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.

1,356

datasets available to search

ShareScore release 0.9.0

Dataset results

1,356 results for “Human Activities”

Learn how ShareScore rates datasets ↗

zenodo52/100

Human Kino-Dynamic Measurements Dataset for Factory-like Activities

This dataset was created as a part of the study presented in IEEE Transactions on Human-Machine Systems with the title "An Online Multi-Index Approach to Human Ergonomics Assessment in the Workplace" by Marta Lorenzini, Wansoo Kim and Arash Ajoudani. This paper introduces an online approach to monitor kinematic and dynamic quantities on the workers, providing on the spot an estimate of the physical load required in their daily jobs. A set of ergonomic indexes is defined to account for multiple potential contributors to work-related musculoskeletal disorders (WMSDs), which remain one of the major occupational safety and health problems in the European Union nowadays. Thus, the continuous tracking of workers’ exposure to the factors that may contribute to their development is paramount. To evaluate the proposed framework, a throughout experimental analysis was conducted. Twelve healthy adult subjects were recruited in the experimental study to perform, in the laboratory settings, occupational activities that are commonly carried out by workers in the current industrial scenario. Three tasks were selected to encompass the most significant risk factors in the workplace: mechanical overloading of the body joints, variable and high-intensity interaction forces, and repetitive and monotonous movements. Accordingly, lifting/lowering of a heavy object, drilling, and painting with a lightweight tool were considered, respectively, in this study. While the subjects were carrying out such activities, the data regarding the whole-body motion and the forces exchanged with the environment (both ground reaction force (GRF) and interaction forces at the end-effector) were collected. In addition, ten surface electromyography (sEMG) sensors were placed on the body of each subject to measure muscle activity as a reference to the effective physical effort required for the tasks. The whole experimental procedure was carried out in accordance with the Declaration of Helsinki and the protocol was approved by the ethics committee azienda sanitaria locale (ASL) Genovese N.3 (Protocol IIT_HRII_ERGOLEAN 156/2020).

opencc-by-4.0Oct 2021View details →

zenodo48/100

Inactive to active transition of human Thymidine Kinase 1 revealed by Molecular Dynamics simulations

The trajectories and input files for the manuscript Inactive to active transition of human Thymidine Kinase 1 revealed by Molecular Dynamics simulations (<a href="https://doi.org/10.1021/acs.jcim.1c01157">https://doi.org/10.1021/acs.jcim.1c01157</a>)  ABSTRACT Despite its importance for the nucleoside (and nucleoside prodrug) metabolism, the structure of the active conformation of human Thymidine Kinase 1 (hTK1) remains elusive. We perform microsecond molecular dynamics simulations of the inactive enzyme form bound to a bisubstrate inhibitor that was shown experimentally to activate another TK1-like kinase, Thermotoga maritima TK (TmTK). Our results are in excellent agreement with the experimental findings for the TmTK closed-to-open state transition. We show that the inhibitor induces an increase of the enzyme radius of gyration due to the expansion on one of the dimer interfaces; the structural changes observed, including the active site pocket volume increase, decrease in monomer-monomer buried surface area and of the number of hydrogen bonds (as compared to the inactive enzyme control simulation), show that the catalytically competent (open) conformation of hTK1 can be assumed in the presence of an activating ligand.

opencc-by-4.0Dec 2021View details →

zenodo48/100

Dataset Activation of Lactate Receptor HCAR1 Down-modulates Neuronal Activity in Rodent and Human Brain Tissue

This dataset is related to the study:  Briquet M, Rocher AB, Alessandri M, Rosenberg N, de Castro Abrantes H, Wellbourne-Wood J, Schmuziger C, Ginet V, Puyal J, Pralong E, Daniel RT, Offermanns S, Chatton JY. Activation of lactate receptor HCAR1 down-modulates neuronal activity in rodent and human brain tissue. J Cereb Blood Flow Metab. 2022 Mar 3:271678X221080324. doi: 10.1177/0271678X221080324. Epub ahead of print. PMID: 35240875.

opencc-by-4.0Mar 2022View details →

zenodo48/100

PALEODEM/ What burned the forest? Wildfires, climate change and human activity during the Mesolithic – Neolithic transition in SE Iberian Peninsula

