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opengpl-2.0Dec 2017View details →

Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - C. elegans unc-16 and N2 motion behavior dataset

Dataset contains Caenorhabditis elegans motor behaviors originally used in this publication <a href="https://www.nature.com/articles/nmeth.2560">10.1038/nmeth.2560</a> and downloaded from the available <a href="http://wormbehavior.mrc-lmb.cam.ac.uk/">DB</a> which points to Zenodo. The dataset consists in two worm strains, 20 individuals from a mutant unc-16 strain with reduced mobility and 40 individuals from a control N2 strain. The behavioral measures derived from each individual worm trajectory were available in a HDF5-formatted file (Hierarchical Data Format Version 5) that has been included in this dataset. The data set consist in: - a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data. - a "N2" folder containing the 40 HDF5 files with the measures derived from the N2 worms. - a "N2" folder containing the 20 HDF5 files with the measures derived from the unc-16 worms.

opencc-by-4.0Jul 2022View details →

MobileWell100+: A Multivariate Longitudinal Mobile Dataset for Investigating Individual and Collective Well-Being

This study engaged 103 participants over a period spanning from November 14 to December 16, 2021, ensuring representation across various demographic factors: 51 females, 52 males, aged 18-70, with varied annual incomes and from 17 Spanish regions. The MobileWell100+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic details, COVID-19-related inquiries, emotional, behavioral, and well-being data. Complementing this, social welfare data from external sources offers contextual insight. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent.  The following is a non-exhaustive list of collected data: <ul> <li>Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, unlocked).</li> <li>Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, symptoms and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ).</li> <li>Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) are measured.</li> <li>Data corresponding to weekly surveys prompted via the smartphone, where information on work situation, symptoms and COVID vaccination, hours of physical activity per week, questions related to physical and mental health, etc. is requested.</li> </ul> For a more detailed description of the study please refer to MobileWell100+StudyDescription.pdf. For a more detailed description of the collected data, variables and data files please refer to MobileWell100+FilesDescription.pdf.

opencc-by-4.0Apr 2024View details →

MobileWell400+: A Large-Scale Multivariate Longitudinal Mobile Dataset for Investigating Individual and Collective Well-Being

This study engaged 409 participants over a period spanning from July 10 to August 8, 2023, ensuring representation across various demographic factors: 221 females, 186 males, 2 non-binary, year of birth between 1951 and 2005, with varied annual incomes and from 15 Spanish regions. The MobileWell400+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic, emotional, social, behavioral, and well-being data. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent.  The following is a non-exhaustive list of collected data: <ul> <li>Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, locked, unlocked).</li> <li>Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, effects and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception.</li> <li>Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) and social interaction (quantity and quality) are measured.</li> <li>Data corresponding to weekly surveys prompted via the smartphone, where information on overall health, hours of physical activity per week, lonileness, and questions related to well-being are asked.</li> <li>Data corresponding to an final survey prompted via the smartphone, consisting of similar questions to the ones asked in the initial survey, namely psychological and well-being items (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception questions.</li> </ul> For a more detailed description of the study please refer to MobileWell400+StudyDescription.pdf. For a more detailed description of the collected data, variables and data files please refer to MobileWell400+FilesDescription.pdf.

opencc-by-4.0Jul 2024View details →

Dataset of the round-robin programme for longitudinal tensile testing

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opencc-by-4.0Feb 2023View details →

DATASET RELATED TO ARTICLE "Muscle quantitative MRI in adult SMA patients on nusinersen treatment_ a longitudinal study"

Excel file with raw data of muscle fat fraction before and after nusinersen treatment

opencc-zeroDec 2022View details →

Normalised absorbance ratios of IgG for myxoma and rabbit hemorrhagic disease viruses: Longitudinal dataset wild European rabbits

This study addresses the long-term dynamics of humoral immunity to two viral pathogens of the European rabbit, an endangered keystone species of major ecological relevance. Such studies are particularly challenging in free-ranging species, and a combination of longitudinal capture-mark-recapture and semi-quantitative serology was used to address this question. Over 600 normalised absorbance ratios of indirect ELISA, obtained from 505 individual rabbits in 7 populations over five years were analysed using linear mixed models. The results support a lifelong acquired humoral immunity to myxoma virus and rabbit hemorrhagic disease virus upon natural infection and suggest the presence of maternal immunity to the latter in wild juvenile rabbits. These results contribute to understanding the epidemiology of two viral diseases threatening this keystone species and assist in developing conservation programs.

