Find research datasets worth reusing

opencc-by-sa-4.0Jun 2022View details →

Annual terrestrial Human Footprint dataset from 1982 to 2000

<a href="https://www.nature.com/articles/s41597-022-01284-8">Human footprint dataset</a> extrapolated to past periods 1982--2000. For each pixel we fit a logit-model and then extrapolate it to past years to produce assumed Human footprint prior to year 2000. This assumes simple linear trends in Human footprint.

opencc-by-4.0Dec 2023View details →

Antigen-specific CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph node and blood following vaccination in humans

Abstract:  SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection. More Information: <ul> <li>Preprint: <a href="https://www.researchsquare.com/article/rs-3304466/v1">Research Square.</a></li> <li>Sample information: data_inventory.csv file.</li> <li>Code code_github_repo.zip or at the <a href="https://github.com/ncborcherding/COVID_TCR">original github repo</a></li> <li>Interactive Portal: <a href="https://cellpilot.emed.wustl.edu/">CellPilot</a></li> </ul>

opencc-by-4.0Jun 2024View details →

MMSEQS meets AntiRef: reference clusters of human antibody sequences

This data set contains pre-computed mmseqs databases for the antiref fasta files created by Briney et al.  Please cite the original work if you use any of the databases provided here. Sources: <ul> <li><a href="https://github.com/brineylab/antiref">Antiref GitHub</a></li> <li><a href="../records/7474336">Antiref Zenodo</a></li> <li><a href="https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad109/7247530?login=true">Antiref Paper</a></li> </ul>   The mmseqs databases were created as follows:   ``` aria2x -x16 -s16 --input-file antiref_links.txt snakemake -s antiref_mmseqs.smk --jobs 1 --cores 1 --local-cores 250 ```

opencc-by-4.0Jun 2024View details →

Data for "Profiling the transcriptomic age of single-cells in humans"

This is a supplementary data for the article titled "Profiling transcriptomic age of human single-cells". Data created in this project is shared here for the scientific community.  Here we used available scRNA-seq data of 1,058,909 blood cells of 508 healthy, human donors, for developing cell-type-specific single-cell transcriptomic clocks and predicting the age of human blood cells.  We also applied our clocks to different external datasets and evaluated the age of single cells originated from COVID-19 patients and human embryos. For the description of the content of the dataset see the ReadMe file.

opengpl-3.0-or-laterJul 2024View details →

Temperature and Climate Attribution estimates supporting "Human Fingerprints on Daily Temperatures in 2022" (2x2 degrees, 2022)

These data support the publication of "Human Fingerprints on Daily Temperatures in 2022" published in the <a href="https://www.ametsoc.org/index.cfm/ams/publications/bulletin-of-the-american-meteorological-society-bams/explaining-extreme-events-from-a-climate-perspective/">BAMS-EEE special issue</a> in 2024 (DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0264.1">10.1175/BAMS-D-23-0264.1</a>). Included are: <ul> <li>Temperatures: Gilfordetal2024_BAMS-EEE_T2022.nc</li> <li>Attributions estimates (Climate Shift Index and Change in Information due to Perspective): Gilfordetal2024_BAMS-EEE_ChIP2022.nc</li> </ul> And an accompanying land-sea mask from ERA5 (Gilfordetal2024_BAMS-EEE_LandSeaMask.nc). All data values valid for the 2022 calendar year and interpolated to a 2x2 degrees spatial grid to support the study's analysis. For more information on this dataset or to follow up, please contact Daniel Gilford (<a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a>). Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, High Meadows Foundation, and the William and Flora Hewlett Foundation.

opencc-by-sa-4.0Dec 2017View details →

Integrated analysis of anatomical and electrophysiological human intracranial data

The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated and readily adjusted to iEEG data sets with other characteristics. The protocol can be implemented by a graduate student or post-doctoral fellow with minimal MATLAB experience and takes approximately an hour, excluding the automated cortical surface extraction. This collection contains the data described in the protocol and that can be used to replicate all results.

