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.
500
datasets available to search
ShareScore release 0.9.0
Dataset results
500 results for “Regular”
Telemarketer & Regular user CDR phone records
<p>Real-world CDR records gathered from Telemarketer PBX and mobile phone users.</p>
Dipole-dipole interaction data for all three regular polytopes
<p>One can find here the raw data employed for discussing the minimum energy for dipoles placed on the vertices of the three families of regular polytopes in any dimension d (up to d=7)</p>
Synthetic dataset for the testing of local conditioning of regularization function using geological uncertainty.
<p>This companion datasets relates to the model shown supplementary information to the manuscript "<strong>Integration of geological uncertainty into geophysical inversion by means of local gradient regularization</strong>", by J. Giraud, M. Lindsay, V. Ogarko, M. Jessell, R. Martin and E. Pakyuz-Charrier, submitted to Solid Earth. The archive contains the input and output geophysical data, starting and inverted models, probabilistic geological model and conditioning volume derived from the calculation of Shannon's entropy. </p> <p>This description will be updated with the accurate reference to the journal publication and acknowledgement prior to publication. </p>
Instance data: open-shop scheduling problems with any regular minsum objective
<p>Open shop scheduling instances used in the working paper</p> <ul> <li>Emde, S. & Lysgaard, J. (2021). Branch-cut-and-price for open-shop scheduling problems with any regular minsum objective.</li> </ul> <p>The instances in file <em>Brucker_instances.csv</em> are based on the test data for the classic [O||Cmax] problem from Brucker, P., Hurink, J., Jurisch, B., & Wöstmann, B. (1997). A branch & bound algorithm for the open-shop problem. <em>Discrete Applied Mathematics</em>, <em>76</em>(1-3), 43-59. They are enriched with release and due dates as well as machine-pair dependent transportation delays. The instances in file <em>random_instances.csv </em>are new.</p> <p>The files contain comma-separated values. Each line (except for the header) stands for one instance. The columns labels are:</p> <p>ID: label (identifier) of the instance</p> <p>n: number of jobs</p> <p>m: number of machines</p> <p>p: processing times; each square bracket stands for one machine, the values inside the brackets for the processing times of the jobs on the respective machine</p> <p>t: transfer times between machines; note that the dummy machine 0 where all jobs originate and end is the last machine, i.e., it has the highest index</p> <p>r: release dates for each job</p> <p>d: due dates for each job.</p> <p>Mj: set of machines on which the jobs must be processed; each square bracket stands for one job, values inside the brackets for machine indices. Note that indices are zero-based.</p> <p>In instance set <em>Brucker_instances.csv</em>, every machine is visited by every job, therefore there is no column Mj. Conversely, due dates are immaterial for the random instances because of their quadratic completion time objective. Hence, they are omitted from the table.</p>
Data Set: Hyperspectral image unmixing with LiDAR data-aided spatial regularization
<p>Data set and matlab codes used for the experimental section of "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization"</p> <p>T. Uezato, M. Fauvel and N. Dobigeon, "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization," in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 56, no. 7, pp. 4098-4108, July 2018.<br> doi: 10.1109/TGRS.2018.2823419<br> URL: <a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475">http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475</a><br> </p>
Artefact for Kaki: Concurrent Update Synthesis for Regular Policies via Petri Games
<p>This is the artefact for the paper "Kaki: Concurrent Update Synthesis for Regular Policies via Petri Games".</p> <p>The files in this package aim to assist you in running Kaki and generating the results presented in the paper. The details on running Kaki, generating the problems and reproducing the results can be read in the README.md file.</p>
Regular rotation and low turbulence in a diverse sample of z~4.5 galaxies observed with ALMA
<p>Clean [CII] line-emission datacubes, parameter files and outputs of 3DBarolo.</p>
Fig. 1. Dentalium regulare, syntype NMSA 2575 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Scaphopoda and Cephalopoda
Fig. 1. Dentalium regulare, syntype NMSA 2575/T687, 25.2×4.0 mm.
