Find research datasets worth reusing

opencc-by-4.0Oct 2021View details →

Data for the research article: "Simulations of Energetic Neutral Atom sputtering from Ganymede in preparation for the JUICE mission"

Data for the research article: "Simulations of Energetic Neutral Atom sputtering from Ganymede in preparation for the JUICE mission"

opengpl-2.0-or-laterNov 2021View details →

Data and codes related to the article: Renard et al. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research.

This package contains data and codes related to the article: B. Renard, M. Thyer, D. McInerney, D. Kavetski, M. Leonard and S. Westra.  A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research. R scripts The main computations of the paper have been performed using a computing code named <a href="https://github.com/STooDs-tools">STooDs</a>, which is called using the bash script launchpad.sh. The R scripts in this package only perform pre-processing (create configuration files) and post-processing (analyze results) steps. <ul> <li>Funk.R: a set of functions called by other scripts.</li> <li>1_defineModel.R: define the model to be inferred and create STooDs configuration files in dataset_XXX/runs.</li> <li>2_analyzeResults.R: analyze the outputs of STooDs runs.</li> <li>3_crossValidation.R: analyze the outputs of cross-validation experiments in dataset_XV and dataset_XV_1971-1990.</li> </ul> Data Data for the 3 cases (full dataset and 2 cross-validation experiments) are located in folders dataset_XXX/data. <ul> <li>dat.txt: raw dataset in text format.</li> <li>dataset.RData: dataset in RData format.</li> <li>DMI.txt, NINO.txt, SAM.txt: 3 standard climate indices.</li> <li>spaceP.txt, spaceQ.txt, spaceT.txt: properties of Precipitation (P), Streamflow (Q) and Temperature (T) stations.</li> <li>[only for cross-validation experiments] validation.RData: left-out data used for validation.</li> </ul>

opencc-by-4.0Nov 2021View details →

Radiative transfer simulations for the Arctic research expedition PS106

The set of data files contain the results of radiative transfer simulations for the Arctic research expedition PS106. The simulations are based on remote sensing observations conducted during the PS106 cruise in 2017, which were used synergistically with Cloudnet algorithm to derive macro and microphysical properties of clouds. Moreover, atmospheric profiles of temperature, pressure, and ozone from ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) and values of surface albedo from CERES (Clouds and the Earth's Radiant Energy System) SYN1deg Ed. 4.1 were considered.

Survey on Developer and Researcher Views on the Ethics of Experiments on Open-Source Projects

Results of a survey of 180 GitHub developers and 44 authors of research papers concerning the ethics of performing experiments on open source projects.

opencc-by-4.0Sep 2021View details →

Research Data Supporting "Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects"

This repository contains the set of data shown in the paper "Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects", published on ACS Nano (DOI: 10.1021/acsnano.1c01398). The data stored in this section is organized as follow: supervisedClustering/ : folder containing scripts for carrying out the supervised analysis as discussed in the main paper. Subfolders are divided in <code>kmeans</code><code>/</code> and <code>spectral</code><code>/</code> contain the different analysis as explained in the paper. unbiasMD/ : folder containing the Gromacs input files for reproducing the unbias MD trajectories of the 3 types of fiber, as explained in the paper. biasMD/ : folder containing the PLUMED input files for reproducing enhanced sampling dynamics as discussed in the paper. unsupervisedClustering/ : folder containing the directions and the original files to reproduce the unsupervised clustering results.

opencc-by-4.0Sep 2021View details →

Research data for "Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure"

This dataset supports the paper: "Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure". The paper is online here: https://doi.org/10.1002/adma.202107515.  The following .xyz and .zip files are provided: <ul> <li>"LDA_structure_final.xyz": the atomic structure of the LDA model generated in this work.</li> <li>"slow_melt_quench.zip": the trajectory of the slow melt-quench process in (extended) XYZ format. </li> <li>"compress_decompress.zip": the trajectory of the ambient-pressure compression and the subsequent decompression processes in (extended) XYZ format. </li> <li>"Structure_factor.zip": atomic structures in (extended) XYZ format at different pressures (used to calculate the structure factors). </li> </ul>

HRE 2021-0515 Research Data: Interview Transcripts

The <a href="https://openknowledge.community/about-coki/">Curtin Open Knowledge Initiative</a> (COKI) in collaboration with <a href="https://library.curtin.edu.au/">Curtin University Library</a> undertook a project to provide better support for the creative practice research outputs (CPROs) of Faculty of Humanities researchers. The reason for this research is to identify ways to increase the visibility of CPROs at Curtin University, and document what information (metadata) is important to researchers when describing their CPROs. The Chief Investigator of this project was Dr Lucy Montgomery, Professor of Knowledge Innovation at Curtin University. This research project has approval from the Curtin University Human Research Ethics Office (HRE2021-0515). This dataset contains deidentified interview transcripts from six interviews with creative practice researchers at Curtin University, Perth,  Australia, who consented to sharing their deidentified data as a publicly available dataset. The participants have been deidentified, and named as P1 to P6. Interviews were conducted between 20/09/2021 and 30/09/2021. Four interviews were in person, and two interviews were online via WebEx. The interview guide is publicly available on Zenodo: <a href="https://doi.org/10.5281/zenodo.5774543">https://doi.org/10.5281/zenodo.5774543</a> The metadata findings from the card sorting activity are available on Zenodo: <a href="https://doi.org/10.5281/zenodo.5774660">https://doi.org/10.5281/zenodo.5774660</a>