This repository contains new XRD data from the Villena paleolake, archaeological radiocarbon evidence from the Villena area and the R code used to produce Summed Probability distribution analyses.   They correspond to the following reference:   Sánchez-García, C., Revelles, J., Burjachs, F., Euba, I., Expósito, I., Ibáñez, J., Schulte, L., Fernández-López de Pablo, J. What burned the forest? Wildfires, climate change and human activity during the Mesolithic – Neolithic transition in SE Iberian Peninsula (submitted to Catena).  We specify the content of file further down: <ul> <li>Vinalopo.csv: the list of radiocarbon dates from Villena spanning ca.9500-5500 cal BP from the following sites: Arenal de la Virgen, Cueva del Lagrimal and Casa Corona. </li> <li>ngrip.csv: NGRIP GICC05 paleotemperature record based on oxygen isotope series from Rasmussen SO et al.2006 A new Greenland ice core chronology for the last glacial termination. J. Geophys. Res. Atmos.111. (doi:10.1029/2005JD006079) and Andersen KK et al.2006 The Greenland Ice Core Chronology 2005, 15–42ka. Part 1: constructing the time scale. Quat. Sci. Rev.25, 3246–3257.</li> <li>Char.csv:  Sedimentary charcoal data set from the Villena Paleolake (VL3 core) published by Jones, S.E., Burjachs, F., Fernández-López de Pablo (2018) DOI/10.5281/zenodo.1244003, according to the new Bacon chronological model of the Villena paleolake (Fernández-López de Pablo et al., 2022 . Impacts of Early Holocene environmental dynamics on open-air occupation patterns in the Western Mediterranean: insights from El Arenal de la Virgen (Alicante, Spain). <a href="https://doi.org/10.31235/osf.io/5yqsr">https://doi.org/10.31235/osf.io/5yqsr</a>)</li> <li>SPD_analysis.R: R script with the code to reproduce the SPD analysis presented in the manuscript. </li> <li>SupplMat1xlsl: an excel file This file is composed by 8 spreadsheets:</li> </ul> <ol> <li>‘Selected variables 12.6-5.5’: all the data included in the time frame 12600-5500 cal BP, interpolated to 50 yr time windows. These data have been used for the Spearmans’rs correlation analysis (see spreadsheet ‘Spearmans’rs 12.6-5.5’ to track the results), Detrended Correspondence Analysis (see spreadsheet ‘Figure 5_DCA 12.6-5.5’ to track the results) and have been plotted in Figure 3 and 7. </li> <li>'Selected variables 9.1-5.5’: data included in the analysis focused on the time period 9.1-5.5 cal BP, interpolated to 50 yr time windows. These data have been used for the Spearmans’rs correlation analysis (see spreadsheet ‘Spearmans’rs 9.1-5.5’ to track the results), Detrended Correspondence Analysis (see spreadsheet ‘Figure 6_DCA 9.1-5.5’ to track the results) and have been plotted in Figure 8.</li> <li>‘Spearmans’rs 12.6-5.5’: Spearmans’rs correlation analysis applied to the 12600-5500 cal BP dataset (data from ‘Selected variables 12.6-5.5’).</li> <li>‘Spearmans’rs 9.1-5.5 cal BP’ Spearmans’rs correlation analysis applied to the 9100-5500 cal BP dataset, including here high-resolution XRD data (data from ‘Selected variables 9.1-5.5’).</li> <li>‘Figure 2 charcoal results’: original sedimentary charcoal results provided in this work. Data plotted in Figure 2. </li> <li>‘Figure 4 XRD results’: original XRD results provided in this work. Data plotted in Figure 4.</li> <li>‘Figure 5 DCA 12.6-5.5’: results of Detrended Correspondence analysis focused on the time period from 12600 to 5500 cal BP. Data plotted in Figure 5.</li> <li>‘Figure 6 DCA 9.1-5.5’ results of Detrended Correspondence analysis focused on the time period from 9100 to 5500 cal BP, including here high-resolution XRD data. Data plotted in Figure 6.</li> </ol>

opencc-by-4.0Nov 2022View details →

zenodo48/100

Non-invasive modulation of human corticostriatal activity [Dataset]

This dataset contains resting-state functional MRI data used in the study "Non-invasive modulation of human corticostriatal activity" (Caballero-Insaurriaga et al, PNAS, 2023). In this study two datasets were used: one from a transcranial static-magnetic-field stimulation (tSMS) experiment (tSMS20) and another one from the Human Connectome Project (HCP100). The tSMS20 dataset was originally acquired for a previous study tSMS over the Supplementary Motor Area (Pineda-Pardo et al, Commun Biol, 2019). The regions used in the study are also provided. As for the tSMS20 dataset, the stimulation protocol consisted of 30-minute tSMS using a single magnet placed over the supplementary motor area (SMA). Each subject underwent two stimulation sessions (real and sham) in two separate days, whose order was randomized. In each session, structural MRI was acquired before tSMS, and resting-state fMRI before and after. Structural images were T1-weighted (T1w), with 1 mm isotropic voxel. Functional data was acquired in 10 minutes-long sessions, TR/TE 2400/30 ms (250 volumes per session), with 3mm isotropic voxel. The preprocessed resting-state fMRI data are included in this repository (see dataset_description.txt file and Pineda-Pardo et al, Commun Biol, 2019 for more details) As for the HCP100 dataset, only the subject list is included, as data are already publicly available from the HCP initiative. If you use this data in a publication, please cite: Pineda-Pardo, J. A., Obeso, I., Guida, P., Dileone, M., Strange, B. A., Obeso, J. A., Oliviero, A. & Foffani, G. Static magnetic field stimulation of the supplementary motor area modulates resting-state activity and motor behavior. Communications Biology 2, (2019) Caballero-Insaurriaga, J., Pineda-Pardo, J. A., Obeso, I., Oliviero, A. & Foffani, G. Non-invasive modulation of human corticostriatal activity. Proceedings of the National Academy of Sciences of the United States of America (2023)

opencc-by-4.0Mar 2023View details →

zenodo44/100

Human Hyperpolarization Activated Cyclic Nucleotide Gated Ion Channel 4 (HCN4); A Target Enabling Package

