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6,025 results for “science”
State of open science practices in France _ Dataset
<p>L’enquête State of Open Science Practices in France (SOSP-FR) a été conduite entre juin 2020 et septembre 2020. Elle a pour but d’interroger les pratiques des outils numériques et autour des données de la recherche dans les communautés scientifiques françaises. Le questionnaire se compose de 38 questions réparties en 9 thématiques. Les questions portent sur des pratiques déjà établies et des pratiques ou usages émergents comme l’<em>open peer review </em>ou les articles de données dits <em>data papers</em>. Le nombre de répondants est de 1 089, permettant d’interroger une répartition disciplinaire, genrée et statutaire assez représentative de l’état de l’emploi dans l’enseignement supérieur et de recherche en France.</p> <p>Les données ont été recueillies en 2020 via le logiciel Sphinx, mis à disposition par la TGIR Huma-Num.</p> <p>Le fichier SOSP_metadonnées_variables liste les variables avec les questions et les modalités associées. Il précise le traitement des données.</p> <p>Cette recherche a été financée par le Comité pour la science ouverte: <a href="https://www.ouvrirlascience.fr/sosp_-state-of-open-science-practices-in-france/">https://www.ouvrirlascience.fr/sosp_-state-of-open-science-practices-in-france/</a></p> <p>The State of Open Science Practices in France (SOSP-FR) survey was carried out between June 2020 and September 2020. It aims to question the practices of digital tools and about research data in French scientific communities. The questionnaire consists of 38 questions divided into 9 themes. The questions concern practices already fixed and those emerging uses such as open peer review or data papers. The number of respondents was 1089, making possible a fairly representative disciplinary, gender and status analyse of the state of employment in higher education and research in France.</p> <p>The data were collected in 2020 using the Sphinx software, provided by the TGIR Huma-Num.</p> <p>The SOSP_metadonnées_variables.csv file lists the variables with the associated questions and modalities.</p> <p>This research was funded by the Open Science Committee: <a href="http://The data were collected in 2020 using the Sphinx software, provided by the TGIR Huma-Num. The readme.csv file lists the variables with the associated questions and modalities. This research was funded by the Open Science Committee: https://www.ouvrirlascience.fr/sosp_-state-of-open-science-practices-in-france/">https://www.ouvrirlascience.fr/sosp_-state-of-open-science-practices-in-france/</a></p>
Data/ codes used in the the Natural Hazards and Earth System Sciences (NHESS) publication titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast" by Pranavam Ayyappan Pillai et al. (2022)
<p>The archive contains datasets and codes used in the manuscript titled "Wind-Wave Characteristics and extremes along the Emilia-Romagna coast", and published in the journal <em>Natural Hazards and Earth System Sciences</em> (<em>NHESS</em>) by Pranavam Ayyappan Pillai et al., 2022.</p> <p>Pranavam Ayyappan Pillai, U., Pinardi, N., Federico, I., Causio, S., Trotta, F., Unguendoli, S., and Valentini, A.: Wind-Wave Characteristics and extremes along the Emilia-Romagna coast, Nat. Hazards Earth Syst. Sci. Discuss. https://doi.org/10.5194/nhess-2022-103, 2022.</p>
Supporting information - A value creation model from science-society interconnections: Components and archetypes
<p>Data protocol and datasets used for the study entitled 'A value creation model from science-society interconnections: Components and archetypes'. </p> <p><strong>Abstract of the paper:</strong></p> <p>The interplay between science and society takes place through a wide range of intertwined relationships and mutual influences that shape each other and facilitate continuous knowledge flows. Stylised consequentialist perspectives on valuable knowledge moving from public science to society in linear and recursive pathways, whilst informative, cannot fully capture the broad spectrum of value creation possibilities. As an alternative we experiment with an approach that gathers together diverse science-society interconnections and reciprocal research-related knowledge processes that can generate valorisation. Our approach to value creation attempts to incorporate multiple facets, directions and dynamics in which constellations of scientific and societal actors generate value from research. The paper develops a conceptual model based on a set of nine value components derived from four key research-related knowledge processes: production, translation, communication, and utilization. The paper conducts an exploratory empirical study to investigate whether a set of archetypes can be discerned among these components that structure science-society interconnections. We explore how such archetypes vary between major scientific fields. Each archetype is overlaid on a research topic map, with our results showing that different archetypes correspond to distinctive topic areas. The paper finishes by discussing the significance and limitations of our results and the potential of both our model and our empirical approach for further research.</p>