Figshare52/100

MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information

This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.

opencc-by-4.0Dec 2016View details →

opencc-by-4.0Jun 2018View details →

Genome-wide association summary statistics for human blood plasma glycome

The dataset contains results of genome-wide association study of human blood plasma glycome. The 113 files contain association summary statistics for 113 glycome traits, of which 36 were directly measured by UPLC technology and 77 were derived glycome traits. Description of each glycome trait can be found in the Additional notes section. This dataset is also available for graphical exploration in the genomic context at <a href="http://gwasarchive.org">http://gwasarchive.org</a>.  The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank. When using downloaded data, please cite corresponding paper and this repository: <ol> <li>Sharapov, S. Z., Tsepilov, Y. A., Klaric, L., Mangino, M., Thareja, G., Shadrina, A. S., … Aulchenko, Y. (2019). Defining the genetic control of human blood plasma N-glycome using genome-wide association study. Human Molecular Genetics. http://doi.org/10.1093/hmg/ddz054</li> <li>Sodbo Sharapov, Yakov Tsepilov, Lucija Klaric, Massimo Mangino, Gaurav Thareja, Mirna Simurina, Concetta Dagostino, Julia Dmitrieva, Marija Vilaj, FranoVuckovic, Tamara Pavic, Jerko Stambuk, Irena Trbojevic-Akmacic, Jasminka Kristic, Jelena Simunovic, Ana Momcilovic, Harry Campbell, Malcolm Dunlop, Susan Farrington, Maria Pucic-Bakovic, Christian Gieger, Massimo Allegri, Edouard Louis, Michel Georges, Karsten Suhre, Tim Spector, Frances MK Williams, Gordan Lauc, Yurii Aulchenko. (2018). Genome-wide association summary statistics for human blood plasma glycome (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1298406</li> </ol> Funding This work was supported by the European Community’s Seventh Framework Programme funded project PainOmics (Grant agreement # 602736) and by the European Structural and Investments funding for the "Croatian National Centre of Research Excellence in Personalized Healthcare" (contract #KK.01.1.1.01.0010). The work of SSh was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Programme. The work of YT was supported by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project #0324-2018-0017). Karsten Suhre and Gaurav Thareja are supported by ‘Biomedical Research Program’ funds at Weill Cornell Medicine - Qatar, a program funded by the Qatar Foundation. We thank all staff at Weill Cornell Medicine - Qatar and Hamad Medical Corporation, and especially all study participants who made the QMDiab study possible. The SOCCS study was supported by grants from Cancer Research UK (C348/A3758, C348/A8896, C348/ A18927); Scottish Government Chief Scientist Office (K/OPR/2/2/D333, CZB/4/94); Medical Research Council (G0000657-53203, MR/K018647/1); Centre Grant from CORE as part of the Digestive Cancer Campaign (<a href="http://www.corecharity.org.uk">http://www.corecharity.org.uk</a>). TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. Column headers: <ol> <li>SNP: SNP rsID</li> <li>CHR: chromosome</li> <li>POS: position (GRCh37 build) </li> <li>OTHER_ALLELE: reference allele (coded as "0")</li> <li>EFFECT_ALLELE: effective allele (coded as "1")</li> <li>EAF: effective allele frequency </li> <li>N: sample size</li> <li>BETA: effect size of effective allele</li> <li>SE: standard error of effect size</li> <li>PVAL: P-value of association (without GC correction)</li> <li>IMPUTATION: imputation quality</li> </ol>

opencc-by-4.0Jul 2018View details →

Genome-wide association summary statistics for human healthspan

The dataset contains genome-wide association summary statistics computed for heathspan. The UKB sub-population of 300,447 genetically Caucasian, British individuals were analyzed. For more details see [1]. The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank. When using downloaded data, please cite corresponding paper and this repository: <ol> <li>Zenin, A., Tsepilov, Y., Sharapov, S., Getmantsev, E., Menshikov, L. I., Fedichev, P. O., & Aulchenko, Y. (2019). Identification of 12 genetic loci associated with human healthspan. Communications Biology, 2(1), 41. http://doi.org/10.1038/s42003-019-0290-0</li> <li>Aleksandr Zenin, Yakov Tsepilov, Sodbo Sharapov, Evgeny Getmantsev, Leonid Menshikov, Peter Fedichev, & Yurii Aulchenko. (2018). Genome-wide association summary statistics for human healthspan (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1302861</li> </ol> Funding The work was supported by Russian Ministry of Science and Education under 5-100 Excellence Programme.  The work was supported by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project #0324-2018-0017).  This research has been conducted using the UK Biobank Resource.  The study has been funded by Gero LLC. Column headers: <ol> <li>SNPID - SNP rsID</li> <li>chr - chromosome</li> <li>pos - position (GRCh37 build / hg19)</li> <li>EA - effective allele (coded as "1")</li> <li>RA - reference allele (coded as "0")</li> <li>EAF - effective allele frequency</li> <li>beta - effect size of effective allele</li> <li>se - standard error of effect size</li> <li>Z - Z-value of association</li> <li>-log10(p-value) - minus log10(P-value) of association</li> </ol>

opencc-by-4.0Dec 2023View details →

THÖR-MAGNI: A Large-scale Indoor Motion Capture Recording of Human Movement and Interaction