HCN4 is one of four hyperpolarisation activated cyclic nucleotide gated ion channels. It is responsible for the pacemaker or funny (If) current in the heart and is required for maintenance of a stable heartbeat. Mutations in HCN4 lead to a number of arrhythmias. HCN4 is the target for the angina drug ivabradine, which reduces HCN4 activity. However, ivabradine is non-selective, affecting all of the four HCN channels. HCN4 is a close homologue of HCN2, which is a target for neuropathic and inflammatory pain treatment. We have solved the structure of HCN4 both in complex with cyclic AMP and without nucleotide. Comparison of our HCN4 structure with that of the related HCN1 channel (86% identity) allows us to suggest ways to design selectivity for small molecule inhibitors between these closely related channels.

opencc-by-4.0Oct 2018View details →

zenodo44/100

Image captioning dataset for human activities

An image captioning dataset including images of humans performing various activities. The included images include the following activities: <code>walking, running, sleeping, swimming, sitting, jumping, riding, climbing, drinking and reading.</code>

opencc-by-4.0Jan 2021View details →

zenodo44/100

Wallhack1.8k Dataset | Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition

This repository contains the Wallhack1.8k dataset for WiFi-based long-range activity recognition in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)/Through-Wall scenarios, as proposed in [1,2], as well as the CAD models (of 3D-printable parts) of the WiFi systems proposed in [2]. PyTroch Dataloader A minimal PyTorch dataloader for the Wallhack1.8k dataset is provided at: <a href="https://github.com/StrohmayerJ/wallhack1.8k" target="_blank" rel="noopener">https://github.com/StrohmayerJ/wallhack1.8k</a> Dataset Description The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets). To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly: <ul> <li>LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system</li> <li>LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system</li> <li>NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system</li> <li>NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system</li> </ul> These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector H being stored in the "data" field and the class label being stored in the "class" field. H is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of H (e.g., via numpy.abs(H)) yields the subcarrier amplitudes A. To extract the 52 L-LTF subcarriers used in [1], the following indices of A are to be selected: <pre><code># 52 L-LTF subcarriers csi_valid_subcarrier_index = [] csi_valid_subcarrier_index += [i for i in range(6, 32)] csi_valid_subcarrier_index += [i for i in range(33, 59)]</code></pre> Additional 56 HT-LTF subcarriers can be selected via: <pre><code># 56 HT-LTF subcarriers csi_valid_subcarrier_index += [i for i in range(66, 94)] csi_valid_subcarrier_index += [i for i in range(95, 123)]</code></pre> For more details on subcarrier selection, see <a href="https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html">ESP-IDF</a> (Section Wi-Fi Channel State Information) and <a href="https://github.com/espressif/esp-csi">esp-csi</a>. Extracted amplitude spectrograms with the corresponding label files of the train/validation/test split: "trainLabels.csv," "validationLabels.csv," and "testLabels.csv," can be found in the spectrograms/ directory. The columns in the label files correspond to the following: [Spectrogram index, Class label, Room label] <ul> <li>Spectrogram index: [0, ..., n]</li> <li>Class label: [0,1,2], where 0 = "no presence", 1 = "walking", and 2 = "walking + arm-waving."</li> <li>Room label: [0,1,2,3,4,5], where labels 1-5 correspond to the room number in the NLoS scenario (see Fig. 3 in [1]). The label 0 corresponds to no room and is used for the "no presence" class.</li> </ul> Dataset Overview: Table 1: Raw WiFi packet sequences. <table> <tbody> <tr> <td>Scenario</td> <td>System</td> <td>"no presence" /  label 0</td> <td>"walking"  / label 1</td> <td>"walking + arm-waving" /  label 2</td> <td>Total</td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4</td> <td>20</td> <td>20</td> <td>44</td> </tr> </tbody> </table> Table 2: Sample/Spectrogram distribution across activity classes in Wallhack1.8k. <table> <tbody> <tr> <td>Scenario</td> <td>System</td> <td> "no presence" /  label 0 </td> <td> "walking"  / label 1 </td> <td>"walking + arm-waving" /  label 2</td> <td>Total</td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>149</td> <td>154</td> <td>155</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>149</td> <td>160</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>148</td> <td>150</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>143</td> <td>147</td> <td>147</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>589</td> <td>611</td> <td>606</td> <td>1,806</td> </tr> </tbody> </table>   Download and Use This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to one of our papers [1,2]. [1] Strohmayer, Julian, and Martin Kampel. (2024). “Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition”, In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 42-56). Cham: Springer Nature Switzerland, doi: <a href="https://doi.org/10.1007/978-3-031-63211-2_4" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-63211-2_4</a>. [2] Strohmayer, Julian, and Martin Kampel., “Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition,” 2024 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2024, pp. 3594-3599, doi: <a href="https://doi.org/10.1109/ICIP51287.2024.10647666" target="_blank" rel="noopener">https://doi.org/10.1109/ICIP51287.2024.10647666</a>. BibTeX citations: <pre>@inproceedings{strohmayer2024data, title={Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages={42--56}, year={2024}, organization={Springer}} @INPROCEEDINGS{10647666,   author={Strohmayer, Julian and Kampel, Martin},   booktitle={2024 IEEE International Conference on Image Processing (ICIP)},    title={Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition},    year={2024},   volume={},   number={},   pages={3594-3599},   keywords={Visualization;Accuracy;System performance;Directional antennas;Directive antennas;Reflector antennas;Sensors;Human Activity Recognition;WiFi;Channel State Information;Through-Wall Sensing;ESP32},   doi={10.1109/ICIP51287.2024.10647666}} </pre>

