Find research datasets worth reusing

opencc-by-4.0Dec 2020View details →

Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.

This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive  of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms  We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references).  This repository contains 3 different files: TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions TableS2_physical_priors.xlsx - contains three tabs: ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) +  ChIP-seq datasets (GSE28350, GSE81521) from  (Hochbaum et. al, 2011, Li et. al, 2016). eY1HATAC- contains  3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016). motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1 TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources) --- Description of methodology to obtain regulatory interactions in TableS2: Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations. For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands. An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs. For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair. For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).

opencc-by-4.0Jun 2024View details →

The interaction of physical structure and nutrient loading drives ecosystem change in a large tropical lake over 40 years (DATA)

Datasets for manuscript entitled "The interaction of physical structure and nutrient loading drives ecosystem change in a large tropical lake over 40 years" Includes data collected by Fadum and Hall as well as unplublished data from Vaux and Goldman 1984. Abstract: Many lakes across the world are entering novel states and experiencing altered biogeochemical cycling due to local anthropogenic stressors. In the tropics, understanding the drivers of these changes can be difficult due to a lack of documented historic conditions or an absence of continuous monitoring that can distinguish between intra- and inter-annual variation. Over the last forty years (1980–2020), Lake Yojoa (Honduras) has experienced increased watershed development as well as the introduction of a large net-pen Tilapia farm, resulting in a dramatic reduction in seasonal water clarity, increased trophic state and altered nutrient dynamics, shifting Lake Yojoa from an oligotrophic (low productivity) to mesotrophic (moderate productivity) ecosystem. To assess the changes that have occurred in Lake Yojoa as well as putative drivers for those changes, we compared Secchi depth (water clarity), dissolved inorganic nitrogen (DIN), and total phosphorus (TP) concentrations at continuous semi-monthly intervals for the three years between 1979 and 1983 and again at continuous 16-day intervals for 2018–2020. Between those two periods we observed the loss of a clear water phase that previously occurred in the months when the water column was fully mixed. Seasonal peaks in DIN coincident with mixing suggest that an enhanced accumulation of ammonium in the hypolimnion (the bottom layer of a stratified lake) during stratification, and release to the epilimnion (the top layer of a stratified lake) with mixing maintains high algal abundance and subsequently low Secchi depth during what was previously the clear water phase. This interaction of nutrient loading and Lake Yojoa's monomictic stratification regime illustrates a key phenomenon in how physical water column structure and nutrients interact in tropical monomictic lakes. This work highlights the need to consider nutrient dynamics of warm anoxic hypolimnions, not just surface water nutrient concentrations, to understand environmental change in these societally important but understudied ecosystems.   (for more recent years of data collection see additional zenodo repositories by Fadum and/or Hall)

opencc-by-4.0Jul 2024View details →

Data and Code to Accompany: "On-Body Textile Hysteresis Estimation for Personalized Physical Human-Robot Interaction"

Data files, Matlab code, and figures to accompany "On-Body Textile Hysteresis Estimation for Personalized Physical Human-Robot Interaction" (submitted for peer-review on 7/20/2024).

opencc-by-4.0Jul 2021View details →

A Physical Human-Robot Interaction Dataset - TacAct

This dataset is the supplementary material of the IROS 2021 article "Organization and Understanding of a Tactile Information Dataset TacAct During Physical Human-Robot Interactions". The dataset was collected by a flexible supercapacitor tactile sensor installed in an imitated mechanical arm device. In a 32 × 32 grid, the sensor data can be sampled at 100 Hz (100 frames per second).  A total of 12 touch actions, namely, pull, squeeze, push, hold, grasp, poke, static drag, strong hit, soft slide, scratch, soft tap, and sliding drag, were recorded. A single-action collected from a subject consists of a 32×32×N matrix (where N is the number of frames or frame length). For the same action, all subjects were asked to use as many postures as possible to apply different forces to different positions of the sensor, and repeat 20 times with each hand. Except for strongly hit and soft tap (complete in an instant, acquisition time 2 s), the duration of each action is 2 s, and the time is 4 s altogether. The experiment was conducted on 50 subjects (36 males and 14 females,ranged from 22 to 36 years and 44 were right-handed) in total, each subject consists of 480 actions (12 actions × 40 repetitions ) in total, and the dataset collected 24,000 actions from the subjects.

opencc-by-4.0Dec 2021View details →

Simple Physical Interactions Yield Social Self-Organization in Honeybees - datasets

Empirical data of the location of the bees in complex thermal environments in specific time intervals. For more details please refer to  Szopek M, Stokanic V, Radspieler G and Schmickl T (2021) Simple Physical Interactions Yield Social Self-Organization in Honeybees. Front. Phys. 9:670317. doi: 10.3389/fphy.2021.670317   exp_1.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 9 repetitions of Experiment 1 (static thermal environment with one global optimum at 36°C and a pessimum at 30°C). exp_2.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 8 repetitions of Experiment 2 (static thermal environment with one global optimum at 36°C and one local optimum at 32°C). exp_3.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 6 repetitions of Experiment 3 (static thermal environment with two equal optima of 36°C). exp_4.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 105 min) for each of the 17 repetitions of Experiment 4 (dynamic thermal environment). exp_5.csv contains the percentage of bees in the left, the center and the right evaluation zone at minute 30 for i) each of the 10 repetitions of Experiment 5 (static thermal environment with social stimulus) and ii) for each of the 8 repetitions of an experiment with the same thermal environment but without a social stimulus for comparison.

