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3,223 results for “Human Study”
Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)
<p>Summary statistics for genome-wide association studies reported in:</p> <p>Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>. </p> <p><strong>Abstract</strong></p> <p>Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.</p> <p> </p> <p>Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.</p>
Lethality datasets for "A comparative study of endoderm differentiation in humans and chimpanzees"
<p>These datasets were used to evaluate the embryonic lethality of 3 categories of genes: genes with shared reduction of variation in gene expression levels, genes with reduction of variation in only one species, and genes without a reduction of variation in either species.To obtain the data, we took the gene list of each of the 3 categories of genes and ran it through the Mammalian Phenotype database from Jackson Lab: <a href="http://www.informatics.jax.org/batch/summary">http://www.informatics.jax.org/batch/summary</a> in January 2018.</p>
Backpain exercise therapy remodels human epigenetic profiles in buccal and human peripheral blood mononuclear cells: An exploratory study in young male participants
<pre><strong>###### Files description #####</strong><br> <strong>Notes</strong>. 1) "BT" refers to before therapy and "AT" to after therapy. 2) 0 refers to FALSE and 1 to TRUE for binary variables. The provided files have tab-separated columns except the .RDS which is and R output of the mixOmics DIABLO integration analysis. <strong># Questionnaire</strong> > participants_categories.tsv: per participant (rows), output of the clustering with the participant ("ID") category ("category") per class<br> ("class") > questionnaire_agility_metrics.tsv: questionnaire and agility metrics per participant (rows) for the participants ("ID") with at least one paired AT+BT data in one type of biological sample (indicated in the columns "swab", "PBMC", and "plasma") <strong># PTMs</strong> Samples´ names are encoded as PBMC_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch (we removed _batch column suffix for the <br>processed files). NA indicates an undetected intensity. > raw_PBMC_light_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_light_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavy_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_heavy_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavynormalized_intensities.tsv: raw intensity of light peptides normalized by heavy peptides intensity (row) by precursor per <br>sample (column) > raw_swab_heavynormalized_labelled_intensities.tsv: idem from buccal cells > processed_cleaned_PBMC_log2intensities.tsv: processed (heavy normalized, imputed, batch-corrected) intensity of peptides aggregated by modification (PTM, row) by precursor per sample (column) after log2-transformation. The relative abundances are computed from this file. Rows without me/ac suffix represents the amount of unmodified peptide for the considered site. > processed_cleaned_swab_log2intensities.tsv: idem from buccal cells > rel_abundance_PTM_PBMC.tsv: relative abundance computed per precursor, e.g. for a given sample, the H3_K4+H3_K4me1+H3_K4me2+H3_K4me3 <br>relative abundance values must sum to 100, with the relative abundance of H3_K4 representing the absence of modified K4. > rel_abundance_PTM_swab.tsv: idem from buccal cells > tests_from_rel_abundance_PTM_swab_PBMC.tsv: per type of samples ("Sample.origin", i.e.swab of PBMC) and per PTM (rows, "PTM"), report <br>the output of classic (p-values, adjusted with Benjamini-Hochberg (BH), or Benjamini-Yekutieli procedure (BY), from raw and arcsin square <br>root transformed percentage) and PLS-DA tests (VIP - Variable Importance score - and its 95% confidence interval). The percentage of change<br>of each PTM after therapy relative tobefore therapy is reported in "perc_change.AT.over.BT" column. The "is_candidate" indicates if the PTM has been considered as a hit in the swab or PBMC. <strong># Plasma</strong> Samples´ names are encoded as PLASMA_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch. NA indicates an undetected intensity. > raw_plasma_maxquant_log2ibaq_intensities.tsv: raw data from protein group MaxQuant file. The iBAQ columns are used in later steps. > processed_cleaned_plasma_log2intensities.tsv: processed (imputed, batch-corrected) intensity of protein groups after log2-transformation. > tests_from_intens_plasma.tsv: per protein group ("Proteins.ID"), report the output of classic (p-values, adjusted Benjamini-Hochberg (BH),<br>or Benjamini-Yekutieli procedure (BY), from log2-transformed intensities) and PLS-DA tests (VIP and its 95% confidence interval). The log2 <br>fold change after therapy relative to before therapy is reported in "log2FC.AT.over.BT" column. The "is_candidate" indicates if the protein group has been considered as a hit. <strong># Integration</strong> > circos_input: output of DIABLO analysis with correlation threshold set to 0.7. Use the readRDS R function to open.</pre> <p> </p>
DWI Traveling Human Phantom Study
Open the record for dataset details and reuse information.
