Find research datasets worth reusing

Data and Analysis Scripts for "Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds"

This document contains the raw data and analysis scripts for the paper "Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds", including Drosophila melanogaster circling and handedness behavior at multiple timepoints and across genotypes and experimental conditions manipulating serotonin. It also contains code used to run ecological simulations in the paper and the results of those simulations, as well as code to generate figures for the paper.

Data Workbook - Ex-Situ Geoheritage Case Study: Quantitative and Qualitative Analysis of the Uppsala University Museum of Evolution Collections

Data Workbook for Thesis. Ex-Situ Geoheritage Case Study: Quantitative and Qualitative Analysis of the Uppsala University Museum of Evolution Collections.  Includes; Images, Conservation Results, Inventory, Valuation Grades, RStudio Results

Educational data collected from parents - regarding the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)

The responses of the 784 parents were collected through the questionnaire available at: <a href="https://forms.gle/Km8WE5QamrYYgXJi7" target="_new" rel="noopener">https://forms.gle/Km8WE5QamrYYgXJi7</a> It was designed with various types of responses, including binomial (yes/no), polynomial (multiple options), and open-ended responses, to capture a comprehensive range of data. This combined approach allows for both quantitative analysis of fixed-response questions and qualitative insights from open-ended questions. Patterns, correlations, and differences between various demographic groups and their experiences and attitudes toward online education can be identified. To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.

Social-ecological dynamics of quarry restoration: a Flickr data analysis

<div> <div> <div> With increasing urbanization and demand for construction materials, quarries have become central to the recovery of degraded landscapes into spaces that offer ecological, but also social benefits. While ecological restoration has long been investigated, integrated social-ecological restoration of post-mining landscapes remains underexplored. The overall aim of this study is to assess the perceptions of cultural ecosystem services and landscape features expressed in social media posts about quarries in Germany, Denmark, and the Czech Republic. We focus on concepts of cultural ecosystem services and landscape features to investigate the interactions between humans and restored ecosystems. Using a mixed-methods approach, we analyzed 1,660 geotagged photographs from 50 quarries across three regions: Berlin, Roskilde, and the Czech Karst. Flickr social media images were analyzed to elicit the richness of cultural ecosystem services (CES) and landscape features (LF), highlighting popular quarries and their social-ecological significance. Our results indicate that rehabilitated quarries exhibit higher CES richness than abandoned or operational ones, and that accessibility significantly influences public engagement. Our study demonstrates that once primarily industrial sites, quarries can evolve into vibrant social-ecological systems that provide diverse landscape features and cultural ecosystem services. It also points to the potential of social media data for designing restoration efforts from a social-ecological perspective. Such an approach provides insights into public perceptions of restored landscapes and may inform future restoration strategies.   This dataset includes: (1) the review protocol, (2) a list of place names used for data collection on Flickr when posts were not geolocated, (3) data on landscape features and cultural ecosystem services identified in Flickr posts from 50 study quarries, (4) characteristics of the quarries, and (5) a shapefile of the quarry polygons.     </div> </div> </div>

opencc-by-sa-4.0Sep 2024View details →

Data for manuscript: "Understanding lower limb haemodynamics: sensitivity analysis of a 0D model"

Open the record for dataset details and reuse information.

Raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using 1H HR MAS NMR Spectroscopy "

The folder contains raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using 1H HR MAS NMR Spectroscopy ".    Raw data measured on Bruker Avance III 400 MHz NMR spectrometer: - 1D 1H HR MAS NMR spectra (path: Patient_code – Sample_code/500/fid) - 2D 1H-1H J-resolved HR MAS NMR spectra (path: Patient_code – Sample_code/600/ser).   Metadata is included in Metadata.xlsx file. Each sample is described with the following parameters: - patient code (after anonymization), - sample code (the label l or r denotes the left or right ovary in patients from whom samples were obtained bilaterally), - sample weight, - clinic-pathological parameters (such as: age, BMI, menopausal status, diagnosis, FIGO stage), - percentage tissue content obtained from histopathological analysis after HR MAS NMR studies (cancer cells, epithelial compartment within benign tumors, necrosis, inflammation, fibrosis, calcification, normal ovary, vessels, fatty tissue).   Some samples were considered representative of particular tissue components: - cancer (HGSOC) compartment, - fibrotic stroma within malignant  (HGSOC) tumors, - fibrotic stroma within benign tumors, - normal ovary tissue (the samples collected from the control group), - normal ovary tissue (the samples collected from the cancer patients), - necrosis, - non-tumoral fibrous tissue / fibrous tumor capsule (obtained from the patients with benign non-neoplastic lesions) - corpus albicans The assignment of the samples to these categories is indicated in the column Tissue components.   The samples classified as outliers in PCA model 1 are indicated in the column Outliers. The samples included in multivariate models are indicted in the columns: PCA 2, PCA 3, PCA 4, PCA 5, PCA 5a, PCA 6, OPLS-DA 1, OPLS-DA 2, OPLS-DA 3, OPLS-DA 4, OPLS-DA 5, OPLS-DA 6 and OPLSR.

