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63 results for “Longitudinal dataset”
[Dataset] Stroke Caregiver Burden in East Coast Peninsular Malaysia, A Short-term Longitudinal Study
<p>Raw dataset for study entitled "INFORMAL CAREGIVERS BURDEN AMONG STROKE PATIENTS IN EAST-COAST MALAYSIA: A SHORT-TERM LONGITUDINAL STUDY"</p> <p>This study is part of study funded by Newton Ungku Omar Fund (2020-2021) under grant for “A Scalable Solution for Supporting Informal Stroke Caregivers in Malaysia: Systematic Development and Feasibility Study” Malaysian Ministry of Education (203.PPSP.678003) and Medical Research Council, United Kingdom (MR/T018968/1).</p> <p>Please note that this data is in raw csv form, imported from REDCap. due to REDCap system, the raw file need to be relabel and relevel to reflect the original score or response.</p> <p>Data dictionary provided for data relabel and relevel purpose.</p> <p>R script also available to convert the raw csv into dataset with appropriate label and level</p> <p> </p> <p> </p>
Longitudinal urban form dataset of Midtown Manhattan: Measuring urban form evolution via quantitative descriptions of plots, buildings and streets from 1890 to the present
<p>This dataset contains data described and used in the research article <strong>"The impact of urban form on physical change: A quantitative and diachronic analysis of urban form evolution in Midtown Manhattan"</strong>. </p> <p>The longitudinal dataset contains urban form data on nearly 17,000 individual plots (parcels) in Midtown Manhattan, documented through four subsequent time frames: 1890, 1920, 1956 and 2021. The data was compiled from historical cartographic resources and open-access geospatial datasets listed in the ReadMe file. </p> <p>The dataset includes an array of quantitative descriptions of plots, buildings and streets central to the field of urban morphology, and the binary information of physical change (1: change, 0: no change) identified via diachronic comparison of each time frame at the scale of plots.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The dataset presented in this repository has been generated as part of a PhD research conducted at the University of Melbourne, Faculty of Architecture, Building and Planning and funded by the University of Melbourne - Melbourne Research Scholarship: </p> <p><strong>Tümtürk, O</strong>. (2024). <strong>A data-driven investigation on urban form evolution: Methodological and empirical support for unravelling the relation between urban form and spatial dynamics</strong>. Unpublished PhD Thesis. The University of Melbourne, Australia. </p>
Dataset for simulations of a beamline that controls longitudinal phase space whilst transporting LWFA electrons to an undulator
<p>This dataset relates to a design for a particle accelerator beamline. The beamline transports particles (electrons) from a laser wakefield accelerator (LWFA) source to an undulator. The unique design allows the 'chirp' or 'longitudinal phase space' of the electron distribution to be sheared during transport.<br> The dataset contains: 1) Initial bunch distributions created by the ASTRA generator program and conversion to MAD8 program format; 2) a working MAD8 batch file; 3) Three set-ups of the beamline to provide positive, negative or no shear; 4) Tracking simulation input files to track the initial distributions through each beamline set-up; 5) Output electron distributions that result from each tracking simulation.</p>
Dataset Musculoskeletal pain in health professionals at the end of their studies and 1 year after entry into the profession: A multi-center longitudinal study.