Survey on Open Science Practices in Functional Neuroimaging. Dataset and Materials
<p>Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. A PubMed search with the search terms ("fMRI" OR "functional magnetic resonance imaging" OR "functional Magnetic Resonance Imaging") was done to collect email addresses from corresponding authors of scientific articles published between 2010/01/01 and 2020/08/28. An email was sent to 14,690 addresses on 2020/01/12 with an invitation to participate, including a personalized link to the survey. If the recipients did not click the link or did not complete the survey after 14 days, they received a single reminder email. The questionnaire was composed of five building blocks. The Blocks 1-3 focused on three areas of open science practices: data structure, preregistration and data sharing. The fourth block asked about technical expertise with software and the fifth part assessed sociodemographic data.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2019-2020
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2019-2020.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2020-2021
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2020-2021.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2018-2019
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2018-2019.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Estimating the carbon footprint of citizen science biodiversity monitoring
<p>Datasets used in the production of the paper Gillings, S. & Harris, S.J. 2022. Estimating the carbon footprint of citizen science biodiversity monitoring. People & Nature.</p> <p>The dataset comprises a) the estimated round-trip distances from approximate locations of observers to survey locations for the UK Breeding Bird Survey and b) questionnaire responses concerning mode of travel used to access survey locations. Data have been anonymised and locations have been coarsened to preserve anonymity.</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p> <p> </p> <p> </p>
CSI-COP Dataset of Organisations to Approach in Citizen Science Projects
<p>This dataset complements CSI-COP project deliverable D2.3 report: '<strong>Framework for Engaging Citizen Scientists</strong>'.</p> <p>The D2.3 deliverable was produced in 2020 by partners in CSI-COP work package 2 led by <strong>Professor Olga Stepankova </strong>of Czech Technical University, Prague (<strong>CTU</strong>).</p> <p>The Stepankova et al. (2020) report is available on this Zenodo platform here:</p> <p><a href="https://zenodo.org/record/4066515#.Yrx9DezMLb0">https://zenodo.org/record/4066515#.Yrx9DezMLb0</a></p>
Open Science and Authorship of Supplementary Material for the MES research community
<p>This spreadsheet contains the data and the results from the analysis described in the paper "Open Science and Authorship of Supplementary Material. Evidence from a Research Community." being accepted at STI 2022.</p>
Open Science as a nest that welcomes, as caring; and with openness to the world
<p>New infographic proposal on Open Science after those of the umbrella and the mushroom; care and welcome are added</p>
TIME4CS WP4 Mapping of citizen science training resources
<p>This dataset was compiled as part of the TIME4CS project, WP4, and lists identified citizen science training resources, as of July 2022.</p> <p>The <a href="https://eu-citizen.science/">EU-citizen.science</a> platform provided the basis for mapping CS training in Europe, as the team behind the platform has put considerable effort into compiling, and encouraging the CS community to contribute, CS training resources. Additionally, training courses were identified based on the case studies in WP1, as most universities do not list their courses on the EU-citizen.science platform.</p>
Code and data accompanying Palmeirim et al. (2022) Emergent properties of species-habitat networks in an insular forest landscape. Science Advances
<p>Dataset containing species distribution in insular forest fragments at Balbina and full R code for analyses and figures.</p> <p>For deatails, please see the original publication: "Emergent properties of species-habitat networks in an insular forest landscape". Ana Filipa Palmeirim, Carine Emer, Maíra Benchimol, Danielle Storck-Tonon, Anderson S. Bueno, Carlos A. Peres. Science Advances (2022). 10.1126/sciadv.abm0397.</p> <p> </p>
Dataset: energy consumption patterns in a science and technology park
