Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

Data for Development of LOS Analysis Procedures and Performance Measurement Systems for Parking (UTEP Year 4 Project)

These Excel files consists of the data in the tables and figures in the final report. They appear in Chapters 3 to 6 in the final report. Each file is for one chapter. Each table or figure is listed as an Excel worksheet. The name of the worksheet is the figure or table number in the final report.

Data and R script for "Fear and cultural background drive sexual prejudice in France – A sentiment analysis approach"

Data: corpus_integral.csv FEEL_1.csv mauvais.txt neg_hetero_corrected.txt participant_info_used.txt pos_hetero_corrected.txt R script: polarities.R sentiments_discrete.R

Data from: Combined analysis of micro RNA and proteomic profiles and interactions in patients with primary lung adenocarcinoma and lung adenocarcinoma brain metastases

We carried out an analysis of miRNAs expression profiles and protein spectrums of non-metastatic primary lung adenocarcinoma (LP) and patients with brain metastases (BM) to better explore the molecular basis of BM. Files containing raw data of miRNA expression and proteomic profiles in the manuscript "Combined analysis of micro RNA and proteomic profiles and interactions in patients with primary lung adenocarcinoma and lung adenocarcinoma brain metastases".

Data and data analysis codes for Palacios et al. "Single-domain Bose condensate magnetometer achieves energy resolution per bandwidth below ℏ"

Data and data analysis codes for the article "Single-domain Bose condensate magnetometer achieves energy resolution per bandwidth below ℏ" by S. Palacios, et al.  https://arxiv.org/abs/2108.11716

Data, code, and manual for analysis of image heterogeneity in mass spectrometric imaging.

This upload contains all replication material for "The software for interactive evaluation of mass spectrometric imaging heterogeneity" (forthcoming). Authors: E.S. Zhvansky, E.V. Zhdanova, M.S. Belenikin, M.A. Shamraeva, S.V. Silkin, K.V. Bocharov, A.A. Sorokin. Code, manual, and data are located within Interactive_CSMM.zip. Code is written MATLAB R2019b and Python 3.5.2. Please find the README.md for code using and the code to replicate the main findings of the paper described below: <ul> <li>imzml2mat.py in conversion directory for file conversion.</li> <li>CSMM.m for application start.</li> <li>40TopL,10TopR,30BottomL,20BottomR-centroid.mat - data in conversion/Data directory for reproducing the results.</li> <li>User_manual.pdf - manual.</li> <li>mp4 files - screencasts.</li> </ul>

openother-openDec 2021View details →

Data and R files for the analysis of the innovative capacity and the network position of national manufacturing industries in world production

Data and R files for the reproducibility of the results obtained in Kim and Ozaygen, Analysis of the innovative capacity and the network position of national manufacturing industries in world production. It also includes an R/Shiny application which runs at <a href="https://awekim.shinyapps.io/Manuf_shiny_R/">https://awekim.shinyapps.io/Manuf_shiny_R/ </a>

RAW data for Correlation analysis of vibration modes in physical vapour deposited Bi2Se3 thin films probed by the Raman mapping technique

Raw data for the "Correlation analysis of vibration modes in physical vapour deposited Bi2Se3 thin films probed by the Raman mapping technique" paper.

Data Regarding Classification of Infrasonic Atmospheric Events Using Electromagnetic Pulse Analysis

<div>The following data files were used for the analysis presented in the paper </div> <div>"Classification of Infrasonic Atmospheric Events Using Electromagnetic Pulse Analysis"</div> <div> </div> <div>The files include details of the infrasonicly detected evnents, and features extracted from electromagnetic signals, as explined in the README file.</div>

opencc-zeroDec 2021View details →

dryad36/100

Data from: Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data

Aim: Animal movement determines home range patterns, which in turn affect individual fitness, population dynamics and ecosystem functioning. Using temperate bats, a group of particular conservation concern, we investigated how morphological traits, habitat specialization and environmental variables affect home range sizes and daily foraging movements, using a compilation of 30 years of published bat telemetry data. Location: Northern America and Europe. Time period: 1988 – 2016. Major taxa studied: Bats. Methods: We compiled data on home range size and mean daily distance between roosts and foraging areas at both colony and individual levels from 166 studies of 3,129 radiotracked individuals of 49 bat species. We calculated multi-scale habitat composition and configuration in the surrounding landscapes of the 165 studied roosts. Using mixed models, we examined the effects of habitat availability and spatial arrangement on bat movements, while accounting for body mass, aspect ratio, wing loading and habitat specialization. Results: We found a significant effect of landscape composition on home range size and mean daily distance at both colony and individual levels. On average, home ranges were up to 42% smaller in the most habitat-diversified landscapes while mean daily distances were up to 30% shorter in the most forested landscapes. Bat home range size significantly increased with body mass, wing aspect ratio and wing loading, and decreased with habitat specialization. Main conclusions: Promoting bat movements through the landscape surrounding roosts at large spatial scales is crucial for bat conservation. Forest loss and overall landscape homogenization lead temperate bats to fly farther to meet their ecological requirements, by increasing home range sizes and daily foraging distances. Both processes might be more detrimental for smaller, habitat-specialized bats, less able to travel increasingly longer distances to meet their diverse needs.