<p>Dataset related to the paper "Musculoskeletal pain in health professionals at the end of their studies and 1 year after entry into the profession: A multi-center longitudinal study."</p> <p><strong>Abstract</strong></p> <p>Background: Musculoskeletal pain, especially back pain, is common among health professionals (HPs). They reduce work productivity and cause high costs. This follow up study investigates the prevalence and individual course of Musculoskeletal pain among HP students at the end of their studies and one year after entering the health care workforce. Participants were asked whether their Musculoskeletal pain was related to study or work conditions.</p> <p>Method: Self reported one year prevalence for lower back pain, neck and shoulder pain, pain in arms or hands, and pain in legs or feet was collected by an online questionnaire at two timepoints from 1046 participating HPs. Generalized estimating equation (GEE) models of the binomial family with log link were employed to estimate adjusted prevalence and corresponding normal based 95% confidence intervals were derived using the bootstrap method with 1,000 replications.</p> <p>Results: Prevalence of lower back pain as well as neck and shoulder pain was very high at baseline and follow up in all students and later HPs. Prevalence for pain in arms or hands, legs or feet was lower and there were significant differences between the professions. HP associated their lower back pain and neck and shoulder pain clearly with study and work conditions; HPs linked pain in arms or hands, legs or feet strongly with work conditions only.</p> <p>Conclusion: The prevention of lower back pain and neck and shoulder pain must be included in the curricula of all health professions at universities. As best practice example, they should incorporate ergonomic measures and exercises as a daily routine of the formation of health professionals. The impact of physically demanding professional tasks on upper and lower extremities needs to be investigated in further studies in order to take preventive measures.</p>
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees
<p>We present a one-year-long <strong>M</strong>ulti-<strong>S</strong>ensor dataset with <strong>P</strong>henotypic trait measurements from honey <strong>B</strong>ees (MSPB). Data were continuously collected between April-2020 and April-2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), <em>Varroa</em> destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, which facilitate a broader scope of analysis on honey bees, such as bee acoustics analysis, multi-modal hive monitoring, queen presence detection, <em>Varroa </em>infection detection, hive population estimation, biological analysis of bees, etc.</p> <h3>Related Info</h3> <p>The data collection process, feature pre-processing, preliminary data analysis, and usage notes can be found in our paper <a href="https://arxiv.org/abs/2311.10876">https://arxiv.org/abs/2311.10876</a></p> <p>Check the project webpage (<a href="https://zhu00121.github.io/MSPB-webpage/">https://zhu00121.github.io/MSPB-webpage/</a>) and Github repo (<a href="https://github.com/MuSAELab/MSPB">https://github.com/MuSAELab/MSPB</a>) for more information.</p> <h3>Citation</h3> <p>Kindly cite the following paper:</p> <p>@misc{zhu2023mspb,</p> <p> title={MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees}, </p> <p> author={Yi Zhu and Mahsa Abdollahi and Ségolène Maucourt and Nico Coallier and Heitor R. Guimarães and Pierre Giovenazzo and Tiago H. Falk},</p> <p> year={2023},</p> <p> eprint={2311.10876},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={eess.AS}</p> <p>}</p> <h3>Contact</h3> <p>You can contact us at Yi.Zhu@inrs.ca, if you encounter any questions accessing the data.</p>
Dataset for: Smoking does not accelerate leukocyte telomere attrition: a meta-analysis of 18 longitudinal cohorts
<p>Summary dataset (.csv file) and R script (.R file) for the manuscript entitled:</p> <p>Smoking does not accelerate leukocyte telomere attrition: a meta-analysis of 18 longitudinal cohorts.</p> <p>The column names are explained at the beginning of the R script.</p> <p> </p>
WikiLinkGraphs: A complete, longitudinal and multilanguage dataset of the Wikipedia link networks