<h1>The THÖR-MAGNI Dataset Tutorials</h1> THÖR-MAGNI datasets is a novel dataset of accurate human and robot navigation and interaction in diverse indoor contexts, building on the previous <a href="https://ieeexplore.ieee.org/abstract/document/8954833/">THÖR dataset protocol</a>. We provide position and head orientation motion capture data, 3D LiDAR scans and gaze tracking. In total, THÖR-MAGNI captures 3.5 hours of motion of 40 participants on 5 recording days. This data collection is designed around systematic variation of factors in the environment to allow building cue-conditioned models of human motion and verifying hypotheses on factor impact. To that end, THÖR-MAGNI encompasses 5 scenarios, in which some of them have different conditions (i.e., we vary some factor): <ul> <li>Scenario 1 (plus conditions A and B): <ul> <li> Participants move in groups and individually;</li> <li> Robot as static obstacle;</li> <li> Environment with 3 obstacles and lane marking on the floor for condition B;</li> </ul> </li> </ul> <ul> <li> Scenario 2: <ul> <li> Participants move in groups, individually and transport objects with variable difficulty (i.e. bucket, boxes and a poster stand);</li> <li> Robot as static obstacle;</li> <li> Environment with 3 obstacles;</li> </ul> </li> </ul> <ul> <li>Scenario 3 (plus conditions A and B): <ul> <li> Participants move in groups, individually and transporting objects with variable difficulty (i.e. bucket, boxes and a poster stand). We denote each role as: Visitors-Alone, Visitors-Group 2, Visitors-Group 3, Carrier-Bucket, Carrier-Box, Carrier-Large Object;</li> <li> Teleoperated robot as moving agent: in condition A, the robot moves with differential drive; in condition B, the robot moves with omni-directional drive;</li> <li> Environment with 2 obstacles;</li> </ul> </li> </ul> <ul> <li>Scenario 4 (plus conditions A and B): <ul> <li> All participants, denoted as Visitors-Alone HRI interacted with the teleoperated mobile robot;</li> <li> Robot interacted in two ways: in condition A (Verbal-Only), the Anthropomorphic Robot Mock Driver (ARMoD), a small humanoid NAO robot on top of the mobile platform, only used speech to communicate the next goal point to the participant; in condition B the ARMoD used speech, gestures and robotic gaze to convey the same message;</li> <li> Free space environment</li> </ul> </li> </ul> <ul> <li>Scenario 5: <ul> <li> Participants move alone (Visitors-Alone) and one of the participants, denoted as Visitors-Alone HRI, transport objects and interact with the robot;</li> <li> The ARMoD is remotely controlled by an experimenter and proactively offers help;</li> <li> Free space environment;</li> </ul> </li> </ul> <h2>Preliminary steps</h2> Before proceeding, make sure to download the data from ZENODO <h3>1. Directory Structure</h3> ├── CLiFF_Maps <- Directory for CLiFF Maps for all files          ├── Files <- Directory for the csv files          ├── Readme.md ├── CSVs_Scenarios <- Directory for aligned data for all scenarios          ├── Scenario_1 <- Directory for the csv files for Scenario 1          ├── Scenario_2 <- Directory for the csv files for Scenario 2          ├── Scenario_3 <- Directory for the csv files for Scenario 3          ├── Scenario_4 <- Directory for the csv files for Scenario 4          ├── Scenario_5 <- Directory for the csv files for Scenario 5 ├── docs          ├── tutorials.md <- Tutorials document on how to use the data ├── Lidar_sample         ├── Files <- Directory for sample files                ├── 170522_SC3B_1 <- Directory for the pcd files                ├── 170522_SC3B_1.csv <- Synchronization file with QTM                ├── manual_view_point.json <- json file with manual view point for visualization                ├── requirements.txt <- script pip requirements                ├── visualize_pcd.py <- script visualize the lidar data        ├── Readme.md ├── maps <- Directory for maps of the environment (PNG files) and offsets (json file)        ├── offsets.json <- Offsets of the map with respect to the global coordinate frame origin        ├── {date}_SC{sc_id}_map.png <- Maps for `date` in {1205, 1305, 1705, 1805} and `sc_id` in {1A, 1B, 2, 3}        ├── 3009_map.png <- Map for the Scenarios 4A, 4B and 5 ├── MP4_Videos        ├── Files <- Directory for the mp4 files        ├── pupil_scene_camera_instrinsics.json <- json file with the intrinsics of pupil camera ├── TSVs_RAWET <- Directory for the TSV files for the Raw Eyetracking data for all Scenarios        ├── synch_info.csv <- Event markers necessary to align motion capture with eyetracking data        ├── Files <- Directory with all the raw eyetracking TSV files ├── goals_positions.csv <- File with the goals locations   <h3>2. Data Structure and Dataset Files</h3> Withing each Scenario directory, each csv file contains: 2.1. Headers The dataset metadata overview contains important information found in the CSV file headers. This reference is designed to help users understand and use the dataset effectively. The headers include details such as FILE_ID, which provides information on the date, scenario, condition, and run associated with each recording. The header of the document includes important quantities such as the number of frames recorded (N_FRAMES_QTM), the count of rigid bodies (N_BODIES), and the total number of markers (N_MARKERS). It also provides information about the order of the contiguous rotation matrix (CONTIGUOUS_ROTATION_MATRIX), modalities measured with units, and specified measurement units. The text presents details on the eyetracking devices used in each recording, including their infrared sensor and scene camera frequencies, as well as an indication of the presence of eyetracking data. The header provides specific information about rigid bodies, including their names (BODY_NAMES), role labels (BODY_ROLES), and the number of markers associated with each rigid body (BODY_NR_MARKERS). Finally, the table lists all marker names used in the file. This metadata provides researchers and practitioners with essential guidance on recording information, data quantities, and specifics about rigid bodies and markers. It is a valuable resource for understanding and effectively using the dataset in the CSV files. 2.2. Trajectory Data The remaining portion of the CSV file integrates merged data from the motion capture system and eye tracking devices, organized based on participants' helmet rigid bodies. Columns within the dataset include XYZ coordinates of all markers, spatial centroid coordinates, 6DOF orientation of the object's local coordinate frame, and if available eye tracking data, encompassing 2D/3D gaze coordinates, scene recording frame numbers, eye movement types, and IMU data. Missing data is denoted by "N/A" or an empty cell. Temporal indexing is facilitated by the "Time" or "Frame" column, indicating timestamps or frame numbers. The motion capture system records at 100Hz, Tobii Glasses at 50Hz (Raw); 25 Hz (Camera), and Pupil Glasses at 100Hz (Raw); 30 Hz (Camera). The dataset is structured around motion capture recordings, and for each rigid body, such as "Helmet_1," details per frame include XYZ coordinates of markers, centroid coordinates, and a 9-element rotational matrix describing helmet orientation. <table> <tbody> <tr> <td>Header</td> <td>Explanation</td> </tr> <tr> <td>Helmet_1 - 1 X</td> <td>X-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - 1 Y</td> <td>Y-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - 1 Z</td> <td>Z-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - [...]</td> <td>Same for Marker 2 and 3 of Helmet_1</td> </tr> <tr> <td>Helmet_1 Centroid_X</td> <td>X-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 Centroid_Y</td> <td>Y-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 Centroid_Z</td> <td>Z-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 R0</td> <td>1st Element of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> <tr> <td>Helmet_1 R[..]