Physiological Data Collected from smartwatch: EDA, Pulse Rate, and Skin Temperature for Stress and Fatigue Analysis

The dataset contains multiple columns capturing both physiological and demographic data. Physiological data, collected using the Empatica EmbracePlus smartwatch, includes electrodermal activity (EDA), pulse rate, and skin temperature. These metrics provide insights into participants' stress and fatigue levels. Empatica's proprietary algorithms preprocess the raw data, extracting digital biomarkers and metrics that reflect the wearer's physiological and behavioral states. The processed data is aggregated on a per-minute basis. Demographic information, such as age, gender, fitness level, and sleep duration from the previous night, is also included. Additionally, participants rated their perceived physical fatigue on the Borg scale (ranging from 6 to 20), offering a subjective measure of exertion during or after physical tasks. The dataset was collected during controlled simulations of industrial tasks in a fitness environment. These simulations involved repetitive activities, including weightlifting, resistance band exercises, and isometric tasks, designed to mimic the physical demands of industrial work. This approach allowed for the safe and effective study of physical fatigue. The resulting data provides valuable insights into the physiological responses associated with repetitive physical labor.

opencc-by-4.0Jan 2024View details →

Data and analysis and plotting scripts for Swaminathan et al., "Regional Impacts Poorly Constrained by Climate Sensitivity"

The datasets included here are of the plotted data from the figures of the paper entitled "Regional Impacts Poorly Constrained by Climate Sensitivity", by Ranjini Swaminathan, Jacob Schewe, Jeremy Walton, Klaus Zimmermann, Colin Jones, Richard A. Betts, Chantelle Burton, Chris D. Jones, Matthias Mengel, Christopher Reyer, Andrew G. Turner & Katja Weigel, submitted for publication in Earth's Futures.  Scripts used for plotting and analysis are also included.

opencc-by-4.0Aug 2024View details →

V 1.0 Dataset for "Emergence and Evolution of Big Data Research: A 30-year (1993-2022) Scientometric Analysis of The Knowledge Field"

This dataset includes the bibliometric data used in the scientometric analysis of the field of big data research over a 30-year period (1993-2022). The data was collected from the Scopus database, and contains information on 70,163 articles and 315,235 author keywords. The dataset is structured by 17 interrelated data categories that trace the conceptual emergence and evolution of the big data field, focusing on keyword co-occurrences, disciplinary distributions, and the temporal growth of publications. This dataset supports the analyses presented in the related manuscript.

Data and analysis code for Repo et al., "Contrasting forest management strategies: impacts on biodiversity and ecosystem services under changing climate and disturbance regimes"

This repository contains analysis code and pre-processed data for the study "Contrasting forest management strategies: impacts on biodiversity and ecosystem services under changing climate and disturbance regimes" by Repo et al. Data processing and analysis mainly done by Aapo Jantunen, Katharina Albrich Due to respository space limitations, the original model outputs are archived in the Finnish "Allas" data storage service. For access, contact katharina.albrich@luke.fi The code used to process the raw data is included here for reproducibility. If you are interested in using iLand, visit https://iland-model.org/ and https://iland-model.org/iland-book/ for information on using the model and a guide to setting up a landscape. This work was supported by the Ministry of Agriculture and Forestry by funding project Future multifunctional forests and their disturbance risk in the changing climate (Foster) through the “Catch the Carbon” initiative (project number VN/28654/2020). A.R. has been supported by the grant [TRACY Trade-offs and synergies in land-based climate change mitigation and biodiversity conservation decision 322066 by the Academy of Finland.], J. H by the grant [CASCADE - Changing Disturbance Regimes and Forest Landscapes of Fennoscandia 342569 by the Academy of Finland].

Data and analysis code for Zhou et al. 2024, Global Change Biology

This dataset accompanies the paper: Zhou, J., Zhu, P., Kluger, D.M., Lobell, D.B., and Jin, Z. 2024. Changes in the yield effect of the preceding crop in the US Corn Belt under a warming climate. Global Change Biology

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Phylogenetic analysis and revision of the leafhopper genus Acuera DeLong & Freytag (Hemiptera: Cicadellidae: Gyponini) based on morphological data.