Anonymized dataset for "Research themes of family and community physicians in Brazil"

This is the anonymized dataset analyzed in the manuscript “Research themes of family and community physicians in Brazil”. It was derived with the first R script in <a href="https://doi.org/10.5281/zenodo.5798092">https://doi.org/10.5281/zenodo.5798092</a> from restricted datasets in <a href="https://doi.org/10.5281/zenodo.3376310">https://doi.org/10.5281/zenodo.3376310</a> and <a href="https://doi.org/10.5281/zenodo.5797816">https://doi.org/10.5281/zenodo.5797816</a>. Both the data and the data dictionary are recorded in comma-separated values, with byte-order mark.

Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities"

Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities", Applied Energy, 2019

FIG. 1 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys

FIG. 1. — Number of species published by semester from the end of the expedition, with first semester: July-December 2015, and last semester: January-July 2021. The expedition was accomplished in August 2015.

FIG. 4 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys

FIG. 4. —Density map of the shared data from the GBIF portal (https://www.gbif.org) for insects at the scale of the Guiana Shield. Only data with a high occurrence resolution (<1 km) are included.

FIG. 2 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys

FIG. 2. — Number of new (non-marine) animal species of French Guiana's fauna described since 2000. New species collected during the Mitaraka survey are indicated in colour.

FIG. 5 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys

FIG. 5. — Number of holotypes collected according to the different techniques used. Abbreviations: BS, beating sheet; HC, hand collecting; PVB, cross flight intercept trap with a blue LED; PVP, idem, with pink LED; PGL, idem, with GemLight; BPT, blue pan trap; SLAM, sea and land air malaise trap; SW & NS, sweeping & net sweeping; WPT, white pan trap; YPT, yellow pan trap (for a more detailed description of the techniques, see Touroult et al. 2018).

FIG. 6 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys

FIG. 6. — Level of compliance with recommendations for citation of expedition, ABS authorization and deposit of specimens according to two discriminating variables (n = 90 articles).

opencc-by-4.0Nov 2021View details →

The Carbon Footprint of Astronomical Research Infrastructures

Content of the directory This record contains code and data that were used for the paper Knoedlseder, J., et al. Estimate of the carbon footprint of astronomical research infrastructures, Nature Astronomy, in press. Data The file ri-carbon-footprint.xls contains all data that were used for the analysis in the paper. The file is an excel file containing the following tabs: <ul> <li>Description - a description of the excel file</li> <li>Summary - summary of the findings of the carbon footprint estimates</li> <li>Carbon footprint (ground) - Master table for carbon footprint of ground-based observatories</li> <li>Carbon footprint (space) - Master table for carbon footprint of space missions</li> <li>Emission factors - Collection of emission factors used as input to the study</li> <li>Active infrastructures - List of astronomical facilities that were active worldwide in 2019</li> <li>Community - Astronomical community and IAU members for several countries (Ahn, S.H., Economic Power, Population, and Size of Astronomical Community, JKAS, 52, 159 (2019).</li> </ul> Code The record contains three Python scripts. adsquery.py Script to query the ADS database to extract number of publications for a given facility. The script also determines the number of unique authors. It returns global numbers since the start of the mission or observatory operations, and numbers restricted to IRAP. bootstrap.py Script to bootstrap the considered facilities to extrapolate the carbon footprint to all infrastructures that exist worldwide. The script also produces Figure 1 of the paper. carbonintensity.py Script to generate various figures from the excel data, and in particular the carbon intensity Figure 2 of the paper. Running the script requires the "xlrd" Python module that can be installed via conda.

SMA-TB Clinical trial research team's thoughts and opinions

In this video SMA-TB Clinical trial research team members from Georgia and South Africa share their thoughts and opinions regarding the SMA-TB project and its impact both at scientific and personal level.  SMA-TB team comprises of doctors, nurses, laboratory technicians and administrative. This video gives opportunity to look at and think of SMA-TB project from different perspectives.  SMA-TB project has received funding from the European Union's Horizon 2020 research and innovation programme under grant ageement No 847762

All data of the manuscript "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process" submitted to Geophysical Research Letters

The data supports the manuscript entitled "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process”. Microsoft Notepad can open the *.txt files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at the first fork of positive or negative leader channels. A normal video player software can open Movies S1.avi, and it shows the entire development process of IC1 discharge. The data can be used freely for scientific purposes with the appropriate citation.

Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives (Supplemental material)

This data upload provides a detailed account of indicators that can be used to measure RRI progress around the world against the UNESCO Recommendation for Science and Scientific Researchers at the level of individual researchers and public opinion. This data upload provides supplemental material for an article in Open Research Europe entitled, 'Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives'. Abstract for main article: As calls for more socially responsible research and innovation (RRI) policies and practices grow more insistent, the need for high quality indicators that can be used to evaluate progress is becoming increasingly important. Given the global nature of science, such indicators need to be relevant to countries across all world regions. Moreover, the methodological quality of indicators is critical to provide a strong foundation for long-term comparative measurement of the impacts of different kinds of policy intervention. There is a practical challenge in this effort relating to the uneven mechanisms for data collection and analysis available in different countries. There is also a geopolitical challenge in gaining buy-in from countries with very different, and sometimes competing, agendas. Here, the 2017 UNESCO-led Recommendation on Science and Scientific Researchers is highlighted as an existing vehicle that can enable cooperation on globally comparative measurement of socially responsible research and innovation. In particular, the quadrennial monitoring of the implementation of this wide-ranging global policy instrument that has been ratified by 195 countries affords a unique opportunity to add value for these countries by linking RRI to the 2017 Recommendation while establishing benchmark indicators for RRI more generally. As a practical and methodological contribution to the global community of science and innovation policymakers, researchers and research and innovation stakeholders committed to socially responsible research, this report contains specific, detailed survey questions and response options focusing on the public opinion and individual researchers’ level of measurement. It provides details of sources of benchmark survey data that have readily available open data that can be used to benchmark the development of socially responsible research and innovation over time from the vantage points of the public and researchers around the world. The aim of this kind of indicators framework is to enable evidence-based practice in socially responsible research and innovation. Robust and practical RRI measurement on a global scale can reduce the risk that well-intentioned but ill-conceived RRI policy and practice interventions create undetected, unchecked, and unreformed negative impacts in practice.

Twitter Dataset - Over 200,000 Tweets containing the word "Vaccine" for research porpuses

This dataset contains 220,085 tweets containing the word vaccine between December 9th and December 18th 2021 at different times during each day, extracted using the Twitter API v2. Each tweet was extracted at least 3 days after its initial posting time in order to register 3 days of engagements, and it doesn't include retweets. Includes: <ul> <li>Tweet ID</li> <li>Text</li> <li>Author ID</li> <li>Date</li> <li>Like count</li> <li>Retweet count</li> <li>Quote count</li> <li>Reply count</li> <li>User data (Followers, Following, Tweet count, Account creation date, Verified status)</li> </ul> Usernames are hidden for privacy reasons

IHTApark. Multi-detailed 3D architectural model for sound perception research in Virtual Reality

IHTApark – Multi-detailed 3D architecture model This dataset describes visual and acoustic 3D architectural models of the park next to the IHTA. Institute of Hearing Technology and Acoustics (IHTA), RWTH Aachen, 52056 Aachen, Germany Files are stored in FBX format for geometry, JPEG format for visual textures, and Unreal Engine for the virtual reality scenes. VERSION 1: Visual photogrammetry + Acoustic model As used in the publication: [1] Llorca-Bofí, J. and Vorländer, M. (2021). Multi-Detailed 3D Architectural Framework for Sound Perception Research in Virtual Reality. Front. Built Environ. 7:687237.doi: https://doi.org/10.3389/fbuil.2021.687237 Data is available separately for each definition, and for each visual and acoustic cue. The level of detail for each definition is shown here: <ul> <li>Visual cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> <li>Acoustic cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> </ul> This version of the model includes only the modules used for the description of the referenced paper. The authors reserve the right to complete other levels of detail if future applications require them. An additional data file contains a unique file in [IHTApark_UnrealEngine] Unreal Engine format, with the set up scenario. The instructions to open the final scenario are described here: <ol> <li>Download the [IHTApark_UnrealEgine] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_UnrealEngine]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a Content Browser tab to navigate through the folder hierarchy. You can open the Content Browser under the tabs Window > Content Browser</li> <li>Open the IHTApark map under the folder Content > Maps by double clicking on it.</li> <li>The scenario will be visible in the Viewport 1 tab. Go to Window > Viewports > Viewport 1 to open the tab.</li> <li>Press key G to hide or unhide the helpers and editor actors.</li> <li>Press keys 0, 1, 2, 3… 9 to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key W to navigate through the scenario.</li> </ol> VERSION 2: Object-based visualization in three different weather conditions As used and described in the publication: [2] Submitted to journal. The file [IHTApark_3weath_comp] Unreal Engine format contains the set up scenario. The instructions to open the final scenario are described here: <ol> <li>Download the [IHTApark_3weath_comp] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_3weath_comp]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a Content Browser tab to navigate through the folder hierarchy. You can open the Content Browser under the tabs Window > Content Browser</li> <li>Open the IHTApark_warm, IHTApark_wet or IHTApark_snowy maps under the folder Content > Maps by double clicking on it to visualize each weather condition.</li> <li>The scenario will be visible in the Viewport 1 tab. Go to Window > Viewports > Viewport 1 to open the tab.</li> <li>Press key G to hide or unhide the helpers and editor actors.</li> <li>Press keys 0, 1, 2, 3… 9 to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key W to navigate through the scenario.</li> </ol> The folder [IHTApark_3weathers_audio] contains the sound signals, as .wav files, in fist order ambisonics format (B-format).