Collection of global datasets for the study of floods, droughts and their interactions with human societies
<p>This is a collection of 134 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to <a href="https://doi.org/10.1002/wat2.1424">Lindersson et al. (2020)</a> for further information about review methodology.</p> <p>The collection is a descriptive list, holding the following information for each dataset: </p> <ul> <li>Category<em> - as structured in Lindersson et al. (2020).</em></li> <li>Sub-category<em>- as structured in Lindersson et al. (2020).</em></li> <li>Abbreviation - <em>official or as specified in Lindersson et al. (2020).</em></li> <li>Title <em>- full title of dataset.</em></li> <li>Product(s)<em> - type of product(s) offered by the dataset.</em></li> <li>Period<em> - time period covered by the dataset, not defined for all datasets.</em></li> <li>Temporal resolution<em> - not defined for static datasets.</em></li> <li>Angular spatial resolution<em> - only defined for gridded datasets.</em></li> <li>Metric spatial resolution <em>- only defined for gridded datasets.</em></li> <li>Map scale</li> <li>Extent<em> - geographic coverage of dataset given in latitude limits.</em></li> <li>Description</li> <li>Creating institute(s)</li> <li>Data type<em> - raster, vector or tabular.</em></li> <li>File format</li> <li>Primary EO type<em> - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.</em></li> <li>Data sources<em> - lists the data sources behind the dataset, to the extent this is feasible.</em></li> <li>Data sources also in this table<em> - data sources that are also included as datasets in this collection.</em></li> <li>Intentionally compatible with<em> - defines other datasets in this collection that the dataset is intentinoally compatible with.</em></li> <li>Citation<em> - dataset reference or credit.</em></li> <li>Documentation <em>- dataset documentation.</em></li> <li>Web address<em> - dataset access link.</em></li> </ul> <p>NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.</p>
What do studies in wild mammals tell us about human emerging viral diseases in Mexico? database
<p>The database used in the article "<strong>What do studies in wild mammals tell us about human emerging viral diseases in Mexico?</strong>". It contains all available records of viral zoonotic and potential zoonotic species in Mexican wild mammals.</p> <p>The first file is a .csv file and the second one is .xls</p>
162 Human Error Descriptions and Categorizations from a User Study
<p><i><strong>Software Engineers' Human Errors</strong></i></p><p>This dataset contains descriptions of 162 human errors experienced by software engineering students during a user study described in the following publication:</p><ul><li>Benjamin S. Meyers and Andrew Meneely. Taxonomy-Based Human Error Assessment for Senior Software Engineering Students. Special Interest Group on Computer Science Education (SIGCSE) Technical Symposium. Forthcoming in 2024.</li></ul><p><i><strong>Included Files</strong></i></p><p>The "experienced_human_errors.csv" file contains a dataset of 162 human errors experienced during our user study. Participants documented their human errors in a Google Form with 8 questions.</p><p><i><strong>CSV Fields</strong></i></p><ul><li><strong>PARTICIPANT</strong>: Anonymous participant ID.</li><li><strong>INTERVIEW_DATE</strong>: Date of interview discussing human error.</li><li><strong>ID</strong>: Unique ID for experienced human error. Prefixed with "P1" for Phase 1 or "P2" for Phase 2.</li><li><strong>FINAL_CATEGORIZATION</strong>: Agreed upon T.H.E.S.E. categorization following discussion with interview facilitator.</li><li><strong>QUESTION_1</strong>: Anonymized participant answer to Question 1.</li><li><strong>QUESTION_2</strong>: Anonymized participant answer to Question 2.</li><li><strong>QUESTION_3</strong>: Anonymized participant answer to Question 3.</li><li><strong>QUESTION_4</strong>: Anonymized participant answer to Question 4.</li><li><strong>QUESTION_5</strong>: Anonymized participant answer to Question 5.</li><li><strong>QUESTION_6</strong>: Anonymized participant answer to Question 6.</li><li><strong>QUESTION_7</strong>: Anonymized participant answer to Question 7.</li><li><strong>QUESTION_8</strong>: Anonymized participant answer to Question 8.</li></ul><p><i><strong>Interview Questions</strong></i></p><ol><li>Please briefly describe the human error that you experienced.</li><li>If the human error you experienced resulted in a defect that was committed, please provide a link (or Git commit hash) to the commit below.</li><li>Is your human error a slip, lapse, or mistake?</li><li>Now, please examine the Taxonomy of Human Errors in Software Engineering (T.H.E.S.E.) and choose the specific human error that most accurately describes the human error you experienced. If you experienced multiple human errors, please submit this form once for each human error.</li><li>If there are other categories of human error that also describe the human error that you experienced, please note them here.</li><li>If you chose a 'General' or 'Other' category in Question (4), this question is required. Do you believe there is a missing human error category that better describes the human error that you experienced? If yes, please describe it below.</li><li>On a scale of 1 (not at all confident) to 5 (completely confident), how confident are you in your classification in the previous question?</li><li>Do you have any additional comments about this human error?</li></ol><p><i><strong>Anonymity</strong></i></p><p>Institutional Review Board approval for this research involving human subjects was granted by the Human Subjects Research Office at RIT on March 18, 2022. Participants signed an informed consent form acknowledging that (1) their participation was entirely voluntary and had no impact on their grades, and (2) their survey responses would be published in an anonymized format. All data released with this publication has been anonymized by replacing any personally identifiable information with participant identifiers.</p><p><i><strong>Contact</strong></i></p><p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p><p><i><strong>Acknowledgments</strong></i></p><p>Collection of this data has been sponsored in part by the National Science Foundation (grant 1922169), by the NSA Science of Security Lablet program (grant H98230-17-D-0080/2018-0438-02), and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
MetaboScope: A statistical toolbox for analyzing 1H nuclear magnetic resonance spectra from human clinical studies.