opencc-by-4.0Oct 2024View details →

zenodo44/100

Dataset: WiFi-based Human Activity Recognition using Raspberry Pi

This dataset contains 980 802.11 Channel State Information captures for 11 activities performed in a small apartment by 1 subject. For full description, check README.md.

openmit-licenseOct 2021View details →

zenodo44/100

Dataset for Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity

Per-trial dataset accompanying the publication Stauch, Peter, Schuler, and Fries (2020): Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity. Additionaly, preprocessing code is provided as Codebase.zip.

opencc-by-4.0Nov 2020View details →

zenodo44/100

Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS

A portable fNIRS system Brite MKII (Artinis, NE) was placed on the PFC of the self-experimenting participant (Male, 43 years). Ten sources and eight detectors are combined into 22 long separation channels (30mm) and two short-separation channels (SSC) to cover the PFC (Figure 1A). The experiment lasted 392s where the participant was subject to pornographic video clips (V) and performed genital self-stimulation (M) until orgasm was reached (O). Citation of the article related to this dataset: <div> <div>Guevara, E. (2024). Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS [Preprint]. OSF. <a href="https://doi.org/10.31219/osf.io/6y2ze">https://doi.org/10.31219/osf.io/6y2ze</a></div> </div>

opencc-by-4.0Oct 2023View details →

zenodo44/100

Data from: Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells

Data that was reported in "Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells" by  Nathalie R. Reinhard1, Sanne van der Niet1, Anna Chertkova1, Marten Postma1, Theodorus W.J. Gadella Jr.1, Peter L. Hordijk1,2, and Joachim Goedhart1*   Affiliations: 1 University of Amsterdam, Molecular Cytology, Swammerdam Institute for Life Sciences, van Leeuwenhoek Centre for Advanced Microscopy, Amsterdam, the Netherlands 2 Department of Physiology, Free University Medical Center, Amsterdam, The Netherlands   *Correspondence to: j.goedhart@uva.nl

opencc-by-4.0Jan 2019View details →

zenodo44/100

Human activity classification

Collected dataset comprises body position recordings for various persons of diverse profile while performing six physical activities. The values include:  - Cod_pat is the unique code for each person; - Time is the timestamp; - Ax, Ay, Az represent the axis of the acceleration; - Mx, My, Mz represent the axis of the magnetometer; - Ch is the compass heading; - Label is the Class that provide the activity of the person.

opencc-by-4.0Dec 2018View details →

zenodo44/100

Shape, membrane morphology, and morphodynamic response of metabolically active human mitochondria revealed by scanning ion conductance microscopy

This contains the hole data set as well as all analysed data for the paper published in Beilstein Journal of Nanotechnology "Shape, membrane morphology and morphodynamic response of metabolically active human mitochondria revealed by Scanning Ion Conductance Microscopy". Most of the images were taken with the SICM. These uncompressed tiff files can be read and processed with the Gwyddion software or other scanning probe image processing software.

opencc-by-4.0Dec 2023View details →

zenodo44/100

Comparable respiratory activity in attached and suspended human fibroblasts

Zdrazilova L, Hansikova H, Gnaiger E (2021) Comparable respiratory activity in attached and suspended human fibroblasts. MitoFit Preprints 2021.7. <a href="http://dx.doi.org/10.26124/mitofit:2021-0007">doi:10.26124/mitofit:2021-0007</a> All respirometric data are expressed in SI units and are made available here Open Access.

opencc-by-4.0Sep 2021View details →

zenodo44/100

Chromatin activity identifies differential gene regulation across human ancestries

This repository contains data related to: Chromatin activity identifies differential gene regulation across human ancestries Kade P. Pettie, Maxwell Mumbach, Amanda J. Lea, Julien Ayroles, Howard Y. Chang, Maya Kasowski, Hunter B. Fraser

opencc-by-4.0Nov 2022View details →

zenodo44/100

Datasets for "Targeted insertion and reporter transgene activity at a gene safe harbor of the human blood fluke, Schistosoma mansoni"

