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datasets available to search
ShareScore release 0.9.0
Dataset results
52 results for “Data Training Sets”
Data set for training a ML model to predict duration of MPI application phases (HPC system) - with previous phase info
<p>This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system.</p> <p>There are 10 different data sets corresponding to different HPC applications.</p> <p>These data sets contain information regarding the previous MPI call with same ID and type.</p>
Data set for training a ML model to predict duration of MPI application phases (HPC system) - without previous phases info
<p>This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system.</p> <p>There are 11 different data sets corresponding to different HPC applications.</p> <p>These data sets do not contain information regarding previous MPI calls</p>
Data set to train a natural language classifier able to differentiate between 15 topics relevant to biodiversity informatics
<p><strong>Scope and size</strong><br> This data set is used to train a natural language processing classifier. The classifier shall be able to differentiate between 15 topics relevant to biodiversity informatics. The list of relevant topics was adapted from Searls (2012).</p> <p>The data set was split into training data, testing data (for tweaking and unit-testing the classifier) and validation data. Each data set is stored as PDF files in a separate directory.</p> <ul> <li>Training data (5494 pages)</li> <li>Test data (977 pages)</li> <li>Validation data (215 pages)</li> </ul> <p><strong>Data sources and licenses</strong><br> Details about the licenses for each data set can be found in the corresponding directories.</p> <ul> <li>Training data was compiled from MIT OpenCourseWare resources provided by MIT under a Creative Commons BY-NC-SA License.</li> <li>Testing data was compiled from MIT OpenCourseWare exams, provided by MIT under a Creative Commons License BY-NC-SA.</li> <li>Validation data was compiled from Wikipedia, provided under a Creative Commons License by Wikipedia editors and contributors.</li> </ul> <p><strong>Topic references</strong><br> Each topic references one or more MIT OpenCourseWare courses:</p> <ul> <li>Algorithms (Demaine, and Devadas, 2011)</li> <li>Artificial Intelligence (Winston, 2010)</li> <li>Building Dynamic Websites (Abelson, and Greenspun, 2003)</li> <li>Computational Biology (Kellis, 2015)</li> <li>Computer Graphics (Matusik, and Durand, 2012)</li> <li>Computer Science and Programming (Bell, Grimson, and Guttag, 2016)</li> <li>Databases (Madden, Morris, Stonebraker, and Curino, 2010)</li> <li>Data Structures (Demaine, 2012)</li> <li>Digital Image Processing (Clifford, Fisher, Greenberg, and Wells, 2007; Golland, 2005)</li> <li>Machine Learning (Singh, Jaakkola, and Mohammad, 2006)</li> <li>Machine Structures (Morris, and Madden, 2009)</li> <li>Natural Language Processing (Berwick, 2003; Collins, and Barzilay, 2005)</li> <li>Parallel Computing (Edelman, 2011)</li> <li>Software Engineering (Jackson, and Devadas, 2005)</li> <li>Structure and Interpretation of Computer Programs (Miller, and Goldman, 2016).</li> </ul> <p><strong>References</strong></p> <p>Harold Abelson, and Philip Greenspun. 6.171 Software Engineering for Web Applications. Fall 2003. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Ana Bell, Eric Grimson, and John Guttag. 6.0001 Introduction to Computer Science and Programming in Python. Fall 2016. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Robert Berwick. 6.863J Natural Language and the Computer Representation of Knowledge. Spring 2003. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Gari Clifford, John Fisher, Julie Greenberg, and William Wells. HST.582J Biomedical Signal and Image Processing. Spring 2007. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Michael Collins, and Regina Barzilay. 6.864 Advanced Natural Language Processing. Fall 2005. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Erik Demaine. 6.851 Advanced Data Structures. Spring 2012. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Erik Demaine, and Srini Devadas. 6.006 Introduction to Algorithms. Fall 2011. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Alan Edelman. 18.337J Parallel Computing. Fall 2011. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Polina Golland. 6.881 Representation and Modeling for Image Analysis. Spring 2005. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Daniel Jackson, and Srini Devadas. 6.170 Laboratory in Software Engineering. Fall 2005. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Manolis Kellis. 6.047 Computational Biology. Fall 2015. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Robert Miller, and Max Goldman. 6.005 Software Construction. Spring 2016. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Samuel Madden, Robert Morris, Michael Stonebraker, and Carlo Curino. 6.830 Database Systems. Fall 2010. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Wojciech Matusik, and Frédo Durand. 6.837 Computer Graphics. Fall 2012. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Morris, Robert, and Madden, Samuel. 6.033 Computer System Engineering. Spring 2009. Massachusetts Institute of Technology: MIT OpenCourseWare, http://hdl.handle.net/1721.1/118791. License: Creative Commons BY-NC-SA.</p> <p>David B. Searls. An online bioinformatics curriculum. 2012. PLoS computational biology, 8(9), p.e1002632.</p> <p>Rohit Singh, Tommi Jaakkola, and Ali Mohammad. 6.867 Machine Learning. Fall 2006. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Patrick Winston. 6.034 Artificial Intelligence. Fall 2010. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p> </p>
Measuring the Quality of Surgical Care and Setting Benchmarks for Training Using Intuitive Data Recorder Technology
ClinicalTrials.gov study NCT04647188. IPD Sharing: NO. Countries: 1. Publications: 0.
