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6,025
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6,025 results for “Science of science”
Survey dataset - Environmental Citizen Science: practices and scientists' attitudes at ILTER
<p>The dataset contains survey outcomes from ILTER scientists about their attitudes and actions with regard to Environmental Citizen Science.</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Data for: Torii et al.,Observed Kinetics of Enterovirus Inactivation by Free Chlorine Are Host Cell-Dependent, Environmental Science and Technology, 10.1021/acs.est.2c07048
<p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>- Figure 1 (Inactivation curves for E11 by free chlorine, UV, and heat)</p> <p>- Figure 2 (Inactivation curves for CVA9, CVB1, E7, E9, and E13)</p> <p>- Figure S1 (Loss of attachment and the PCR-target by free chlorine treatment)</p> <p>- Figure S2 (Flow cytometric analysis)</p> <p> </p> <p> </p> <p> </p>
Data Table - Digital Scholarship, PhD project "Bridging Data Science and Intellectual History: Computing the Nodes and Edges in the Old University of Louvain (1425-1797)"
<p>How were academic networks configured in the premodern world, and how did they change? This doctoral project seeks to offer a data-driven answer by computing networks at and around the Old University of Louvain (1425-1797), a crucial hub for the transfer of knowledge in late medieval and early modern Europe. Drawing upon datasets under construction from the teams of the PIs at KU Leuven and UCLouvain, this project sets out to plot and visualize networks of students, scholars and their ‘books’ over almost four centuries, thus integrating data from demographic, prosopographical and book historical datasets. This will lead to a better understanding of how academic communities evolved in the past, and it will help to assess how their organization and structure promoted or hindered the creation and transfer of knowledge in premodern Europe. Hence, the doctoral research project offers an innovative test case to develop novel understandings of networks (e.g.. new tested ways to define nodes and edges) in datasets on scholars and ‘literati’, and it will be able to compare these new results to interpretations of human capital indices in the past. As such, the project creates a pioneering pilot for integrating data science into the field of intellectual and early modern history. This enhanced collaboration between KU Leuven and UCLouvain on a theme related to their common past is timely in view of 600 years Leuven/Louvain in 2025.</p>
Supplementary dataset to the publication by Hieronymi et al.: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science (under review, submitted 22 Dec 2022)
<p>The dataset is an annex to the publication (under review, submitted 22 Dec 2022):</p> <p>Martin Hieronymi, Shun Bi, Dagmar Müller, Eike M Schütt, Daniel Behr, Carsten Brockmann, Carole Lebreton, François Steinmetz, Kerstin Stelzer and Quinten Vanhellemont: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science.</p> <p>The data were created to compare the results of different atmospheric correction methods for ocean (water) color imagery. The dataset includes ten modified ESA/EUMETSAT Copernicus Sentinel-3 OLCI satellite scenes from optically diverse sea areas worldwide. The NetCDF files are optimized for visualization in the ESA Sentinel Application Platform (SNAP) and especially the Spectrum View. The data include original OLCI Level-1B top-of-atmosphere radiances recorded by the sensor and the results from five different atmospheric correction methods, i.e., spectral remote-sensing reflectance at 16 OLCI bands. The atmospheric correction methods compared are</p> <ol> <li> <p>IPF (Collection 3, the standard method),</p> </li> <li> <p>C2RCC (v1.7 including IPF gains; Brockmann et al. [2016]),</p> </li> <li> <p>A4O (v0.23 (2022-01-19); a novel method by Hieronymi et al.),</p> </li> <li> <p>POLYMER (v4.14 (2021-12-17); Steinmetz et al. [2011]), and</p> </li> <li> <p>ACOLITE-DSF (v2022-10-25.0; Vanhellemont and Ruddick [2021]).</p> </li> </ol> <p>The original flags supplied in each case are also provided.