To identify sites that could serve as potential genomic safe harbours (GSHs) for transgene integration, we conducted a genome-wide bioinformatic search based on established, widely accepted criteria, along with newly introduced criteria (below), that would satisfy benign and stable gene expression.  At the outset, we identified euchromatic regions in all developmental stages of S. mansoni to avoid silencing genes to be integrated upon CRISPR/Cas manipulation. With these criteria, we enriched for regions that were, (i) close to peaks of H3K4me3, a histone modification that is associated with euchromatin and transcription start sites, (ii) regions that did not include H3K27me3, a histone modification that is associated with heterochromatin, (iii) regions of open euchromatin accessible to Tn5 integration, in an Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) providing a positive display of integration events, and (iv) given that HIV-1 integrates preferentially into euchromatin in human cell lines, we used sites of HIV proviral integration known from S. mansoni to likewise support predictions of euchromatic regions. Examination of the draft genome of S. mansoni in Worm Base Parasite, version 7 (WormBase Parasite) identified 6,884 regions with enrichment of H3K4me3 in the absence of H3K27me3 in available developmental stages (H3K4me3 not K3K27me3). In mature, adult schistosomes, we found consistently 10,533 ATAC positive regions. There were 4,027 ATAC regions that overlapped with H3K4me3 but not K3K27me3, and 2,915 genes overlapped with (ATAC and H3K4me3 not H3K27me3). Forty-two unambiguous HIV integration sites were identified, and eight genes were ≤ 11 kb upstream or downstream from these integration sites. Repeats were masked with RepeatMasker V4.1.0 using a specific repeat library produced with RepeatModeler2 V2.0.1 and stored as a GFF file. To identify intergenic GSH, we located 10,149 intergenic regions.  There were 9,985 regions beyond 2 kb upstream and 8,837 regions outside long non-coding-RNA (lncRNA), which were intersected to 95,587 unique intergenic regions outside 2 kb and lncRNA of ≥100 bp.  Two hundred regions were identified intersecting with merged ATAC H3K4me3 signal. Four of these were situated ≤ 11 kb distance from HIV integration sites.  Made at George Washington University, Justus Liebig University Giessen, Khon Kaen University, Naresuan University, Aberystwyth University, Schistosomiasis Resource Center, IHPE.