<p>This dataset contains yearly snapshots of the Wikipedia's internal link network for the 9 largest language edition (de, en, es, fr, it, nl, pl, ru, sv). The dataset spans over 17 years, from the creation of Wikipedia in 2001 to March 2018. The snapshots are taken on March 1st of every year.</p> <p>The graphs include the links extract from the wikitext of each page (i.e in the form [[wikilink]]). Links transcluded from templates are not included. Redirects are resolved to their target page.</p> <p>More detailed information and supporting datasets are available at: http://disi.unitn.it/~consonni/datasets/.</p> <p><strong>IMPORTANT NOTICE</strong></p> <p>Gzipped files are compressed two times by Zenodo, the MD5 provided by Zenodo and the SHA512 sums provided in the `.sha512sums.txt` files, match with the files compressed once. In other words, when you download a `.gz` file save it as `.gz.gz`, uncompress it once and it should match both the MD5 provided by Zenodo and the SHA512 sum provided by us. We have opened a bug report for this behavior on Zenodo's repository at: https://github.com/zenodo/zenodo/issues/1705</p> <p> </p>
A longitudinal study of the associations of children's body mass index and physical activity with blood pressure – dataset
<p>B-Proact1v is a longitudinal study examining changes in children’s physical activity and sedentary behaviours as they progress through primary school. In 2012-2013, 1299 Year 1 children (median age: 6 years) were recruited from 57 schools in greater Bristol, UK (total number of eligible children: 2600; recruitment rate: 50.0%). Following this, data were collected from 1223 Year 4 children (median age: 9 years) from 47 of the original schools between March 2015 and July 2016 (total number of eligible children: 2047; recruitment rate: 59.7%). This included 685 children from the original sample.</p> <p> </p> <p>This dataset represents a subset of the B-Proact1v data to examine the longitudinal associations of children’s body mass index and physical activity with blood pressure. Included in this repository is the dataset and a data dictionary. The dataset includes the variables that underlie the findings in a manuscript entitled ‘A longitudinal study of the associations of children’s body mass index and physical activity with blood pressure’ that has been submitted to PLOS ONE. This dataset has been made available so that future researchers can replicate the study findings using the data. If you wish to use the data for any purpose other than replicating the study findings, please contact the Principal Investigator Professor Russ Jago (russ.jago@bristol.ac.uk) to discuss this.</p>
Measuring Web Latency and Rendering Performance: Method, Tools & Longitudinal Dataset
<p>The dataset used in the paper entitled "Measuring Web Latency and Rendering Performance: Method, Tools & Longitudinal Dataset" published in IEEE Transactions for Network and Service Management. </p>
Dataset for the paper "Mental Health and Burnout during Medical School: Longitudinal Evolution and Covariates" published in PLOS ONE (2024)
<p><strong>Full reference of the paper:</strong></p> <p>Carrard V, Berney S, Bourquin C, Ranjbar S, Castelao E, Schlegel K, et al. (2024) Mental health and burnout during medical school: Longitudinal evolution and covariates. PLoS ONE 19(4): e0295100. <a href="https://doi.org/10.1371/journal.pone.0295100">https://doi.org/10.1371/journal.pone.0295100</a></p>
YJMob100K: City-Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories
<p>The YJMob100K human mobility datasets (YJMob100K_dataset1.csv.gz and YJMob100K_dataset1.csv.gz) contain the movement of a total of 100,000 individuals across a 75 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of 80,000 individuals across a 75-day business-as-usual period, while the second dataset contains the movement of 20,000 individuals across a 75-day period (including the last 15 days during an emergency) with unusual behavior. </p> <p>While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (cell_POIcat.csv.gz). The list of 85 POI categories can be found in POI_datacategories.csv. </p> <p>For details of the dataset, see Data Descriptor: </p> <ul> <li>Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. <em>Scientific Data</em>, <em>11</em>(1), 397. <a href="https://www.nature.com/articles/s41597-024-03237-9" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03237-9</a> </li> </ul> <p> </p> <p> </p> <p><strong>--- Details about the Human Mobility Prediction Challenge 2023 (ended November 13, 2023) --- </strong></p> <p>The challenge takes place in a mid-sized and highly populated metropolitan area, somewhere in Japan. The area is divided into 500 meters x 500 meters grid cells, resulting in a 200 x 200 grid cell space.</p> <p>The human mobility datasets (task1_dataset.csv.gz and task2_dataset.csv.gz) contain the movement of a total of 100,000 individuals across a 90 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of a 75 day business-as-usual period, while the second dataset contains the movement of a 75 day period during an emergency with unusual behavior.</p> <p>There are 2 tasks in the Human Mobility Prediction Challenge.