<p>This dataset contains time series of energy consumption and external temperature for a group of buildings in a science and technology park, from 2018 to 2022, that is suitable for the development of algorithms to improve energy efficiency and for the early detection of energy consumption peaks based on night-time outdoor temperatures.</p> <p>Time series of power, energy consumption and external temperature for group of tertiary buildings, from 2018-01-01 to 2022-09-15.</p> <p>Peaks in electricity consumption are a major concern for building owners, especially during summer, when external temperatures are high, and users demand air conditioning. Owners may face high costs, observe increased risk of overheating in energy intensive equipment, and may exceed the power threshold set out in the electricity supply contract.</p> <p>Several effective "peak-shaving" strategies can be put in place, such as a higher temperature set-point (which implies a temporary reduction of comfort levels), switching off low-priority processes, and starting the cooling process earlier than usual.</p> <p>This dataset can be used to develop algorithms for early detection of peaks in energy consumption, based on external temperatures measured during the night.</p> <p> </p>
Unprocessed data from the Jungle Weather Zooniverse citizen science project
<p>The <a href="https://www.zooniverse.org/projects/khufkens/jungle-weather">Jungle Weather project</a> aimed to transcribe weather observations recorded between 1949 and 1958 in the tropical rainforest of the Democratic Republic of the Congo. Long-term observations of tropical weather are rare. The Jungle Weather, as part of the COBECORE project, contains observations of three decades of data of weather in the central African tropical forest, and are therefore an extraordinary source of information to support our understanding of for example drought resilience of trees species.</p> <p><strong>Summary</strong></p> <p>Both input and output of the citizen science transcriptions are provided in this data set. This includes the original cut-outs as used in the Zoonivese project, and the output as generated by the Zooniverse data export routines. The data export routines provided CSV output with JSON subfields on the content of each classification made. In addition, we provided the exported subject list and the details of each workflow.</p> <p>In total the project output constitutes of four files:</p> <ul> <li>transcribe-climate-data-classifications.csv (annotations of the table cells)</li> <li>transcribe-meta-data-classifications.csv (annotations of table headers)</li> <li>jungle-weather-workflows.csv (description of the citsci workflow)</li> <li>jungle-weather-subjects.csv (list of all images transcribed, and their online location for validation / referencing)</li> </ul> <p>and roughly ~3GB in data volume.</p> <p><strong>Context</strong></p> <p>Our understanding of forest ecosystem responses to climate change relies on consistent long-term observations to provide baseline measurements. In the central Congo Basin established long-term observation programs are rare. In terms of meteorological observations, the central Congo Basin is currently represented by only a few rain gauges, limiting climate forecasts across the Congo Basin and the central African continent. This lack of long-term (historical) climatological data leaves the central Congo Basin spatially and temporally under-represented. However, old climate records could provide valuable information about previous growing conditions of the forest.</p> <p>Large amounts of ecological and climatological data, approximately five decades (~1910 – 1960), exists as unexplored heritage, stored in various Belgian federal archives and collections. As part of a larger project called Congo Basin eco-climatological data recovery and valorization (COBECORE, see) the Jungle Weather project will help transcribe historical climatological data as measured throughout the Congo Basin. These data will in part complement the completed <a href="https://www.zooniverse.org/projects/khufkens/jungle-rhythms">Jungle Rhythms Zooniverse project</a>, further valorizing these transcribed data.</p> <p><strong>Historical data</strong></p> <p>Within this project we will focus on data records as recorded throughout the tropical part of what is currently the Democratic Republic of the Congo (DRC). The area which we will cover is shown above in the map as an open polygon. The project will not cover the southern province of Katanga (red crosshatches) as this area transitions here from tropical to a humid subtropical climate.</p> <p>The historical data is archived and stored in the Belgian State Archives. The Belgian State Archive harbour almost all data regarding colonial affairs, ranging from communications about trade to the raw data as digitized within the context of the Jungle Weathers project. Row upon row of data is stored in the basement. Below you see a part of the INEAC (Institut National pour l’Etude Agronomique du Congo belge) archive, which holds all climatological records.</p> <p>These climatological records were noted rigorously on carbon copy paper. However, due to the hand written nature of the data (and the volume involved) automated processing is not possible. Although optical character recognition (OCR) works wonderfully on printed data the high variability in characters and the low contrast pencil markings contribute to the failure of current automated approaches. Similar to the <a href="https://www.oldweather.org/">Old Weather project</a> and in spirit of the Jungle Rhythms project, a keen eye is required to decipher the numbers written down on these sheets.