opencc-by-4.0Feb 2022View details →

PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions

Training, test datasets of the paper "PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions".

opencc-by-4.0Oct 2021View details →

Physical Interaction Detection

This folder contains data collected from a robot physically interacting with a human or the environment. The data has been used for the development, training, and validation of methods for detecting discrete changes in either human intent or environmental contact.   Each folder contains a description of the test conditions under which the data was collected, and an explanation of the data format.

dryad36/100

Long-term spatially-replicated data show no physical cost to a benefactor species in a facilitative plant-plant interaction

Facilitation is an interaction where one species (the benefactor) positively impacts another (the beneficiary). However, the reciprocal effects of beneficiaries on their benefactors are typically only documented using short-term datasets. We use Azorella selago, a cushion plant species and benefactor, and a co-occurring grass species, Agrostis magellanica, on sub-Antarctic Marion Island, comparing cushion plants and the grasses growing on them over a 13-year period using a correlative approach. We additionally compare the feedback effect of A. magellanica on A. selago identified using our long-term dataset with data collected from a single time period. We hypothesized that A. selago size and vitality would be negatively affected by A. magellanica cover and that the effect of A. magellanica on A. selago would become more negative with increasing beneficiary cover and abiotic-severity, due to, e.g., more intense competition for resources. We additionally hypothesized that A. magellanica cover would increase more on cushion plants with greater dead stem cover, since dead stems do not inhibit grass colonization or growth. The relationship between A. magellanica cover and A. selago size and vitality was not significant in the long-term dataset, and the feedback effect of A. magellanica on A. selago did not vary significantly with altitude or aspect; however, data from a single time period did not consistently identify this same lack of correlation. Moreover, A. selago dead stem cover was not significantly related to an increase in A. magellanica cover over the long term; however, we observed contrasting results from short-term datasets. Long-term datasets may, therefore, be more robust (and practical) for assessing beneficiary feedback effects than conventional approaches, particularly when benefactors are slow-growing. For the first time using a long-term dataset, we show a lack of physical cost to a benefactor species in a facilitative interaction, in contrast to the majority of short-term studies.

opencc-zeroNov 2022View details →

opencc-by-4.0Feb 2023View details →

Surfactant Proteins SP-B and SP-C in Pulmonary Surfactant Monolayers: Physical Properties Controlled by Specific Protein–Lipid Interactions - Simulation dataset

Simulation data related to the article: Liekkinen, J., Olzynska, A., Cwiklik, L., Bernardino de la Serna, J., Vattulainen, I., & Javanainen, M. Surfactant Proteins SP-B and SP-C in Pulmonary Surfactant Monolayers: Physical Properties Controlled by Specific Protein–Lipid Interactions. bioRxiv, pp.2022-12. (Pre-print available at https://doi.org/10.1101/2022.12.12.520108)   DPPC/POPC/POPG/CHL1 (60/20/10/10 mol-%) monolayers simulated in non-equilibrium conditions. The monolayer is compressed or expanded at a constant rate at different temperatures. Surfactant proteins SP-B and SP-C are included in the monolayers for comparison. The Charmm36 lipid model [1] is used together with the 4-point OPC water model [2]. Each system consist of two monolayers with 169 lipids each. The monolayers are separated by a water slab, and surrounded by vacuum. All trajectories are simulated for 5000 ns with Gromacs 5.1.x [3] using the default Charmm36 monolayer simulations parameters given in the mdp file. Topology (.top) and index (.ndx) files are also included. The topologies (.itp) for the lipids can be obtained from Charmm-GUI and for the OPC water model from https://bioinformatics.cs.vt.edu/~izadi/ [1] DOI: 10.1021/jp101759q [2] DOI: 10.1021/jz501780a [3] DOI: 10.1016/j.softx.2015.06.001

dryad36/100

Long-term spatially-replicated data show no physical cost to a benefactor species in a facilitative plant-plant interaction

Open the record for dataset details and reuse information.

publicDec 2022View details →

Interactive Tool to Support Self-management Through Lifestyle Feedback, Aimed at Physical Activity of COPD/DM Patients

ClinicalTrials.gov study NCT01867970. IPD Sharing: Not stated. Countries: 1. Publications: 2.

Interactions Between Physical Activity and Cannabis Use in Adults

ClinicalTrials.gov study NCT00838448. IPD Sharing: Not stated. Countries: 1. Publications: 2.

The Interactive Physical and Cognitive Exercise System

ClinicalTrials.gov study NCT03069391. IPD Sharing: NO. Countries: 1. Publications: 2.

Interaction of Inflammation, Taste Perception and Preferences As a Function of Physical Activity and Body Composition

ClinicalTrials.gov study NCT06384365. IPD Sharing: NO. Countries: 1. Publications: 0.

Long-Term Impact of COVID-19 Restrictions on Physical Activity and Social Interactions in Young Adults

ClinicalTrials.gov study NCT06135623. IPD Sharing: NO. Countries: 1. Publications: 3.

Increasing Physical Activity in Stroke Survivors Using STARFISH, an Interactive Mobile Phone App

ClinicalTrials.gov study NCT02494245. IPD Sharing: Not stated. Countries: 1. Publications: 9.

Effects of an Adapted Physical Activity With an Interactive Gaming Platform on Fear of Falling and Risk of Falling

ClinicalTrials.gov study NCT04134988. IPD Sharing: NO. Countries: 1. Publications: 7.

The Interaction Between Metformin and Physical Training

ClinicalTrials.gov study NCT03316690. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.