</p> <p>In task 1, participants are provided with the full time series data (75 days) for 80,000 individuals, and partial (only 60 days) time series movement data for the remaining 20,000 individuals (task1_dataset.csv.gz). Given the provided data, Task 1 of the challenge is to predict the movement patterns of the individuals in the 20,000 individuals during days 60-74. Task 2 is similar task but uses a smaller dataset of 25,000 individuals in total, 2,500 of which have the locations during days 60-74 masked and need to be predicted (task2_dataset.csv.gz).</p> <p>While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (which is optional for use in the challenge) (cell_POIcat.csv.gz).</p> <p>For more details, see https://connection.mit.edu/humob-challenge-2023</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - Mouse feeding behavior dataset
<p>Dataset contains feeding and drinking behavioral recordings of C57BL6/J male mice. Mice were distributed into 2 groups (9 control mice and 8 high-fat diet mice) and tracked individually on Phecomp cages for 9 weeks. During the first experimental week all animals were given <em>ad libitum</em> access to a standard chow (habituation phase). After this first week, control mice continued with the same diet regime while high-fat mice were exclusively given <em>ad libitum</em> access to a high-fat chow. Data was used originally in this publication <a href="http://onlinelibrary.wiley.com/doi/10.1111/adb.12595/abstract">10.1111/adb.12595.</a></p> <p>The data set consist in:</p> <p>- a "mouse_recordings" folder containing a CSV file containing mouse recordings and the files.</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "phases" folder containing a CSV file containing experimental phases.</p> <p>- a "chromHMM_files" folder containing a cellmarkfiletable table used by chromHMM to learn a HMM model</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - D. melanogaster behavior dataset obtained with JAABA
<p>Dataset contains <em>Drosophila melanogaster </em>behavioral annotations of chasing. The dataset is formed by 20 GAL4 line flies group from a TrpA activation screen with increased propensity to chasing and by 19 pBDPGAL4U line (control) flies group. Ctrax motor trajectories derived from the original video-recordings (1000 seconds) were downloaded from this <a href="https://sourceforge.net/projects/jaaba/files/Sample%20Data/sampledata_v0.1.zip/download">link</a> and used to obtain the chasing behavioral annotations using <a href="http://jaaba.sourceforge.net/">JAABA</a> <a href="https://www.nature.com/articles/nmeth.2281">10.1038/nmeth.2281</a>: </p> <p>The data set consist in:</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "perframe_TrpA" folder containing mat files with three Ctrax derived variables (dnose2ell, dtheta and velmag) from the motor trajectories of the GAL4 line.</p> <p>- a "perframe_pBDPGAL4" folder containing mat files with three Ctrax derived variables (dnose2ell, dtheta and velmag) from the motor trajectories of the control line.</p> <p>- a "scores" folder including the JAABA chasing annotations in two mat file one for each fly line.</p>
Longitudinal dataset of human-building interactions in U.S. offices
<p>One year of longitudinal data (fifteen minute interval) on local thermal conditions, related behaviors, and comfort of twenty-four occupants of a medium-sized office building in Philadelphia, PA.</p> <p><strong><a href="https://zenodo.org/api/files/378e889e-3651-4d7a-8648-6e4170d7db06/langevincodebook.xlsx">langevincodebook.xlsx</a></strong> defines all the variables and their associated column number in the raw data .txt file.</p> <p><strong><a href="https://zenodo.org/api/files/378e889e-3651-4d7a-8648-6e4170d7db06/langevindata.txt.zip">langevindata.txt.zip</a></strong> includes the full one-year data from the study.</p>
Can Free Drawing Anticipate Handwriting Difficulties? A Longitudinal Study - DATASET
<p>Data to support the conference paper:</p> <p>Dui, L. G., Toffoli, S., Speziale, C., Termine, C., Matteucci, M., & Ferrante, S. (2022, September). Can Free Drawing Anticipate Handwriting Difficulties? A Longitudinal Study. In <em>2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)</em> (pp. 1-4). IEEE.</p> <ul> <li>BHI22_risk.xlsx: an Excel file with information about: <ul> <li>risk: the risk for handwriting delay, 0=no risk, 1=risk</li> <li>hand: right or left</li> <li>sex: M=male, F=female</li> <li>age: computed at the beginning of the longitudinal study</li> </ul> </li> <li>drawing_features.mat: a Matlab file with five datasets, one for each time point of the longitudinal study, with rows=children, columns=features</li> <li>metadata.mat: features names and type</li> </ul> <p> </p>
Longitudinal transport of suspended sediment in the Modaomen Estuary of the Pearl River: effects of river, tide, and mouth bar: Datasets