<p>MetaboScope is purposefully built as a pipeline where each module accepts the output generated by the previous one. This provides flexibility and simplicity of use, while being straightforward to maintain. The system and its libraries were developed in JavaScript and run as a web app; therefore, all the operations are performed on the local computer, circumventing the need to upload data. The code is open source (DOI: https://www.cheminfo.org/flavor/metabolomics/index.html) and can be readily installed locally. We provide module notes and video tutorials, in addition to clinical spectral datasets for modelling purposes.</p> <p>View data:</p> <p><a title="nmrium.org" href="https://www.nmrium.org/nmrium#?toc=https://zenodo.org/api/records/12916741/files/toc.json/content" target="_blank" rel="noopener">https://www.nmrium.org/nmrium#?toc=https://zenodo.org/api/records/12916741/files/toc.json/content</a></p>
Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices
<p>This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices"</p> <p>With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule.</p> <p>For more detail about this dataset, please refer to the paper listed above.</p>
Extracting interpretable rules with Bayesian Networks. A case study of intrinsic human hazardous properties of silver nanoforms for the Safety Dimension of Safe and Sustainable by design paradigm.
<p>Three different datasets: toxicological attributes in i) lung and ii) intestinal cell line along with system dependent features and iii) system independent pchem properties) were merged. Each row represents one set of experimental testing conditions and related system dependent nanodescriptors based on the exposure dose and NFs pre-treatment (for intestinal assessments). The system independent inputs are NF specific and independent of experimental conditions. Data is captured via FAIR principles where the reader can find the origin (institution) of each data, the responsible data creators (experimentalists), the raw measurements, the protocols followed and the instrumentations used for each experiment. .</p>
Data from: Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study
<p>This dataset accompanies the following article: "Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study," in <em>IEEE Transactions on Biomedical Engineering</em>, doi: 10.1109/TBME.2023.3263388.</p> <p>Knee acoustic emissions (AE) recorded in the 100-450 kHz and 15-200kHz frequency ranges from a cadaver specimen knee in flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. </p> <p><em><strong>Stages of artificially inflicted cartilage damage:</strong></em> the cartilage surface damage on the medial compartment, KL III; the cartilage surface damage on the medial compartment plus patellofemoral surface, KL III; the cartilage surface damage on the medial compartment plus on the patellofemoral surface KL IV; the cartilage surface damage on the medial compartment plus on the patellofemoral surface and lateral compartment.</p> <p><strong><em>Sensor positions</em></strong>: medial and lateral knee</p>
HUMANE internal case study: eVACUATE #1
<p>This case study was conducted on 14 December 2015. The purpose was to evaluate the usefulness of the HUMANE approach as perceived by relevant developers (software engineers), and additionally ask if the HUMANE typology facilitates cross-disciplinary understanding.</p> <p>The files included here provide a summary of the analysis and the transcript from a semi-structured focus group.</p>
HUMANE external case study: eVACUATE #2
<p>This case study was conducted in September to October 2016 with the purpose of providing an external validation of the HUMANE typology and method. This eVACUATE case-study comprises four different engagements in order to ensure a comprehensive evaluation: a quantitative online survey on the HUMANE design patterns; a quantitative survey on the HUMANE typology used for characterising Human-Machine Networks (HMNs); and two focus groups evaluating the HUMANE method (covering the profiling process, network diagramming, implication analysis, and design pattern approach).</p> <p>A summary of results, along with focus group transcripts, surveys and survey results are included here.</p>
Retractions in Humanities and Social Sciences: A Study of Retracted Papers from China
<p>This dataset presents a comprehensive study of retractions in the field of Humanities and Social Sciences (HSS), focusing specifically on retracted papers originating from China. </p>
First-in-human study of epidural spinal cord stimulation in individuals with spinal muscular atrophy
<p>Data from the paper: First-in-human study of epidural spinal cord stimulation in individuals with spinal muscular atrophy, Nature Medicine 2024</p>