opencc-zeroMay 2023View details →

Imaging dataset from: Longitudinal tracking of acute kidney injury reveals injury propagation along the nephron

<div> Acute kidney injury (AKI) is a risk factor for chronic kidney disease (CKD), but the cellular mechanisms leading to impaired tubular recovery and subsequent AKI-CKD-transition are not yet fully understood. In this study, we combined transgenic mice to monitor proliferation in vivo, a novel injury model of AKI, in which ischemia-reperfusion injury (IRI) was induced in half of the kidney (partial-IRI), and serial intravital 2-photon imaging via an Abdominal Imaging Window (AIW) to track tissue remodeling in post- and non-ischemic kidney regions longitudinally over 3 weeks. Our results and novel findings are presented in the associated pre-print "Longitudinal tracking of acute kidney injury reveals injury propagation along the nephron". </div> <div> In this dataset, we provide the imaging data which we acquired during the study. Overall, the dataset consists of: #1: serial intravital imaging via 2-photon microscopy and genetic identification of proliferating cells in kidneys undergoing partial IRI; #2: serial intravital imaging via 2-photon microscopy and genetic identification of proliferating cells of control, uninjured kidneys; #3: intravital imaging of kidney epithelial cells during intravenous injection of a fluorescent bolus to determine the identity of tubular segments across the nephron; #4: intravital imaging of and genetic identification of proliferating cells after selective laser injury of S1 proximal tubule cells; #5 in vivo and ex-vivo imaging of kidney and kidney slices after VCAM1 staining. The data descriptors provide detailed guidelines on how to place individual imaging files in the context of the pathophysiological states used in the study. </div> <div> Our goal is to provide an organized and accessible unique and unprocessed in vivo imaging dataset that documents in vivo tubule cell remodeling in the mouse kidney longitudinally and during physiological and pathological conditions. Sharing of this dataset ensures reproducing and expanding of our preclinical findings of tubule injury and (failed) recovery during AKI. Furthermore, this dataset may also be utilized for teaching purposes, as the combination of fluorescent imaging techniques allows the detailed visualization of kidney anatomy and physiology over multiple conditions. </div>

publicJun 2023View details →

Normalised absorbance ratios of IgG for myxoma and rabbit hemorrhagic disease viruses: Longitudinal dataset wild European rabbits

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publicMay 2023View details →

Imaging dataset from: Longitudinal tracking of acute kidney injury reveals injury propagation along the nephron

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publicJan 2024View details →

Data from: physiological and emotional assessment of college students using wearable and mobile devices during the 2020 COVID-19 lockdown: an intensive, longitudinal dataset

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opencc-by-4.0May 2022View details →

Mapping Research on Thucydides - A Longitudinal Analysis - Dataset

Mapping Research on Thucydides - A Longitudinal Analysis - Dataset Analyzed and presented at the 11th GSDA Conference http://www.gsda.gr

opencc-by-4.0Dec 2023View details →

A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study

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opencc-by-4.0Dec 2023View details →

A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study

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opencc-by-4.0Mar 2022View details →

Dataset of the study "Psychological factors and trajectories of post-surgical pain: A longitudinal prospective study"

Dataset of the study "Psychological factors and trajectories of post-surgical pain: A longitudinal prospective study" published in Pain Practice, 2022 Feb; 22(2): 159-170. doi: 10.1111/papr.13074

opencc-by-4.0May 2024View details →

Datasets Correlational Longitudinal Studies (Task 4.4, D4.3)