Natural history specimen data linked to collectors and determiners held within, "Phylogenetic analysis and revision of the leafhopper genus Acuera DeLong & Freytag (Hemiptera: Cicadellidae: Gyponini) based on morphological data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6ba6d2ca-5dad-4270-914f-1042327d503c">https://bionomia.net/dataset/6ba6d2ca-5dad-4270-914f-1042327d503c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6ba6d2ca-5dad-4270-914f-1042327d503c">https://gbif.org/dataset/6ba6d2ca-5dad-4270-914f-1042327d503c</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Stolonochloa, a new Australian genus segregated from Panicum (Poaceae: Panicoideae: Paniceae: Boivinellinae) based on phenetic analysis of morphological data.

Natural history specimen data linked to collectors and determiners held within, "Stolonochloa, a new Australian genus segregated from Panicum (Poaceae: Panicoideae: Paniceae: Boivinellinae) based on phenetic analysis of morphological data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706">https://bionomia.net/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706">https://gbif.org/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706</a>. Formatted as a Frictionless Data package.

CS#1H Monitoring Data of Subsurface Passage in Managed Aquifer Recharge: Microbial and Organic Composition Analysis

<div>This dataset contains flow cytometry and cultivation-based microbial data, along with measurements of natural organic matter (NOM) characterized by fluorescence, absorbance, and liquid chromatography-organic carbon detection (LC-OCD) from CS#1H. Data were collected over a one-year monitoring period at two sampling locations: the infiltration ditch and the abstraction well. </div>

opencc-by-4.0Aug 2024View details →

uncropped western blots for analysis of RPN13 ubiquitylation and NRF1 activation by protein aggregates, as well as source data for qPCR plots and flow cytometry gating and FCS files for agDD-GFP in HeLa or HEK cells

This entry contains uncropped blots for Fig 4D and Fig S4C, Fig. 5B, Fig S5 and Fig S6, and the raw FCS files for Flow Cytometry data in doi.org/10.1101/2024.08.30.610524.

opencc-by-4.0Jun 2021View details →

Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Public and Stakeholder Engagement

This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project. Aim: Alignment of RRI to advance the UN SDGs. Objectives: <ul> <li>Mapping the RSSR to the SDGs </li> </ul> Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable. The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders. Two documents were used for this task: <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>

opencc-by-4.0Jun 2021View details →

Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Ethics and Ethical Governance

opencc-by-4.0Nov 2020View details →

Data analysis of an LC-MS dataset from a human urine biofluid cohort study

Supplementary dataset and tutorials for the "Statistical analysis in metabolic phenotyping"   This repository contains Jupyter Notebooks with two examplar metabolomic data analysis workflows, applied to a liquid chromatography mass spectrometry dataset (LC-MS). The LC-MS dataset used comes from a metabolic phenotyping investigation of human urine biofluid samples from a dementia cohort. In this sample set, baseline spot urine samples (first sample collected after recruitment to the study) were collected as part of the AddNeuroMed1 and ART/DCR study consortia, with the aim of identifying biomarkers of neurocognitive decline and Alzheimer’s disease. These samples were analysed by LC-MS and 1H NMR, using the methods described by Lewis et al2 and Dona et al. Detailed information about this cohort and other available phenotypic measurements can be found in Lovestone and the ANMERGE3 repository, which can be accessed via the Sage BioNetworks portal (<a href="https://doi.org/10.7303/syn22252881">https://doi.org/10.7303/syn22252881</a>). Information about the metabolic profiling experiments can be found in the study's MetaboLights entry: <a href="https://www.ebi.ac.uk/metabolights/MTBLS719">https://www.ebi.ac.uk/metabolights/MTBLS719</a>.   1.           Lovestone, S. et al. AddNeuroMed - The european collaboration for the discovery of novel biomarkers for alzheimer’s disease. in Annals of the New York Academy of Sciences (2009). doi:10.1111/j.1749-6632.2009.05064.x 2.           Lewis, M. R. et al. Development and Application of UPLC-ToF MS for Precision Large Scale Urinary Metabolic Phenotyping. Anal. Chem. 88, acs.analchem.6b01481 (2016). 3.           Birkenbihl, C. et al. ANMerge: A comprehensive and accessible Alzheimer’s disease patient-level dataset. medRxiv (2020). doi:10.1101/2020.08.04.20168229

dryad40/100

Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis

Background RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. Results With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments. Conclusion RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

opencc-zeroJul 2021View details →