</td> <td>Same for R1- R7</td> </tr> <tr> <td>Helmet_1 R8</td> <td>9th Element of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> </tbody> </table>   2.3. Eyetracking Data The eye tracking data in the dataset includes 16 participants, providing a comprehensive dataset of over 500 minutes of recorded data across the different activities and scenarios with three different eyetracking devices. Devices are denoted with a special "Tracker_ID" in the dataset, i.e.: <table> <tbody> <tr> <td>Tracker ID</td> <td>Eyetracking Device</td> </tr> <tr> <td>TB2</td> <td>Tobii 2 Glasses</td> </tr> <tr> <td>TB3</td> <td>Tobii 3 Glasses</td> </tr> <tr> <td>PPL</td> <td>Pupil Insivisible Glasses</td> </tr> </tbody> </table> Gaze points are classified into fixations and saccades using the Tobii I-VT Attention filter, which is specifically optimized for dynamic scenarios with a velocity threshold of 100°. Eyetracking devices were systematically repeated after each 4-minute recording to account for natural variations in participants' eye shapes and to improve the gaze estimation algorithms. In addition, gaze estimation adjustments for the pupil invisible glasses were made after each 4-minute recording to mitigate potential drifts. It's worth noting that the scene cameras of the eye tracking glasses had different fields of view. The scene camera of the Pupil Invisible Glasses had a 1088x1080 image with both horizontal (HFOV) and vertical (VFOV) opening angles of 80°, while the Tobii Glasses provided a 1920x1080 image with different opening angles for Tobii Glasses 3 (HFOV: 95°, VFOV: 63°) and Tobii Glasses 2 (HFOV: 82°, VFOV: 52°). NOTE AS OF 2024: Videos are NOW part of the dataset For one participant, wearing the Tobii Glasses 3 and Helmet_6, the data would be denoted as: <table> <tbody> <tr> <td>Header</td> <td>Explanation</td> </tr> <tr> <td>Helmet_6 - [...]</td> <td>*X,Y,Z Coordinates for 5 markers*</td> </tr> <tr> <td>Helmet_6 [...]</td> <td>X,Y,Z Coordinates for 1 Centroid* </td> </tr> <tr> <td>Helmet_6 R[...]</td> <td>9 Elements of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> <tr> <td> Helmet_6 TB3_Accelerometer_[...] </td> <td>Accelerometer data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_Gyroscope_[...]</td> <td>Gyroscope data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_Magnetometer_[...]</td> <td>Magnetometer data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_G2D_[...]</td> <td>2D Eye tracking data (X,Y)</td> </tr> <tr> <td>Helmet_6 TB3_G3D_[...]</td> <td>3D Cyclopic Eye gaze Vector (X,Y,Z)</td> </tr> <tr> <td>Helmet_6 TB3_Movement</td> <td>Eye movement type (N/A, Fixation or Saccade)</td> </tr> <tr> <td>Helmet_6 TB3_SceneFNr</td> <td>Frame number of the scene camera recording </td> </tr> </tbody> </table> <h2>How to use and tools</h2> <a href="https://github.com/tmralmeida/magni-dash/tree/dash-public">magni-dash</a> <a href="https://magni-dash.streamlit.app">This</a> is a dashboard to quickly visualize our data: trajectories, speeds, eye-tracking data and LiDAR visualization (for Scenario 3). If you cannot use the dashboard from the streamlit cloud service, just run it locally by following the <a href="https://github.com/tmralmeida/magni-dash/tree/dash-public">README File</a>. <a href="https://github.com/tmralmeida/thor-magni-tools">thor-magni-tools</a> To install and use the package, follow the instructions on the <a href="https://github.com/tmralmeida/thor-magni-tools/blob/main/README.md">README file</a> . This package comprises: <ul> <li>3D trajectory restoration: agents in the scene wore an helmet. The helmet is equipped with markers, which are tracked by the Mocap system. 3D trajectory restoration stands for <a href="https://github.com/tmralmeida/thor-magni-tools/blob/main/thor_magni_tools/preprocessing/cfg.yaml#L3">two different ways</a> of aggregating the trackings of the various markers in each helmet: (1) 3D-restoration and (2) 3D-best marker. The former applies an average over the locations of all visible markers while the latter uses the marker with highest tracking duration.</li> <li>3D pre-processing of restored trajectories: interpolation, downsampling and smoothing. To run the 3D pre-processing, check <a href="https://github.com/tmralmeida/thor-magni-tools?tab=readme-ov-file#preprocessing#preprocessing">this</a>.</li> <li>trajectory analysis: trajectory-related metrics like tracking duration (in seconds), number of 8s tracklets, motion speed, path efficiency score, and minimal distance between people. To run the trajectory analysis, check <a href="https://github.com/tmralmeida/thor-magni-tools?tab=readme-ov-file#preprocessing#analysis">this</a>.</li> </ul>