The datasets are part of WP4 and the Report correlational longitudinal studies (D4.3). Data for this deliverable was collected among various types of energy communities and within different sociopolitical contexts: <ul> <li>A representative sample of the Dutch general population via a pre-recruited panel (based on gender, education, income, and age; 3 waves with 6 months in between) & a gender representative sample of the Spanish general population via a pre-recruited panel (3 waves with 6 months in between).</li> <li>Members and non-members of a network of local energy initiatives called Buurkracht (which roughly translates to “neighbor power”) in the Netherlands (2 waves with 9 months in between)</li> <li>Members of the Global Ecovillage Network (GEN) in Europe (2 waves with 12 months in between).</li> <li>Members of various energy communities in Europe (EU Ecom) via REScoop, the European federation of citizen energy cooperatives, and via the Local Governments for Sustainability global network (ICLEI).</li> <li>The Polish Housing Cooperative Wroclaw South (HCWS) in which multiple energy community initiatives were initiated such as an initiative among residents to collectively decide to install PV systems on a particular building</li> </ul> Available upon request: <ul> <li> More extensive versions of these datasets (e.g., including socio-demographics, levels of involvement in energy communities, number of contacts within energy community, more detailed household energy measures) are available upon request via DataverseNL (<a href="https://doi.org/10.34894/YJDPEB">https://doi.org/10.34894/YJDPEB). </a> </li> </ul> Note. An earlier version of Task4.1-D4.3_BK-Longitudinal Data_anonymized, when not all data was collected yet, was also used for Deliverable D2.3 <a href="https://online-raketen.at/sites/site0261/media/downloads/d2.3_energy_citizenship_scale_-_validation_study_final_version.pdf">Energy citizenship scale - Validation Study</a> and as such stored in folder Datasets Energy Citizenship Scale Development and Validation (Task 2.4, D2.2, D2.3; <a href="https://doi.org/10.5281/zenodo.11367936">https://doi.org/10.5281/zenodo.11367936</a>).

opencc-by-4.0Sep 2019View details →

Fibre Identification Blind Test dataset: Images of longitudinal and cross-section views of hemp, nettle, and flax fibres

Dataset produced by Denis Waudby while undertaking analysis of a sample of fibres presented to him as a blind test of a new fibre identification methodology designed to reduce subjectivity in fibre identification protocols for distinguishing bast fibres (specifically Hemp, Nettle, and Flax)

dryad32/100

Data from: A framework for the identification of long-term social avoidance in longitudinal datasets

Animal sociality is of significant interest to evolutionary and behavioural ecologists, with efforts focused on the patterns, causes and fitness outcomes of social preference. However, individual social patterns are the consequence of both attraction to (preference for) and avoidance of conspecifics. Despite this, social avoidance has received far less attention than social preference. Here, we detail the necessary steps to generate a spatially explicit, iterative null model which can be used to identify non-random social avoidance in longitudinal studies of social animals. We specifically identify and detail parameters which will influence the validity of the model. To test the usability of this model, we applied it to two longitudinal studies of social animals (Eastern water dragons (Intellegama leseurii) and bottlenose dolphins (Tursiops aduncus) to identify the presence of social avoidances. Using this model allowed us to identify the presence of social avoidances in both species. We hope that the framework presented here inspires interest in addressing this critical gap in our understanding of animal sociality, in turn allowing for a more holistic understanding of social interactions, relationships and structure.

opencc-zeroDec 2016View details →

opencc-by-4.0Jan 2023View details →

Dataset: Stunting and Its Association with Education and Cognitive Outcomes in Adulthood: A Longitudinal Study in Indonesia

Abstract Stunting is associated with adverse outcomes in adulthood. This article specifically aims to analyse the relationship between childhood stunting and education as well as cognitive outcomes for adults in Indonesia. Pooled data from two waves of the Indonesia Family Life Survey in 1993 and 1997, identified 4851 children aged 0-5 by their stunting status as well as their relative height to be compared for their differences in educational outcomes and cognitive abilities in 2014. Height-for-age z-score was used to proxy relative height and determine stunting status based on 2006 WHO child’s growth standards. The outcomes for education and cognitive abilities include educational levels, years of schooling, age of school entry, failing a grade, and scores for cognitive tests. The study employs estimation models of pooled regressions and instrumental variables. Stunting and relatively small stature had a significant association with cognitive development, and they worked as an intermediary to cognitive developmental barriers manifested in reduced educational outcomes. A lack of one SD in height-for-age was associated with a 0.125-point reduction in education level, 0.4 years shortened length of the school, 6% higher chances of failing grades and dropouts from secondary school, and 0.19-0.24 SD lowered cognitive and numerical scores. Similarly, stunting tends to decrease cognitive test scores by 0.56-0.8 SD compared to non-stunting and reduce educational achievement by 0.38 points, 1.4 years shorter schooling, 17.7% and 23% more likely to fail grades. In addition, HAZ and stunting are also associated with the school entry age of 0.35 and 0.11 years, respectively.