openapache2.0Dec 2021View details →

Tomography data for interparticle contact detection analysis in spheroidal granular packings

This collection contains a series of synchrotron XCT scans on a hexagonal close-packed arrangement of soda-glass pellets. The field of view (FOV) diameter is 68.9 mm in diameter, approximately, and the nominal individual pellet diameter is 10 mm. The detector pixel size is 21 microns for all scans. The pellets were arranged in three horizontal lattices (layers). The middle and top lattices were separated by a layer of polyethylene film (cling film), while the bottom and middle layer were fully-contacting. Each file corresponds to a scan of either the bottom contacting or top non-contacting lattice pair. Thus, each filename includes a 'top' and 'bot' identifier.  Acquisition parameters (number of projections, exposure time per projection, rotation range and sample position) were varied to achieve different image qualities and are included in 'README.txt'. All but scan A5 were local scans; scan A5 is a full-field scan acquired using the 'half-acquisition' method.  Tomographic reconstruction was carried out using filtered back-projection in Savu. After reconstruction, a 3D median filter (kernel size = 2) and an anisotropic diffusion filter (diffusion threshold = 100; iterations = 2) were used to reduce noise. Data was acquired using Beamline I12-JEEP at Diamond Light Source (proposal NT26307-1). Please read README.txt Copyright 2021 Diamond Light Source Ltd. Licensed under the Apache License, Version 2.0.

dryad36/100

Data from: The effects of human-altered habitat spatial pattern on frugivory and seed dispersal: a global meta-analysis

Seed dispersal by frugivorous animals is important for plant mobility, regeneration, and persistence. Human-caused landscape change is thought to disrupt seed dispersal, but evidence is scarce. We performed a comprehensive meta-analysis on the effects of habitat spatial pattern on frugivory and seed dispersal. We found 233 effects from 71 studies. At a patch or local scale, altered habitat spatial pattern was measured as declining patch size, increasing patch isolation, or habitat edge (vs. interior). At a landscape scale it was measured as declining amount of habitat, increasing mean patch isolation, increasing number of patches, or increasing habitat edge in the landscape. We found overall negative effects of altered habitat spatial pattern on: (i) the quantity of frugivory or seed dispersal, (ii) the number of species involved in a plant-frugivore interaction, and (iii) seed dispersal distance. Moderator variable analysis was only possible for the first of these. It revealed negative responses of the quantity of frugivory or seed dispersal to habitat loss at both the local scale (declining patch size), and the landscape scale (declining habitat amount), but little evidence for a response to habitat edge at either scale. In addition, altered habitat spatial pattern reduced the quantity of frugivory or seed dispersal more strongly in temperate than tropical areas. Finally, the few-recorded effects of landscape-scale fragmentation per se (increasing patch density or edge density) on the quantity of frugivory or seed dispersal were mixed and weak. Our meta-analysis reinforces the notion that habitat loss is a major threat to frugivory and seed dispersal by animals, and reveals an insufficiency of studies of the effects of habitat fragmentation per se. Thus, based on the current literature, we conclude that maintaining and increasing habitat amount is vital for maintaining seed dispersal by frugivorous animals.

opencc-zeroDec 2021View details →

Data for "Meta-analysis of induced anti-herbivore defence traits in plants from 647 manipulative experiments with natural and simulated herbivory"

Data used in analysis in "Meta-analysis of induced anti-herbivore defence traits in plants from 647 manipulative experiments with natural and simulated herbivory" in Journal of Ecology.  Code used for analysis are included as Supplementary Material of the main article.

Scale growth data for analysis in Vollset et al. 2021

Scale samples of adult Atlantic salmon were compiled from 180 rivers in Norway, which comprise almost half of all Atlantic salmon rivers in Norway, during 1989-2016. The scales were collected by anglers when sport fishing in the various rivers. The back-calculation of fish length is based on the linear relationship between fish length and the diameter of the fish scale from the centre of the scale to the edge . We have focused on the average growth across four large geographic regions: South-western, south-western, middle-, and north Norway. In total, 52 371 Atlantic salmon were sampled by fishers. For each individual fish, the following data were used: length at capture (cm), back-calculated freshwater growth (cm), back-calculated early marine growth (cm), and smolt year, which is the year they migrated from the rivers as juveniles to the sea. Freshwater growth was defined as the distance from the centre of the scale to the end of the freshwater zone in the scale. Early marine growth, also termed post-smolt growth, was measured as the distance between the end of the freshwater zone and the end of the first winter at sea. Growth estimates from scales may be impacted by the size of the fish when they leave the rivers and enter the sea. To correct for this error, a relative growth estimate can be used where the early marine growth is divided by the freshwater growth of the individual, which we refer to as relative growth. Also, the timing of out-migration will vary by up to two months from north to south in Norway, meaning that the time to grow before winter is shorter for northern populations. Putative monthly growth estimates indicate that growth is limited from November, and we therefore calculate growth from day of out-migration to 31 October. Day of out migration was calculated according to using a model that explains out-migration data based on latitude, longitude, air temperature during winter, and river discharge. The estimated growth is called relative growth per day.