opencc-by-4.0Oct 2024View details →

Raw Data on Extracellular Particles in 613 Human and 163 Canine Diluted Plasma and Blood Samples Assessed by Interferometric Light Microscopy

Extracellular nanoparticles (EPs) are cellular fragments. After being released in cell exterior, they become  mediators of the cell-cell interaction. Their characterization in bodily fluids may reflect the clinical status of the organism. Here we present data on the number density n and hydrodynamic diameter Dh of EPs assessed directly in diluted plasma and blood by using a recently developed technique, Interferometric Light Microscopy  (Romolo et al., 2022). The data are presented in the attached Table. We collected 613 blood and plasma samples from human patients with Inflammatory Bowel Disease (IBD) taken into tubes with trisodium citrate and ethylenediaminetetraacetic acid (EDTA) anticoagulants and 163 blood and plasma samples from canine patients with Brachycephalic Obstructive Airway Syndrome (BOAS). The human study was conducted in accordance with the Declaration of Helsinki, and approved by the National Medical Ethics Committee of the Republic of Slovenia (0120-271/2022/4; KME 27 July 2022). All procedures in the animal study complied with the relevant Slovenian government regulations (Animal Protection Act, Official Gazette of the Republic of Slovenia, No. 43/2007). The animal study was approved by the Animals in Experiments Welfare Commission of the Veterinary Faculty, University of Ljubljana, approval number 18-3/2022-1. Information regarding sample preparation is documented in the MIBlood-EV reports. <div> <div> <div><a name="_msocom_1"></a></div> </div> </div>

opencc-by-4.0Mar 2023View details →

Data on a citation context analysis focusing on natural sciences and social sciences and humanities

This dataset contains data on citation context analysis between natural sciences (NS) and social sciences and humanities (SSH). In particular, the data were created through manual coding of each citation between papers related to SDG7 (renewable energy) and SDG13 (climate change) and papers cited by them. This dataset consists of 9 files, associated with the article: Nishikawa, K. How and why are citations between disciplines made? A citation context analysis focusing on natural sciences and social sciences and humanities. Scientometrics (2023). <a href="https://doi.org/10.1007/s11192-023-04664-y">https://doi.org/10.1007/s11192-023-04664-y</a>   The files are numbered as follows: <ul> <li>00 – README</li> <li>01 – Data by citation pair for SDG7 (original)</li> <li>02 – Data by citation pair for SDG13 (original)</li> <li>03 – Data by mention location for SDG7 (original)</li> <li>04 – Data by mention location for SDG13 (original)</li> <li>05 – Data by citation pair for SDG7 (additional)</li> <li>06 – Data by citation pair for SDG13 (additional)</li> <li>07 – Data by mention location for SDG7 (additional)</li> <li>08 – Data by mention location for SDG13 (additional)</li> </ul> See README for more information.

openCC0Jan 2019View details →

Human Olfaction Without Apparent Olfactory Bulbs

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openCC0Jan 2020View details →

Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI

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openCC0Jan 2020View details →

Taste Quality Representation in the Human Brain

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openCC0Jan 2021View details →

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

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opencc-by-4.0Jan 2020View details →

Deep splicing plasticity of the human adenovirus type 5 transcriptome as a driver of virus evolution nanopore data 48hpi

Adenovirus infected MRC5 cells direct RNA sequencing of the mRNA using nanopore. From the paper Deep splicing plasticity of the human adenovirus type 5 transcriptome as a driver of virus evolution. Both the uncorrected fastq files and the lordec corrected files together with the normalised illumina data used to correct the nanpore data are here.

opencc-by-4.0Jan 2020View details →

PALEODEM/Late Glacial and Early Holocene human demographic responses to climatic and environmental change in Atlantic Iberia

This data files and R markdown scripts have been used in the meta-analysis of chronological and subsistence patterns of Atlantic hunter-gatherer groups between Late Glacial and Early Holocene in Atlantic Iberia. They correspond to the following reference:  McLaughlin, T.R., Gómez-Puche, M., Cascalheira, J., Bicho, N.F., Fernández-López de Pablo, J. 2020. Late Glacial and Early Holocene human demographic responses to climatic and environmental change in Atlantic Iberia. Phil. Trans. R. Soc. B. (revised submitted version 29/04/2020) We specify the content of each file further down: <ol> <li>Analysis_markdown.Rmd – R markdown file with the scripts to reproduce the analyses.</li> <li>Analysis_markdown.pdf – R markdown file in pdf format to reproduce the analyses.</li> <li>database_references.docx –A separate text file that comprises the extended bibliographic references used as source of the archaeological radiocarbon archaeological and isotopic data sets analyzed.</li> <li>Datelist.csv – spreadsheet that contains the 371 radiocarbon dates used as raw data to run the scripts. The last column of the table includes the bibliographical reference of the archaeological data compiled.</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>Pailler_and_Bard_42.csv – Sea surface temperature data of the Atlantic margin of Iberia based on the paper: Pailler D, Bard E. 2002 High frequency palaeoceanographic changes during the past 140 000 yr recorded by the organic matter in sediments of the Iberian Margin. Palaeogeogr. Palaeoclimatol. Palaeoecol.181, 431–452. (doi:https://doi.org/10.1016/S0031-0182(01)00444-8)</li> <li>Paleodiet.csv – spreadsheet containing the published palaeodietary isotopic information of the human remains considered in this study.</li> <li>src.r – source r code of custom functions called upon this analysis by the R.markdown files. </li> </ol> To reproduce analyses reported in the McLaughlin et al Phil Trans paper, donwload R_scripts and csv_files into the same folder. Open the *.rmd scripts in RStudio (https://www.rstudio.com), and run the scripts.  The csv files can also be imported into R and used by the scripts.