</p> <p><strong>Pre-processing / digitization</strong></p> <p>The project provided citizen scientists with digital pictures of the original sheets. Scanning these climate data sheets was a laborious process. In total more than 70 000 records were digitized. Unlike the Old Weather project we did not require citizen scientists to outline valid sections of the sheet. This part of the processing has been automated. We refer to our<a href="https://doi.org/10.5281/zenodo.3378864"> Jungle Weather pre/post-processing repository </a>for more details and example code</p> <p>As such, once digitized and properly aligned the whole record was divided into an estimated 30 million cells and 70 000 header files. Below you find an example of a header file and a table cell. During the Jungle Weather project we selected a subset of ~300K table cells for transcription in efforts to validate further Machine Learning based, automated, transcriptions approaches. All data were transcribed by citizen scientists in the spring/summer of 2020.</p> <p><strong>Notes</strong></p> <p>The provided data is raw data, and expert knowledge is required for the correct interpretation of this data. Please contact the authors for the proper context if you are interested in using this data in your project.</p>
Dataset for "Authorship concentration in health sciences journals from Latin America and the Caribbean"
<p>Authorship concentration indexes and other data for journals in the LILACS (Latin American and the Caribbean Literature on Health Sciences) bibliographic database, from 2015 to 2019 (FONTENELLE, 2022). These data are read and created by <a href="https://doi.org/10.5281/zenodo.6127497">analytic code in Zenodo</a>.</p> <ul> <li><em>authorship_concentration.csv</em> - dataset derived in Fontenelle (2022) from raw data exported from LILACS. This is the main file, and it's CC-BY because other researchers might have curated the raw data differently and thus derived different data. CSV file encoded with ASCII.</li> <li><em>authorship_concentration_datadictionary.csv</em> - data dictionary for the previous file. This file is actually CC0. CSV file encoded with ASCII.</li> <li><em>journals.csv</em> - dataset about the journals indexed in LILACS between 2015 and 2019. As a result of simply converting and filtering the original TITLE database, this is actually CC0 by the Pan American Health Organization (PAHO). CSV file encoded with UTF-8.</li> <li><em>journal_subjects.csv</em> - DeCS descriptors for the journals identified by the ISSN. CC0 by the Pan American Health Organization (PAHO), as above. CS file encoded with ASCII.</li> </ul>
Reproducible and Attributable Materials Science Workflows
<p>This set includes the deidentified data, reproducible analysis and research report of the project on Reproducible and Attributable Materials Science Workflows.</p>
Climatological data from mechanistic model experiments of Boljka and Birner (2022/3; npj Climate and Atmospheric Science)
<p>Some climatological output data from mechanistic dry dynamical core model experiments used for the paper of Boljka and Birner (2022/3): "Potential impact of tropopause sharpness on the structure and strength of the general circulation", npj Climate and Atmospheric Science. For more details see the manuscript. </p>
Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"
<p>Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G.</p> <p>The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales.</p> <p>The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications.</p> <p>This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.</p>
Supplementary data to `Do science maps from open access literature capture the overall topic structure of an academic field?`
<p>The dataset contains the 8,528 academic articles records related to Sustainable Food research sourced with the query `TS=("sustainab*" NEAR/2 "food*")` .</p> <p>They are the records present in the largest component of the citation network, as specified in the manuscript. </p> <p>The dataset was sourced from OpenAlex based on the original data used in the manuscript and it is composed of the following columns:</p> <table> <tbody> <tr> <td><em><strong>Column</strong></em></td> <td><em><strong>Description</strong></em></td> </tr> <tr> <td>Id</td> <td>OpenAlex ID</td> </tr> <tr> <td>DOI</td> <td>Document Object Identifier</td> </tr> <tr> <td>display_name</td> <td>The article title</td> </tr> <tr> <td>publication_year</td> <td>The publication year of the article</td> </tr> <tr> <td>open_access</td> <td>An object with details of the open access status of the article</td> </tr> </tbody> </table> <p>We choose the `.rdata` format for easy loading in R. Use the function `load()` to add the data frame to the enviroment. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.