Data from: Size and Causes of the Shadow Economy in Iran: An Analysis with the MIMIC Approach

All the data needed (in raw form) in order to replicate the results of the paper “Size and Causes of the Shadow Economy in Iran: An Analysis with the MIMIC Approach” is available here. Information on how to use this data to produce the results is provided within the paper and its appendices. All the data gathered here are taken from public sources, namely the “World Bank” (World Development Indicators data), “Central Bank of the Islamic Republic of Iran”, “Plan and Budget Organization of the Islamic Republic of Iran” and “Statistical Center of Iran”.

opencc-zeroJan 2022View details →

dryad36/100

Data from: Analysis of the PEBP gene family and identification of a novel FLOWERING LOCUS T orthologue in sugarcane

Sugarcane (Saccharum spp.) is an important economic crop for both sugar and biomass, the yields of which are negatively affected by flowering. The molecular mechanisms controlling flowering in sugarcane are nevertheless poorly understood. RNA-seq data analysis and database searches have enabled a comprehensive description of the PEBP gene family in sugarcane. It is shown to consist of at least 13 FLOWERING LOCUS T (FT)-like genes, two MOTHER OF FT AND TFL (MFT)-like genes, and four TERMINAL FLOWER (TFL)-like genes. As expected, these genes all show very high homology to their corresponding genes in Sorghum, and also to FT-like, MFT-like, and TFL-like genes in maize, rice, and Arabidopsis. Functional analysis in Arabidopsis showed that the sugarcane ScFT3 gene can rescue the late flowering phenotype of the Arabidopsis ft-10 mutant, whereas ScFT5 cannot. High expression levels of ScFT3 in leaves of short day-induced sugarcane plants coincided with initial stages of floral induction in the shoot apical meristem as shown by histological analysis of meristem dissections. This suggests that ScFT3 is likely to play a role in floral induction in sugarcane; however, other sugarcane FT-like genes may also be involved in the flowering process.

Data and analysis code for Sauer et al., 2022

Position and spiking data along with code for the analysis of spatial tuning properties and position decoding.

Data for: Optimizing broad ion beam polishing of zircaloy-4 for electron backscatter diffraction analysis

This is the data to support a manuscript that explores how to optimize sample preparation of zircaloy-4 using broad ion beam polishing. If you wish to follow-up on this data, please contact Dr Ben Britton (ben.britton@ubc.ca). The data was collected and curated by Ning Fang and Ruth Birch.

GRAND-SLAM analysis of SARS-CoV-2 data from Finkel et al., Nature 2021 (https://www.nature.com/articles/s41586-021-03610-3)

This is the processed SLAM-seq data from Finkel et al., Nature 2021 (https://www.nature.com/articles/s41586-021-03610-3). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The file sars.tsv.gz is the GRAND-SLAM output table.   To generate the GRAND-SLAM output yourself, first <a href="https://github.com/erhard-lab/gedi/wiki/Preparing-genomes">prepare</a> the human (ensembl v90) and the SARS-CoV-2 genome (NC_045512). Then run: <pre><code class="language-bash">gedi -e Slam -trim5p 15 -reads sars.cit -genomic h.ens90 SARS-CoV2 -prefix grandslam_t15/sars -plot -progress </code></pre> To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it. You can also start from scratch (i.e., the json file): <ol> <li><a href="https://github.com/erhard-lab/gedi/wiki/Preparing-genomes">Prepare</a> the human genome (ensembl v90), the SARS-CoV-2 genome (NC_045512), the human rRNA sequence (U13369.1), and the Mycoplasma hominis sequence</li> <li>Prepare the joint STAR index for the human and virus genome by calling gedi -e GenomicUtils -p -m star -g h.ens90 SARS-CoV2</li> <li>Modify the starindex entry in the json file to match your file system</li> <li>Run: gedi -e Pipeline -r parallel -j sars.json rnaseq_mapping.sh report.sh grandslam.sh</li> </ol> Software versions: <ul> <li>gedi toolkit 1.0.4</li> <li>GRAND-SLAM 2.0.7</li> <li>cutadapt 3.4</li> <li>Bowtie 2 version 2.3.0</li> <li>STAR version 2.5.3a</li> </ul>

Input data for Differential NicheNet analysis performed in the liver atlas paper Guilliams et al., Cell 2022

Input data for Differential NicheNet analysis performed in the liver atlas paper Guilliams et al., Cell 2022 See https://github.com/saeyslab/NicheNet_LiverCellAtlas and https://www.sciencedirect.com/science/article/pii/S0092867421014811

Mass spectrometry proteomics data obtained from analysis of the secretome of Anisakis simplex (sensu stricto) L3 larvae.

Mass spectrometry proteomics data obtained from analysis of the secretome of Anisakis simplex (sensu stricto) L3 larvae.