<p><strong>This dataset supplements the article: Longitudinal transport of suspended sediment in the Modaomen </strong><strong>E</strong><strong>stuary of the Pearl River: effects of river, tide, and mouth bar, submitted to Marine Geology</strong></p> <p>Contact information: Dr. Liu, F., School of Ocean Engineering and Technology, Sun Yat-sen University, Guangzhou, 510275, China.</p> <p>Email: <a href="mailto:liuf53@mail.sysu.edu.cn">liuf53@mail.sysu.edu.cn</a> (Liu, Feng)</p> <p><strong><em>Brief view of the dataset</em></strong></p> <p>Hydrodynamics, suspended sediment concentration and salinity distribution were measured at three fixed stations along the longitudinal direction of the estuary. The stations were located at Guadingjiao (M1), inside the bar (M2), and outside the bar (M3), and sampling was simultaneously conducted at neap tide (NT) from July 31 to August 1 and spring tide (ST) from August 8 to August 9 in 2017. All observation data has been compiled to include data from the surface to the bottom six layers within 26 hours. This directory contains the following datasets.</p> <p><strong>M10731.xlsx: </strong>Current velocity, current direction, salinity and SSC during neap tide at Station M1 </p> <p><strong>M10808.xlsx: </strong>Current velocity, current direction, salinity and SSC during spring tide at Station M1 </p> <p><strong>M20731.xlsx: </strong>Current velocity, current direction, salinity and SSC during neap tide at Station M2</p> <p><strong>M20808.xlsx: </strong>Current velocity, current direction, salinity and SSC during spring tide at Station M2</p> <p><strong>M30731.xlsx: </strong>Current velocity, current direction, salinity and SSC during neap tide at Station M3</p> <p><strong>M30808.xlsx: </strong>Current velocity, current direction, salinity and SSC during spring tide at Station M3</p>
Dataset and R script: a longitudinal hospital study of febrile children in Ghana
<p>This is the dataset and R script accompanying the manuscript "Fever in focus: symptoms, diagnoses, and treatment of febrile children in Ghana, a longitudinal hospital study."</p>
Dataset for the paper Exploring the Impact of Longitudinal Modulation on the Twisting Angle in Pancharatnam-Berry Phase-Based Waveguides
Open the record for dataset details and reuse information.
A large-scale longitudinal structured dataset of the dark web cryptomarket Evolution (2014–2015)
<p>Data includes a structured dataset of the forum and marketplace of the dark web cryptomarket Evolution as well as a longitudinal network snapshot dataset of a temporal weighted communication network extracted from the forum data. The datasets were extracted from Dark Net Market archive data while resolving many of the data quality issues inherent to raw data scraped from online sources.</p><p>Dark web cryptomarkets facilitate the online trade of illicit goods. Evolution was active from January 2014 until March 2015; and formed a combination of a carding forum and an underground drug market. This dataset includes the vast majority of data that could be obtained from the webpages of the forum and marketplace of Evolution. This includes, for example, information on the number of sales each individual vendor has completed, forum post contents, and product categories.</p><p>The extracted communication network models users' co-posting in the same forum topics, in essence modelling communication between them.</p><p>Additionally included is the code used to produce the dataset from the Dark Net Market archive data.</p>
Viral Communication: Trajectories of Experience through the Pandemic - A Qualitative Longitudinal Dataset
<p>Here, we present a dataset collected within a longitudinal interview study that has been conducted as part of a larger project (i.e., Viral Communication), exploring (changing) public attitudes and behaviours through the course of the pandemic in Germany. From a nationally representative survey, forty participants were purposively sampled on the basis of gender, age and socioeconomic status for the interviews. Each participant was interviewed three times within a 10 month time frame (between December 2020 and September 2021), with the exception of two dropouts from the study. The semi-structured interviews were developed to further elaborate on some of the responses in the survey instrument and to provide additional insights into topics and controversies surrounding the Covid-19 pandemic in Germany, such as information/misinformation, trust/distrust, compliance, vaccination, and conspiracy beliefs. </p> <ul> <li>Interviews Phase I (December 2020): 40 Interviews</li> <li>Interviews Phase II (April 2021): 38 Interviews</li> <li>Interviews Phase III (September 2021): 38 Interviews</li> </ul>
ScienceDex guides
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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.