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.
1,944
datasets available to search
ShareScore release 0.9.0
Dataset results
1,944 results for “Class I”
Compound activity classes from ChEMBL for machine learning analysis
<p>Ten activity classes are provided that were extracted from ChEMBL version 24 for machine learning studies. Compounds are given in SMILES representations. The following selection criteria were applied. Compounds were required to be tested in a direct binding assay against a single human protein with a ChEMBL assay confidence score of 9. In addition, K<sub>i</sub> measurements had to be available. If multiple K<sub>i</sub> values were available for a compound and did not fall within the same order of magnitude, the compound was not selected. Furthermore only compounds with (mean) pK<sub>i</sub> of at least 5 were considered. Moreover, activity classes had to contain at least 200 compounds belonging to at least 50 computationally determined analog series. The 10 deposited classes consist of 243 to 955 compounds and 57 to 216 analog series.</p>
Model weights for single-label, 5-class models from https://doi.org/10.1101/482877
<p>The set of molecules used for training is in "5cls_rmsaltol.csv". If you try to predict from an image of any of these molecules the model will almost certainly be correct because this was the training data. </p> <p>5cls_single_novalidation.model.h5 uses only the molecule with index 0 from 5cls_rmsaltol.csv.</p> <p>5cls_single_valset0.h5 and 5cls_single_valset1.h5 both used 20% of the data as validation set - the respective indexes of those validation sets are givent in 5cls_newskf0.csv and 5cls_newskf1.csv</p> <p> </p> <p> </p> <p> </p>
Promiscuous compounds with activity against different target classes
<p>Reported are 1063 promiscuous compounds with activity against multiple target classes identified in PubChem. The tab-separated file consists of four columns:</p> <p>- cid: compound identifier (CID) from PubChem</p> <p>- NonstereoAromaticSMILES: SMILES representation of a compound</p> <p>- Number_of_tested_targets: total number of targets a compound was tested against in PubChem</p> <p>- PD: promiscuity degree (PD) of a compound, defined as the number of targets it was found active against</p>
5-Class Burst C-VEP with Dry EEG
<p><strong>Participants</strong></p> <p>The experiment was conducted with twenty four healthy volunteers (4 women, mean age = 29.3 years, SD = 7.5), all students and staff at ISAE-SUPAERO. None of the participants reported any of the exclusion criteria (neurological antecedents, being under psychoactive medication at the time of the study) and had normal or corrected-to-normal vision. The study was approved by the ethics committee of the University of Toulouse (CER approval number 2023-749) and was carried in accordance with the declaration of Helsinki. Participants gave informed written consent prior to the experiment</p> <p><strong>Experimental Protocol</strong></p> <p>Participants were comfortably seated and instructed to read and sign the informed consent. EEG data were recorded using the dry 8-electrodes Enobio system at a sample rate of 500Hz to record the surface brain activity. The 8 electrodes were positioned over the occipital and parieto-occipital sites: PO7, O1, O2, O3, PO8, PO3, POz, PO4. EEG data and markers were synchronized during recording using Lab Streaming Layer. Once equipped with the dry 8-electrode EEG system, volunteers were asked to focus on five targets that were cued sequentially in a random order for 0.5s, followed by a 2.2s stimulation phase, before a 0.7s inter-trial period. The cue sequence for each trial was pseudo-random and different for each block. After each block, a pause was observed and subjects had to press the space bar to continue. The participants were presented with fifteen blocks of five trials for each of the three conditions (Gabor-based textures, Ricker-based textures or Plain stimuli). The stimuli were presented on the following LCD 24.5'' monitor: Iiyama Gold Phoenix G-Master GB2590HSU-B1, 1920x1080 pixels, 400 cd/m², and the refresh rate has been set to 60Hz.</p> <p><strong>Preprocessing and Lag Correction<br></strong></p> <p>No pre-processing was applied on these data, and they have been neither sliced or epoched, although it remains possible to do so with the help of the markers. However, note that the Enobio streaming system we used introduced a constant 80ms lag in its data. The data provided in the dataset has not been corrected as they we only present raw data without modifications. We advise users of this dataset to correct this by shifting the EEG data 80ms for precise timing.</p> <p><strong>Electrode Location and Naming</strong></p> <p>In the dataset files, electrodes are named EEG001, ..., EEG007, the generic naming of the electrodes with the Enobio system we used. We provide along the dataset files a .loc file to map these generic names to standard 10-20 naming scheme and their respective location.</p>
Physical features for small sample of data (1000 events per class)
<p>These repository contains physical features extracted from four classes - <br>1) Earthquake, 2) Explosions, 3) Noise, 4) Surface events. <br><br>Each waveform is of 40s (P-10, P+30). <br>tapered 10%<br>bandpass filtered (0.5 - 15 Hz)<br>normalized by max. </p>
Data and codes for "Habitat structural complexity increases age-class coexistence and population growth rate through relaxed cannibalism in medaka fish"
<p>The zip file contains readme files, as well as data and codes to reproduce results and figures from the paper.</p>
Replication package for "An Exploratory Study on Default Class Openness in Java and Kotlin"
<p>This dataset includes scripts, text files, and cached CSV/Parquet or raw TXT data files used to generate all analysis and results from the paper. A <strong>README.md</strong> file is included in <strong>replication-pkg.zip</strong> for details on using the scripts.</p> <p>If you only want to inspect the figures, you do not need a data ZIP.</p> <p>If you want to simply re-generate the figures without changes, download <strong>data-cached.zip</strong>. If you want to make any sort of change to the analyses, you will want to download <strong>data-csv.zip</strong> and/or <strong>data-raw.zip</strong>. The CSV files are converted versions of the raw TXT files.</p>
Mapping discrete forest age classes of Mediterranean pinelands since the pre-satellite era using historical orthoimage mosaics and machine learning.