Normal Retinotopy in Primary Visual Cortex in a Congenital Complete Unilateral Lesion of Lateral Geniculate Nucleus in Human: A Case Study
<p>The data set contains .nii files for each condition of retinotopic mapping in fMRI. (Meridians, Wedges and concentric rings). It also contains DTI data files with .bvec and .bval files. Psychophysics data is in two excel files for motion and orientation discrimination. </p>
Dataset from "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers" (Sverdljuk et al. 2022)
<p>Contains URNs (identifiers) for the newspapers used in the corpus study "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers".</p> <p>For each subcorpus there is an Excel file containing references to the objects used, together with basic metadata.</p> <p>The corpus definitions can be used in various webapps of the DH-LAB at the National Library of Norway, e.g.:</p> <p><a href="https://beta.nb.no/dhlab/concordances/">https://beta.nb.no/dhlab/concordances/</a></p> <p><a href="https://beta.nb.no/dhlab/collocations/">https://beta.nb.no/dhlab/collocations/</a></p> <p>See more at <a href="https://www.nb.no/dh-lab/">https://www.nb.no/dh-lab/</a></p>
Differences in dogs' event related potentials in response to human and dog vocal stimuli: A non-invasive study
<p>Recent advances in the field of canine neuro-cognition allow for the non-invasive research of brain mechanisms in family dogs. Considering the striking similarities between dog's and human (infant)'s socio-cognition at the behavioural level, both similarities and differences in neural background can be of particular relevance. The current study investigates brain responses of N=17 family dogs to human and conspecific emotional vocalisations using a fully non-invasive ERP paradigm. We found that similarly to humans, dogs show a differential ERP response depending on the species of the caller demonstrated by a more positive ERP response to human vocalisations compared to dog vocalisations in a time-window between 250-650 ms after stimulus onset. A later time-window between 800-900 ms also revealed a valence sensitive ERP response in interaction with the species of the caller. Our results are the first ERP evidence to show the species sensitivity of vocal neural processing in dogs along with indications of valence sensitive processes in later post-stimulus time-periods.</p>
Congruence among multiple indices of habitat preference for species facing human-induced rapid environmental change: A case study using the Brewer's sparrow
<p>Accurate evaluations of habitat preference are key to understanding optimal conditions for wildlife survival and reproduction. Habitat selection, however, usually is evaluated using a single index of preference, and congruence among multiple, relevant indices of preference is examined rarely.</p> <p>We assessed the concordance between patterns of habitat preference using three different indices of breeding site preference in a migratory songbird. Specifically, we compared the chronology of territorial establishment, pair formation, and reproductive initiation of the Brewer's sparrow (<em>Spizella breweri</em>) along a gradient of surface disturbance associated with natural gas development in Wyoming, USA during 2019.</p> <p>We expected all three indices to demonstrate a preference for breeding sites with less surface disturbance, where reproductive success typically is higher. By contrast, all indices suggested suboptimal preference with respect to surface disturbance, with some discrepancy among them. The chronology of settlement and pairing did not vary across the disturbance gradient, whereas nest initiation tended to occur earlier at sites with more disturbance.</p> <p>If the pattern of suboptimal selection of breeding sites that we identified is generalizable across other populations of migratory birds affected by energy development, the resultant lower fitness in those areas may exacerbate population declines.</p> <p>Our results suggest that traditional, single-index approaches to the study of habitat selection, if chosen carefully, may provide adequate inference on habitat preferences. Different metrics, however, can lead to at least subtle differences in patterns of habitat selection. The simultaneous examination of multiple indices of preference across a diversity of systems would help clarify the contexts under which preference metrics can become decoupled.</p>
Fig. 2 in Strongyloidiasis in humans and dogs in Southern Italy: an observational study
Fig. 2 (MAP 2): Geographic distribution of positive dogs (n=6) and humans (n=9). Different colours as in map 1 are indicative of the different habits (red= kennels, blue= agricultural farms; violet= livestock farm). None of the kennels/farms positive for dogs were positive for humans and vice-versa
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.