Repositiry for the article: "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain Mbebi & Zoran Nikoloski
<p>This is the repository for the manuscript "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain J. Mbebi & Zoran Nikoloski.</p> <p><strong>Organisation</strong></p> <ol> <li>The folder Codes contains the following R scripts with the K-folds cross-validation option to learn the hyperparameters:</li> </ol> <ul> <li>Mixed_L1L21_GRN.R which computes L1L21-solution</li> <li>Mixed_L1L21G_GRN.R which computes L1L21G-solution</li> <li>Mixed_L2L21_GRN.R which computes L2L21-solution</li> <li>Mixed_L2L21G_GRN.R which computes L2L21G-solution</li> <li>L1L21_Dream5_Scerevisiae_example_run.R is an example run using the L1L21-solution with S. cerevisiae data (Network 4 in DREAM5 challenge) All files needed to successfully run "L1L21_Dream5_Scerevisiae_example_run" are locaded in the folder Codes.</li> </ul> <p>2. The folder Figures contains all figures in the manuscript.</p> <p>3. The folder Inferred-networks contains all network objects for each dataset and each inference methods in the comparative analysis.</p> <p><strong>Dependencies and required packages</strong></p> <p>The following packages are required for the contending approaches in the comparative analysis: "devtools", "foreach", "plyr", "glmnet" and "randomForest".</p> <p><strong>GENIE3</strong></p> <p>The GENIE3 package can be installed from: <a href="http://bioconductor.org/packages/release/bioc/html/GENIE3.html">http://bioconductor.org/packages/release/bioc/html/GENIE3.html</a></p> <p><strong>TIGRESS</strong></p> <p>The TIGRESS repository can be obtained from: <a href="https://github.com/jpvert/tigress">https://github.com/jpvert/tigress</a></p> <p><strong>ENNET</strong></p> <p>The ENNET repository can be obtained from: <a href="https://github.com/slawekj/ennet">https://github.com/slawekj/ennet</a></p> <p><strong>PLSNET</strong></p> <p>The Matlab source code of PLSNET can be obtained from: <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17</a></p> <p><strong>PORTIA</strong></p> <p>The PORTIA repository can be obtained from: <a href="https://github.com/AntoinePassemiers/PORTIA">https://github.com/AntoinePassemiers/PORTIA</a></p> <p><strong>D3GRN</strong></p> <p>The Matlab source code of D3GRN can be obtained from: <a href="https://github.com/chenxofhit/D3GRN">https://github.com/chenxofhit/D3GRN</a></p> <p><strong>Fused-LASSO</strong></p> <p>The fused-LASSO repository can be obtained from: <a href="https://github.com/omranian/inference-of-GRN-using-Fused-LASSO">https://github.com/omranian/inference-of-GRN-using-Fused-LASSO</a></p> <p><strong>ANOVerence</strong></p> <p>Because of some technical issues (e.g code's accessibility: <a href="http://www2.bio.ifi.lmu.de/%CB%9Ckueffner/anova.tar.gz">http://www2.bio.ifi.lmu.de/˜kueffner/anova.tar.gz</a>), we were not able to reproduce ANOVerence results and used the inferred network from DREAM5 challenge instead.</p> <p>4. Although the codes here were tested on Fedora 29 (Workstation Edition) using R (version 4.2.2), they can run under any Linux or Windows OS distributions, as long as all the required packages are compatible with the desired R version.</p>
scNCL transfers labels from scRNA-seq to scATAC-seq data with neighborhood contrastive regularization
<p>Data used in our manuscript.</p> <p>'scNCL_data' folder contains gene expression data (with protein) and gene activitiy data (with protein).</p> <p>'GLUE_data' folder contains gene expression data and raw chromatin accessibility data. </p> <p>'HFA-resampling' folder contains multiple independent sampling of HFA-subset dataset.</p> <p>'Accuray on HFA-subsets.xlsx' file contains accuray results of all compared methods on HFA-subsets datasets.</p>
Impact of Seed Cycling on Menstrual Regularity, Dysmenorrhea, and Premenstrual Symptoms
ClinicalTrials.gov study NCT07052500. IPD Sharing: NO. Countries: 1. Publications: 7.
Evaluation of Effectiveness and Tolerability of Tapentadol Hydrochloride in Subjects With Severe Pain Due to Osteoarthritis Taking Either WHO Step I or Step II Analgesics or no Regular Analgesic
ClinicalTrials.gov study NCT00983073. IPD Sharing: Not stated. Countries: 5. Publications: 1.
Custom Cutting Block Instrument vs Regular Instrumentation Total Knee Replacement (TKR)
ClinicalTrials.gov study NCT03416946. IPD Sharing: NO. Countries: 1. Publications: 1.
An Efficacy and Safety Study of Luspatercept (ACE-536) Versus Placebo in Adults Who Require Regular Red Blood Cell Transfusions Due to Beta (β) Thalassemia
ClinicalTrials.gov study NCT02604433. IPD Sharing: Not stated. Countries: 15. Publications: 7.
Pharmacokinetic and Glucodynamic Crossover Study of Subcutaneously (SC) Administered Insulin Lispro + Recombinant Human Hyaluronidase (rHuPH20) and Regular Human Insulin + rHuPH20 Compared to Insulin
ClinicalTrials.gov study NCT00862849. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study to Evaluate Efficacy and Safety of AG-348 in Not Regularly Transfused Adult Participants With Pyruvate Kinase Deficiency (PKD)
ClinicalTrials.gov study NCT03548220. IPD Sharing: NO. Countries: 16. Publications: 5.
Gender Differences in Switching From Smoking Regular Cigarettes to E-Cigarettes
ClinicalTrials.gov study NCT03856515. IPD Sharing: Not stated. Countries: 1. Publications: 1.
How Different Beta-2 Receptor Genotypes Affect an Asthmatic's Response to Regular Salmeterol Treatment
ClinicalTrials.gov study NCT00595361. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Liquid Meal Study With Insulin Lispro With/Without Recombinant Human Hyaluronidase PH20 (rHuPH20) and Regular Human Insulin With rHuPH20 to Compare Pharmacokinetics, Postprandial Glycemic Response, an
ClinicalTrials.gov study NCT00916357. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Regular Insulin vs Rapid Insulin Delivered by V-Go
ClinicalTrials.gov study NCT03495908. IPD Sharing: NO. Countries: 1. Publications: 1.
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.