E-Predict Training Data Set and Examples
GEO Series GSE2228. Viruses. 56 samples. Type: Expression profiling by array.
Acetaminophen (APAP) Rat Blood Training Gene Expression Data Set
GEO Series GSE5593. Rattus norvegicus. 68 samples. Type: Expression profiling by array.
Blood and skeletal muscle gene signatures of exercise training in men (PBMC data set)
GEO Series GSE111552. Homo sapiens. 32 samples. Type: Expression profiling by array.
Blood and skeletal muscle gene signatures of exercise training in men (Total blood data set G2)
GEO Series GSE111554. Homo sapiens. 40 samples. Type: Expression profiling by array.
Data set from Martins de Abreu R, Porta A, Rehder-Santos P, Cairo B, Donisete da Silva C, De Favari Signini É, Sakaguchi CA, Catai AM. Effects of inspiratory muscle-training intensity on cardiovascular control in amateur cyclists. Am J Physiol Regul Integr Comp Physiol. 2019 Dec 1;317(6):R891-R902. doi: 10.1152/ajpregu.00167.2019. Epub 2019 Oct 9. PMID: 31596110.
<p>Data set from Martins de Abreu R, Porta A, Rehder-Santos P, Cairo B, Donisete da Silva C, De Favari Signini É, Sakaguchi CA, Catai AM. Effects of inspiratory muscle-training intensity on cardiovascular control in amateur cyclists. Am J Physiol Regul Integr Comp Physiol. 2019 Dec 1;317(6):R891-R902. doi: 10.1152/ajpregu.00167.2019. Epub 2019 Oct 9. PMID: 31596110.</p> <p>This is the abstract:</p> <p>Chronic effects of inspiratory muscle training (IMT) on autonomic function and baroreflex regulation are poorly studied. This study aims at evaluating chronic effects of different IMT intensities on cardiovascular control in amateur cyclists. A longitudinal, randomized, controlled blind study was performed on 30 recreational male cyclists undergoing IMT for 11 wk. Participants were randomly allocated into sham-trained group (SHAM, <em>n</em> = 9), trained group at 60% of the maximal inspiratory pressure (MIP60, <em>n</em> = 10), and trained group at critical inspiratory pressure (CIP, <em>n</em> = 11). Electrocardiogram, finger arterial pressure, and respiratory movements were recorded before (PRE) and after (POST) training at rest in supine position (REST) and during active standing (STAND). From the beat-to-beat series of heart period (HP) and systolic arterial pressure (SAP), we computed time domain markers, frequency domain indexes in the low frequency (0.04-0.15 Hz) and high frequency (HF, 0.15-0.4 Hz) bands, an entropy-based complexity index (CI), and baroreflex markers estimated from spontaneous HP-SAP sequences. Compared with SHAM, the positive effect of MIP60 over the HP series led to the HF power increase during REST (PRE: 521.2 ± 447.5 ms<sup>2</sup>; POST: 1,161 ± 878.9 ms<sup>2</sup>) and the CI rise during STAND (PRE: 0.82 ± 0.18; POST: 0.97 ± 0.13). Conversely, the negative effect of CIP took the form of the decreased HP mean during STAND (PRE: 791 ± 71 ms; POST: 737 ± 95 ms). No effect of IMT was visible over SAP and baroreflex markers. These findings suggest that moderate-intensity IMT might be beneficial when the goal is to limit cardiac sympathetic hyperactivity at REST and/or in response to STAND.</p> <p><strong> </strong></p>
Blood and skeletal muscle gene signatures of exercise training in men (Muscle data set)
GEO Series GSE111551. Homo sapiens. 26 samples. Type: Expression profiling by array.
Blood and skeletal muscle gene signatures of exercise training in men (Total blood data set G1)
GEO Series GSE111553. Homo sapiens. 44 samples. Type: Expression profiling by array.
Data set from Current status of MSK radiology training: an international survey by the European Society of Musculoskeletal Radiology (ESSR) Young Club
<p>Data set from Current status of MSK radiology training: an international survey by the European Society of Musculoskeletal Radiology (ESSR) Young Club</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.