opencc-by-4.0Dec 2022View details →

zenodo44/100

A Dataset of Bot and Human Activities in GitHub

A Dataset of Bot and Human Activities in GitHub This repository provides an updated version of a dataset of GitHub contributor activities that is accompanied by a paper published at MSR 2023 in the Data and Tool Showcase Track. The paper is entitled A Dataset of Bot and Human Activities in GitHub and is co-authored by Natarajan Chidambaram, Alexandre Decan and Tom Mens (Software Engineering Lab, University of Mons, Belgium). DOI: <a href="https://www.doi.org/10.1109/MSR59073.2023.00070">https://www.doi.org/10.1109/MSR59073.2023.00070</a>. This work is done as a part of Natarajan Chdiambaram's PhD research in the context of <a href="https://www.digitalwallonia.be/ia/">DigitalWallonia4.AI research project ARIAC (grant number 2010235)</a> and <a href="https://trail.ac/en/">TRAIL</a>. The dataset contains 1,015,422 high-level activities made by 350 bots and 620 human contributors on GitHub between 25 November 2022 and 15 April 2023. The activities were generated from 1,221,907 low-level events obtained from the GitHub's Event API and cover 24 distinct activity types. This dataset facilitates the characterisation of bot and human behaviour in GitHub repositories, by enabling the analysis of activity sequences and activity patterns of bot and human contributors. This dataset could lead to better bot identification tools and empirical studies on how bots play a role in collaborative software development. Files description The following files are provided as part of the archive: <ul> <li>bot_activities.json - A JSON file containing 754,165 activities made by 350 bot contributors;</li> <li>human_activities.json - A JSON file containing 261,258 activities made by 620 human contributors (anonymized);</li> <li>JsonSchema.json - A JSON schema that validates the above datasets;</li> <li>bots.txt - A TEXT file containing login names of all the 350 bots</li> </ul> Example Below is an example of a Closing pull request activity: <pre><code>{ "date": "2022-11-25T18:49:09+00:00", "activity": "Closing pull request", "contributor": "typescript-bot", "repository": "DefinitelyTyped/DefinitelyTyped", "comment": { "length": 249, "GH_node": "IC_kwDOAFz6BM5PJG7l" }, "pull_request": { "id": 62328, "title": "[qunit] Add `test.each()`", "created_at": "2022-09-19T17:34:28+00:00", "status": "closed", "closed_at": "2022-11-25T18:49:08+00:00", "merged": false, "GH_node": "PR_kwDOAFz6BM4_N5ib" }, "conversation": { "comments": 19 }, "payload": { "pr_commits": 1, "pr_changed_files": 5 } }</code></pre> List of activity types In total, we have identified 24 different high-level activity types from 15 different low-level event types. They are Creating repository, Creating branch, Creating tag, Deleting tag, Deleting repository, Publishing a release, Making repository public, Adding collaborator to repository, Forking repository, Starring repository, Editing wiki page, Opening issue, Closing issue, Reopening issue, Transferring issue, Commenting issue, Opening pull request, Closing pull request, Reopening pull request, Commenting pull request, Commenting pull request changes, Reviewing code, Commenting commits, Pushing commits. List of fields Not only does the dataset contain a list of activities made by bot and human contributors, but it also contains some details about these activities. For example, commenting issue activities provide details about the author of the comment, the repository and issue in which the comment was created, and so on. For all activity types, we provide the date of the activity, the contributor that made the activity, and the repository in which the activity took place. Depending on the activity type, additional fields are provided. In this section, we describe for each activity type the different fields that are provided in the JSON file. It is worth to mention that we also provide the corresponding JSON schema alongside with the datasets. Properties <ul> <li>date <ul> <li>Date on which the activity is performed</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-25T09:55:19+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>activity <ul> <li>The activity performed by the contributor</li> <li>Type: <code>string</code></li> <li>e.g., "Commenting pull request"</li> </ul> </li> <li>contributor <ul> <li>The login name of the contributor who performed this activity</li> <li>Type: <code>string</code></li> <li>e.g., "analysis-bot", "anonymised" in the case of a human contributor</li> </ul> </li> <li>repository <ul> <li>The repository in which the activity is performed</li> <li>Type: <code>string</code></li> <li>e.g., "apache/spark", "anonymised" in the case of a human contributor</li> </ul> </li> <li>issue <ul> <li>Issue information - provided for Opening issue, Closing issue, Reopening issue, Transferring issue and Commenting issue</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>id <ul> <li>Issue number</li> <li>Type: <code>integer</code></li> <li>e.g., 35471</li> </ul> </li> <li>title <ul> <li>Issue title</li> <li>Type: <code>string</code></li> <li>e.g., "error building handtracking gpu example with bazel", "anonymised" in the case of a human contributor</li> </ul> </li> <li>created_at <ul> <li>The date on which this issue is created</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-10T13:07:23+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>status <ul> <li>Current state of the issue</li> <li>Type: <code>string</code></li> <li>"open" or "closed"</li> </ul> </li> <li>closed_at <ul> <li>The date on which this issue is closed. "null" will be provided if the issue is open</li> <li>Types: <code>string</code>, <code>null</code></li> <li>e.g., "2022-11-25T10:42:39+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>resolved <ul> <li>The issue is resolved or not_planned/still open</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li>GH_node <ul> <li>The GitHub node of this issue</li> <li>Type: <code>string</code></li> <li>e.g., "IC_kwDOC27xRM5PHTBU", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li>pull_request <ul> <li>Pull request information - provided for Opening pull request, Closing pull request, Reopening pull request, Commenting pull request changes and Reviewing code</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>id <ul> <li>Pull request number</li> <li>Type: <code>integer</code></li> <li>e.g., 35471</li> </ul> </li> <li>title <ul> <li>Pull request title</li> <li>Type: <code>string</code></li> <li>e.g., "error building handtracking gpu example with bazel", "anonymised" in the case of a human contributor</li> </ul> </li> <li>created_at <ul> <li>The date on which this pull request is created</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-10T13:07:23+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>status <ul> <li>Current state of the pull request</li> <li>Type: <code>string</code></li> <li>"open" or "closed"</li> </ul> </li> <li>closed_at <ul> <li>The date on which this pull request is closed. "null" will be provided if the pull request is open</li> <li>Types: <code>string</code>, <code>null</code></li> <li>e.g., "2022-11-25T10:42:39+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>merged <ul> <li>The PR is merged or rejected/still open</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li>GH_node <ul> <li>The GitHub node of this pull request</li> <li>Type: <code>string</code></li> <li>e.g., "PR_kwDOC7Q2kM5Dsu3-", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li>review <ul> <li>Pull request review information - provided for Reviewing code</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>status <ul> <li>Status of the review</li> <li>Type: <code>string</code></li> <li>"changes_requested" or "approved" or "dismissed"</li> </ul> </li> <li>GH_node <ul> <li>The GitHub node of this review</li> <li>Type: <code>string</code></li> <li>e.g., "PRR_kwDOEBHXU85HLfIn", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li>conversation <ul> <li>Comments information in issue or pull request - Provided for Opening issue, Closing issue, Reopening issue, Transferring issue, Commenting issue, Opening pull request, Closing pull request, Reopening pull request and Commenting pull request</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>comments <ul> <li>Number of comments present in the corresponding issue or pull request</li> <li>Type: <code>integer</code></li> <li>e.g., 5</li> </ul> </li> </ul> </li> </ul> </li> <li>comment <ul> <li>Comment information - Provided for all the activities for which the field issue or pull_request is reported and additionally for commit comment</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>length <ul> <li>Length of the comment text (or description text if comment is not provided)</li> <li>Type: <code>integer</code></li> <li>e.g., 25</li> </ul> </li> <li>GH_node <ul> <li>The GitHub node of this comment or description. "null" will be provided if there is no comment expected</li> <li>Types: <code>string</code>, <code>null</code></li> <li>e.g., "IC_kwDOEj6V8c5PHT78", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li>gitref <ul> <li>Tag information - provided for Creating branch, Creating tag, Deleting branch, Deleting tag, Editing wiki page and Publishing a release</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>type <ul> <li>Type of the gitref</li> <li>Type: <code>string</code></li> <li>"tag" or "branch" or "commit"</li> </ul> </li> <li>name <ul> <li>Name of the gitref</li> <li>Type: <code>string</code></li> <li>e.g., "cherry-pick-11-to-release-4.10"</li> </ul> </li> <li>description_length <ul> <li>Length of the description text provided while creating the gitref. "null" be provided if the type is "branch" or "commit" as they do not have any description</li> <li>Type: <code>integer</code>, <code>null</code></li> <li>e.g., 23</li> </ul> </li> </ul> </li> </ul> </li> <li>release <ul> <li>Release information - provided for Publishing a release</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>name <ul> <li>The name of the release that is created. "null" will be provided if the name is not provided</li> <li>Type: <code>string</code>, <code>null</code></li> <li>e.g., "v0.65.9"</li> </ul> </li> <li>description_length <ul> <li>Length of the description of the release that is created</li> <li>Type: <code>integer</code></li> <li>e.g., 888</li> </ul> </li> <li>created_at <ul> <li>The date at which the release is created (activity date is the release published date)</li> <li>Type: <code>string</code></li> <li>e.g., "2022-11-25T11:34:48+00:00"</li> <li>String format must be a "date-time"</li> </ul> </li> <li>prerelease <ul> <li>If the release that is created is a prerelease or not</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li>new_tag <ul> <li>If a new tag is created for this release or another tag is re-used</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> <li>GH_node <ul> <li>The corresponding release node ID</li> <li>Type: <code>string</code></li> <li>e.g., "RE_kwDOCm6M2s4FBGxT", "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> <li>page <ul> <li>Page information - provided for Editing wiki page</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>name <ul> <li>Name of the page</li> <li>Type: <code>string</code></li> <li>e.g., "Workflow-status"</li> </ul> </li> <li>title <ul> <li>Title of the page</li> <li>Type: <code>string</code></li> <li>e.g., "Workflow status"</li> </ul> </li> <li>new <ul> <li>If the page is created new or existing page is edited</li> <li>Type: <code>boolean</code></li> <li>true or false</li> </ul> </li> </ul> </li> </ul> </li> <li>payload <ul> <li>Other additional details - Provided for Opening pull request, Closing pull request, Reopening pull request and pushing commits</li> <li>Type: <code>object</code></li> <li>Properties <ul> <li>pr_commits <ul> <li>The number of commits in this pull request</li> <li>Type: <code>integer</code></li> <li>e.g., 3</li> </ul> </li> <li>pr_changed_files <ul> <li>The number of files that are changed in this pull request</li> <li>Type: <code>integer</code></li> <li>e.g., 2</li> </ul> </li> <li>pushed_commits <ul> <li>The number of commits present in this push</li> <li>Type: <code>integer</code></li> <li>e.g., 4</li> </ul> </li> <li>distinct_pushed_commits <ul> <li>The distinct number of commits present in this push</li> <li>Type: <code>integer</code></li> <li>e.g., 1</li> </ul> </li> <li>github_push_id <ul> <li>The corresponding GitHub push ID</li> <li>Type: <code>integer</code></li> <li>e.g., 11790446870, "anonymised" in the case of a human contributor</li> </ul> </li> </ul> </li> </ul> </li> </ul> Mapping between activities and events For many activity types, the corresponding activity can be observed by the occurrence of a single event type. For example, the activity types Forking repository and Starring repository would require the occurrence of a single event type for each as given below. <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td>Forking repository</td> <td><code>ForkEvent</code></td> <td>-</td> </tr> <tr> <td>Starring repository</td> <td><code>WatchEvent</code></td> <td>action = "started"</td> </tr> </tbody> </table> However, in some cases, the same event type yields different activity types depending on the value present in the payload. For example, three different activity types can be generated from the same low-level event type CreateEvent, depending on the value of its ref_type (either "repository", "branch", or "tag") present in the payload. <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td>Creating repository</td> <td><code>CreateEvent</code></td> <td>ref_type = "repository"</td> </tr> <tr> <td>Creating branch</td> <td><code>CreateEvent</code></td> <td>ref_type = "branch"</td> </tr> <tr> <td>Creating tag</td> <td><code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> </tbody> </table> In some cases, there is no one-to-one mapping between events and activities. This is because some actions on GitHub may generate more than a single event and lead to a sequence of one mandatory event and a second optional event (marked with ?). For example, for the activity type Publishing a release, event type ReleaseEvent is mandatory with payload's action value = "published", while event type CreateEvent is optional as it is required only when a new tag is created along with the published release. <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td>Publishing a release</td> <td><code>ReleaseEvent</code></td> <td>action = "published"</td> </tr> <tr> <td> </td> <td>? <code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> </tbody> </table> All the identified activities along with their events type(s) and payload information is given in the following table. <table> <tbody><tr> <th>Activity type</th> <th>Event type</th> <th>Payload</th> </tr> </tbody><tbody> <tr> <td>Creating repository</td> <td><code>CreateEvent</code></td> <td>ref_type = "repository"</td> </tr> <tr> <td>Creating branch</td> <td><code>CreateEvent</code></td> <td>ref_type = "branch"</td> </tr> <tr> <td>Creating tag</td> <td><code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> <tr> <td>Deleting tag</td> <td><code>DeleteEvent</code></td> <td>ref_type = "tag"</td> </tr> <tr> <td>Deleting repository</td> <td><code>DeleteEvent</code></td> <td>ref_type = "branch"</td> </tr> <tr> <td>Publishing a release</td> <td><code>ReleaseEvent</code></td> <td>action = "published"</td> </tr> <tr> <td> </td> <td>? <code>CreateEvent</code></td> <td>ref_type = "tag"</td> </tr> <tr> <td>Making repository public</td> <td><code>PublicEvent</code></td> <td>-</td> </tr> <tr> <td>Adding collaborator to repository</td> <td><code>MemberEvent</code></td> <td>action = "added"</td> </tr> <tr> <td>Forking repository</td> <td><code>ForkEvent</code></td> <td>-</td> </tr> <tr> <td>Starring repository</td> <td><code>WatchEvent</code></td> <td>action = "started"</td> </tr> <tr> <td>Editing wiki page</td> <td><code>GollumEvent</code></td> <td>pages-->action = "created" or "edited"</td> </tr> <tr> <td>Opening issue</td> <td><code>IssuesEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td>Closing issue</td> <td><code>IssuesEvent</code></td> <td>action = "closed"</td> </tr> <tr> <td> </td> <td>? <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Reopening issue</td> <td><code>IssuesEvent</code></td> <td>action = "reopened"</td> </tr> <tr> <td> </td> <td>? <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Transferring issue</td> <td><code>IssuesEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td>Commenting issue</td> <td><code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Opening pull request</td> <td><code>PullRequestEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td>Closing pull request</td> <td><code>PullRequestEvent</code></td> <td>action = "closed"</td> </tr> <tr> <td> </td> <td>? <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Reopening pull request</td> <td><code>PullRequestEvent</code></td> <td>action = "opened"</td> </tr> <tr> <td> </td> <td>? <code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Commenting pull request</td> <td><code>IssueCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Commenting pull request changes</td> <td><code>PullrequestReviewCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td> </td> <td>? <code>PullRequestReviewEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Reviewing code</td> <td><code>PullRequestReviewEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Commenting commits</td> <td><code>CommitCommentEvent</code></td> <td>action = "created"</td> </tr> <tr> <td>Pushing commits</td> <td><code>PushEvent</code></td> <td>-</td> </tr> </tbody> </table>