opencc-by-4.0Jul 2020View details →

Global consensus map of human transcription factor footprints

Vierstra, J. et al. Global reference mapping of human transcription factor footprints. Nature 583, 729–736 (2020). <a href="https://doi.org/10.1038/s41586-020-2528-x">https://doi.org/10.1038/s41586-020-2528-x</a> Preprint @ bioRxiv: <a href="https://doi.org/10.1101/2020.01.31.927798">https://doi.org/10.1101/2020.01.31.927798</a> Contact: Jeff Vierstra (<a href="mailto:jvierstra@altius.org?subject=Consensus%20DNase%20I%20footprints">jvierstra@altius.org</a>) Genomic DNase I footprinting enables quantitative, nucleotide-resolution delineation of sites of transcription factor occupancy within native chromatin. We combined sampling of >67 billion uniquely mapping DNase I cleavages from >240 human cell types and states to index, with unprecedented accuracy and resolution, human genomic footprints and thereby the sequence elements that encode transcription factor recognition sites. Please see <a href="http://vierstra.org/resources/dgf">http://vierstra.org/resources/dgf </a>for additional information and a complete set of raw DNase I data for individual datasets. Additionally, raw data can also be accessed via the ENCODE data portal (<a href="http://encodeproject.org">http://encodeproject.org</a>) using the dataset accessions found in Supplementary Table 1. Code for footprint analysis and tutorials on how to access and manipulate digital genomic footprint data can be found at <a href="https://footprint-tools.readthedocs.io/en/latest/">https://footprint-tools.readthedocs.io/en/latest/</a>. All files herein correspond to human genome build version GRCh38 (UCSC hg38). Dataset contents: <ul> <li>Biosample metadata – Supplementary_Table_1.xlsx</li> <li>Motif clustering metadata – Supplementary_Table_2.xlsx</li> <li>ChIP-seq validation metadata – Supplementary_Table_3.xlsx</li> <li>Consensus footprint coordinates and assigned motif archetypes TSV file (BED-format) with consensus footprint (posterior probability>0.99) coordinates and overlaps with matches to motif model clusters. The legend file contains column definitions in detail. <ul> <li>consensus_footprints_and_motifs_hg38.bed.gz</li> <li>consensus_footprints_and_motifs_legend.txt</li> </ul> </li> <li>Motif archetype matches overlapping consensus footprints TSV file (BED-format) containing the coordinates for clustered motif model matches that overlap consensus footprints <ul> <li>collapsed_motifs_overlaping_consensus_footprints.bed.gz</li> <li>collapsed_motifs_overlaping_consensus_footprints_legend.txt</li> </ul> </li> <li>Footprint occupancy matrix of consensus footprints Rows are same order as the consensus footprint file and columns are same order as in the metadata files. <ul> <li>consensus_index_matrix_full_hg38.txt.gz (Values are –log(1-posterior))</li> <li>consensus_index_matrix_binary_hg38.txt.gz (binary occupancy matrix, where footprints with posterior footprint probability >0.99 are considered occupied)</li> </ul> </li> <li>Single nucleotide variants tested for allelic imbalance  The legend file contains column definitions in detail. <ul> <li>genotypes.vcf.gz - Genotyping and allelic read depth for each biosample (see header for more information)</li> <li>tested_snvs_padj.bed.gz - SNVs tested for imbalance (TSV, BED-format)</li> <li>tested_snvs_padj_legend.txt</li> </ul> </li> </ul>