
Dataset: Experimental vignette-based survey data on NPI acceptance during travel (SNSF NRP 78)
<p>The data set contains rating scales on the willingness of the Swiss resident population to take risks in connection with touristic travel during the coronavirus pandemic. The data includes a selection of items of the Domain-Specific Risk-Taking Scale (DOSPERT) (Weber et al., 2002), the health belief model (HBM; Rosenstock, 1974, see also Champion & Skinner, 2008), and the theory of planned behaviour (Ajzen, 1991) to predict tourists’ intentions to travel under implementation of specific NPIs and the vaccination passport. The dataset contains data of N = 2’018 participants that have been collected between March 29 and April 9, 2021, based on a representative quota sampling (language region, age, gender). The data were collected based on a computer assisted web interview (CAWI) of a leading Swiss market research company. The following travel-related protective measures were tested regarding the constructs of the above-mentioned theories:</p> <ul> <li>Vaccination passport</li> <li>Surgical masks</li> <li>Travel warnings</li> <li>Rapid Testing at points of entry</li> <li>FFP2 masks</li> <li>PCR tests taken 72h before travel</li> <li>10-day quarantine of returning travelers from high-risk areas</li> <li>14-day quarantine of inbound travelers</li> </ul> <p>Ajzen, I. (1991). The theory of planned behavior. <em>Organizational Behavior and Human Decision Processes, 50</em>, 179-211. <a href="https://doi.org/10.1016/0749-5978(91)90020-T">https://doi.org/10.1016/0749-5978(91)90020-T</a></p> <p>Rosenstock, I. M., (1974). The health belief model and preventive health behavior. <em>Health Education Monographs,</em> <em>2</em>, 354-386. <a href="https://doi.org/10.1177/109019817400200405">https://doi.org/10.1177/109019817400200405</a></p> <p>Weber, E. U., Blais, A.-R., & Betz, N. E. (2002). A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. <em>Journal of Behavior Decision Making, 15</em>, 263-290. <a href="https://doi.org/10.1002/bdm.414">https://doi.org/10.1002/bdm.414</a></p> <p>Champion, V. L., & Skinner, C. S. (2008). The health belief model. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), <em>Health Behavior and Health Education. Theory, Research, and Practice</em> (4 ed, pp. 45-65). San Francisco, CA: Jossey-Bass.</p>
Phase 2 Clinical Trial of NPI-0052 in Patients With Relapsed or Relapsed/Refractory Multiple Myeloma
ClinicalTrials.gov study NCT00461045. IPD Sharing: YES. Countries: 1. Publications: 28.
data for hackathon geoscience 7 facies model npy format
<p>This dataset is an extraction of zenodo deposit: DOI: 10.5281/zenodo.7244958</p> <p>Dataset will be used for an Hackathon </p>
REMIND SSP2-NPi ei38 unfold datapackage
Open the record for dataset details and reuse information.
RNA-sequencing of SVF-derived preadipocytes and adipocytes from inguinal white adipose tissue of male mice treated with vehicle, CGRPa, NPY, NE, or SP
GEO Series GSE293584. Mus musculus. 21 samples. Type: Expression profiling by high throughput sequencing.
Chronic stress and its effects on behavior, RNA expression of the bed nucleus of the stria terminalis, and the M-current of NPY neurons
GEO Series GSE235108. Mus musculus. 38 samples. Type: Expression profiling by high throughput sequencing.
Sucralose Promote Food Intake Through NPY and A Neuronal Fasting Response
GEO Series GSE73179. Drosophila melanogaster. 2 samples. Type: Expression profiling by high throughput sequencing.
EB-mediated NPY expression and release
GEO Series GSE60013. Rattus norvegicus. 8 samples. Type: Expression profiling by array.
Transcriptomically-guided pharmacological experiments in neocortical and hippocampal NPY-positive GABAergic interneurons
GEO Series GSE193293. Mus musculus. 47 samples. Type: Expression profiling by high throughput sequencing.
3.9.1 cutoff remind SSP2-NPi
unfold data package
3.10 cutoff remind SSP1-NPi
unfold data package
3.9.1 cutoff remind SSP1-NPi
unfold data package
3.10 cutoff remind SSP5-NPi
unfold data package
3.10 cutoff remind SSP2-NPi
unfold data package
3.9.1 cutoff remind SSP5-NPi
unfold data package
3.9.1 consequential remind SSP5-NPi
unfold data package
3.9.1 consequential remind SSP1-NPi
unfold data package
3.9.1 consequential remind SSP2-NPi
unfold data package
3.10 consequential remind SSP5-NPi
unfold data package
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.