opencc-by-4.0Mar 2023View details →

zenodo44/100

CollecTRI Data for Investigation of SETBP1 gene expression and transcription factor activity across human tissues

Here we provide the human CollecTRI prior (accessed May 2023) for inference of TF activity across 31 GTEx tissues using multivariate linear modeling method decoupleR. The `human_prior_tri.csv` includes 1,178 unique TFs (referred to as the source) that target 6,627 unique genes (referred to as targets) to give us 42,595 interactions in the CollecTRI prior input. Interactions are represented as a + or - 1 (mor).

opencc-by-4.0Aug 2023View details →

zenodo40/100

Dataset related to article "NKp46-expressing human gut-resident intraepithelial Vδ1 T cell subpopulation exhibits high antitumor activity against colorectal cancer"

γδ T cells account for a large fraction of human intestinal intraepithelial lymphocytes (IELs) endowed with potent antitumor activities. However, little is known about their origin, phenotype, and clinical relevance in colorectal cancer (CRC). To determine γδ IEL gut specificity, homing, and functions, γδ T cells were purified from human healthy blood, lymph nodes, liver, skin, and intestine, either disease-free, affected by CRC, or generated from thymic precursors. The constitutive expression of NKp46 specifically identifies a subset of cytotoxic Vδ1 T cells representing the largest fraction of gut-resident IELs. The ontogeny and gut-tropism of NKp46+/Vδ1 IELs depends both on distinctive features of Vδ1 thymic precursors and gut-environmental factors. Either the constitutive presence of NKp46 on tissue-resident Vδ1 intestinal IELs or its induced expression on IL-2/IL-15-activated Vδ1 thymocytes are associated with antitumor functions. Higher frequencies of NKp46+/Vδ1 IELs in tumor-free specimens from CRC patients correlate with a lower risk of developing metastatic III/IV disease stages. Additionally, our in vitro settings reproducing CRC tumor microenvironment inhibited the expansion of NKp46+/Vδ1 cells from activated thymic precursors. These results parallel the very low frequencies of NKp46+/Vδ1 IELs able to infiltrate CRC, thus providing insights to either follow-up cancer progression or to develop adoptive cellular therapies.   This dataset is created with fcs files form, in order to guarantee the access we attach a pdf information about

opencc-by-4.0Mar 2020View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record