<p>Data and sripts for Journal of Forestry Research submitted manuscript. Authors: Vicent Agustí Ribas Costa, Andrew Trlica, & Aitor Gastón González.</p> <p>This data and scripts are part of Vicent's PhD project at Universidad Politécnica de Madrid, supervised by Dr. Aitor. Contact Vicent for any queries at va.ribas@upm.es.</p> <p>The PNOA LiDAR and 1956 and 2021 orthophotos are openly available at the Centro de Descargas of the Instituto Geográfico Nacional (<a href="https://centrodedescargas.cnig.es/CentroDescargas/index.jsp">https://centrodedescargas.cnig.es/CentroDescargas/index.jsp</a>). The 1989 orthophoto is available under request at the Institut Cartogràfic i Geogràfic de les Illes Balears (<a href="https://www.caib.es/webgoib/institut-cartografic-i-geografic-de-les-illes-balears-icgib-">https://www.caib.es/webgoib/institut-cartografic-i-geografic-de-les-illes-balears-icgib-</a>).</p> <p>Forest inventory data is property of the landowners and managed by Terrapi World Ltd.</p>
Data from: Comparative genomic analysis of the pheromone receptor Class 1 family (V1R) reveals extreme complexity in mouse lemurs (genus, Microcebus) and a chromosomal hotspot across mammals
<p><span>Sensory gene families are of special interest, both for what they can tell us about molecular evolution, and for what they imply as mediators of social communication. The vomeronasal type-1 receptors (V1Rs) have often been hypothesized as playing a fundamental role in driving or maintaining species boundaries given their likely function as mediators of intraspecific mate choice, particularly in nocturnal mammals. Here, we employ a comparative genomic approach for revealing patterns of V1R evolution within primates, with a special focus on the small-bodied nocturnal mouse and dwarf lemurs of Madagascar (genera <i>Microcebus</i> and <i>Cheirogaleus</i>, respectively). By doubling the existing genomic resources for strepsirrhine primates (i.e., the lemurs and lorises), we find that the highly speciose and morphologically cryptic mouse lemurs have experienced an elaborate proliferation of V1Rs that we argue is functionally related to their capacity for rapid lineage diversification. Contrary to a previous study that found equivalent degrees of V1R diversity in diurnal and nocturnal lemurs, our study finds a strong correlation between nocturnality and V1R elaboration, with nocturnal lemurs showing elaborate V1R repertoires and diurnal lemurs showing less diverse repertoires. Recognized subfamilies among V1Rs show unique signatures of diversifying positive selection, </span>as might be expected if they have each evolved to respond to specific stimuli<span>. Further, a detailed syntenic comparison of mouse lemurs with mouse (genus <i>Mus</i>) and other mammalian outgroups shows that orthologous mammalian subfamilies, predicted to be of ancient origin, tend to cluster in a densely populated region across syntenic chromosomes that we refer to as a V1R "hotspot."</span></p>
Figure 43 in Atlas of Myriapod Biogeography. I. Indigenous Ordinal and Supra-Ordinal Distributions in the Diplopoda: Perspectives on Taxon Origins and Ages, and a Hypothesis on the Origin and Early Evolution of the Class
Figure 43. Distribution of Chordeumatida in Europe, North Africa, and the Middle East.
Figure 21 in Atlas of Myriapod Biogeography. I. Indigenous Ordinal and Supra-Ordinal Distributions in the Diplopoda: Perspectives on Taxon Origins and Ages, and a Hypothesis on the Origin and Early Evolution of the Class
Figure 21. Distribution of Polyzoniida in Europe and the Middle East.
Figure 18 in Atlas of Myriapod Biogeography. I. Indigenous Ordinal and Supra-Ordinal Distributions in the Diplopoda: Perspectives on Taxon Origins and Ages, and a Hypothesis on the Origin and Early Evolution of the Class
Figure 18. Distribution of Platydesmida in North America/the Western Hemisphere.
Figure 17 in Atlas of Myriapod Biogeography. I. Indigenous Ordinal and Supra-Ordinal Distributions in the Diplopoda: Perspectives on Taxon Origins and Ages, and a Hypothesis on the Origin and Early Evolution of the Class
Figure 17. Distribution of the Order Platydesmida.
Figure 26 in Atlas of Myriapod Biogeography. I. Indigenous Ordinal and Supra-Ordinal Distributions in the Diplopoda: Perspectives on Taxon Origins and Ages, and a Hypothesis on the Origin and Early Evolution of the Class
Figure 26. Distribution of the Order Julida.
Fig. 6. Map showing all the 50 in Atlas of European millipedes 3: Order Chordeumatida (Class Diplopoda)
Fig. 6. Map showing all the 50 km squares which provided records in this volume.
Raw Research Data Supporting "Generalizing hydrogel microparticles into a new class of bioinks for extrusion bioprinting"
<p>Raw research data supporting:</p> <p>Xin S. et al., 2021, Science Advances, Vol 7, Issue 42, abk3087</p>
BG, GR-GRC, TR-TUE. in Atlas of European millipedes 3: Order Chordeumatida (Class Diplopoda)
BG, GR-GRC, TR-TUE.
FR-FRA in Atlas of European millipedes 3: Order Chordeumatida (Class Diplopoda)
FR-FRA
ES-SPA in Atlas of European millipedes 3: Order Chordeumatida (Class Diplopoda)
ES-SPA
Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts.
<p>Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 7-band (RGB+NIR+SWIR+NDWI+MNDWI) images of coasts.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571</p> <p>Classes: {0=water, 1=whitewater, 2=sediment, 3=other}</p> <p>File descriptions</p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><br> References</p> <p>*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>** Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571</p> <p> </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.