</p> <table> <tbody> <tr> <td> <p><strong># </strong></p> </td> <td> <p><strong>Sensor-Date-UTC</strong></p> </td> <td> <p><strong>Region </strong></p> </td> <td> <p><strong>Special features </strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>S3A-20160720-092821</p> </td> <td> <p>Barents Sea</p> </td> <td> <p>High latitudes, bloom of coccolithophores</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>S3A-20160720-093421</p> </td> <td> <p>North Sea, Wadden Sea</p> </td> <td> <p>Moderately to extremely scattering waters, tidal areas, in situ data</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>S3A-20170114-130626</p> </td> <td> <p>South Atlantic Ocean, Rio de la Plata estuary</p> </td> <td> <p>Extremely scattering waters, clear oceanic waters, sun glint, South Atlantic Anomaly</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>S3A-20170527-015236</p> </td> <td> <p>Yellow Sea, East China Sea, Yangtze, Lake Taihu</p> </td> <td> <p>Extremely scattering waters, tidal areas, large rivers, absorbing aerosols, sun glint</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>S3A-20170529-092334</p> </td> <td> <p>Mediterranean Sea</p> </td> <td> <p>Large areas with clear waters, sun glint</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>S3A-20170913-080730</p> </td> <td> <p>Black Sea, Aegean Sea</p> </td> <td> <p>Clear and absorbing waters</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>S3A-20180715-093613</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Intense bloom of cyanobacteria partly with scum</p> </td> </tr> <tr> <td> <p>8 9</p> </td> <td> <p>S3A-20200601-092517 S3B-20200601-084546</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Inter-comparison of S3A and S3B with different observation angles, absorbing waters</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>S3B-20200406-093801</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>High OWT diversity</p> </td> </tr> </tbody> </table> <p> </p>
Research whale - from unacceptable science to ideal science - what is hidden and what is visible
<p>Thi diagram shows how most of the research practice is hidden and we cannot see most of the malpractices due to limited research transparency.</p>
Data to reproduce the results presented in Lake et al. 2023. Science of The Total Environment, https://doi.org/10.1016/j.scitotenv.2023.162332 ("Use of a submersible spectrophotometer probe to fingerprint spatial suspended sediment sources at catchment scale")
<p>This repository contains the absorbance data measured on the water samples collected in all sampling sites, for the three campaigns, as described in Lake et al., 2023. </p> <p>Data consists of:</p> <p>- Absorbance data compensated for measured concentration and compensated for absorbance measured on filtered water </p> <p>- Absorbance data compensated for measured concentration</p> <p>Shown files are the input files for the MixSIAR modelling exercise as described in Lake et al., 2023.</p>
Open Science stakeholders in Albania, Armenia, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Georgia, Greece, Hungary, Moldova, Montenegro, North Macedonia, Romania, Serbia and Slovenia [updated, February 2023]
<p>The dataset contains tabular information about 1118 stakeholders (1079 unique entities) in 15 countries of Southeastern Europe that have been identified as Open Science stakeholders within the framework of the project NI4OS-Europe, funded by the European Commission under the INFRAEOSC-5b call.</p> <p>It was collected between November 2022 and the end of January 2023 based on the information provided by project partners from Albania, Armenia, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Georgia, Greece, Hungary, Moldova, Montenegro, North Macedonia, Romania, Serbia and Slovenia. It builds upon the dataset collected in 2019 as part of the NI4OS-Europe landscaping activity (Kosanović, Biljana, Ševkušić, Milica, & Otašević, Vladimir. (2020). Open Science stakeholders in Albania, Armenia, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Georgia, Greece, Hungary, Moldova, Montenegro, North Macedonia, Romania, Serbia and Slovenia [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3766125">https://doi.org/10.5281/zenodo.3766125</a>). In November 2022, the project partners were invited to review and update this original dataset.</p> <p>The stakeholders are classified into five groups based on their role in the research ecosystem: FUND (research funders and policymakers), CREATE (universities, research institutes, etc.), SUPPORT (libraries, repositories, research infrastructures, etc.), CONSUME (organizations using research results in their work, e.g. SMEs) and FACILITATE (individuals and organizations involved in promoting the principles off open science).<br> The dataset contains the following information for each entry: country, stakeholder category/role, official/legal name of the organization, city, Zipcode, addresses (street name and number), URL of the institutional website, geographic coordinates (latitude and longitude).</p> <p>****Dataset contents****<br> NI4OS_Stakeholder_Map_20230222.csv, data file, comma-separated values<br> NI4OS_Stakeholder_Map_20230222-README.txt, metadata, text format</p> <p>****Column headers and field types***<br> Country (text)<br> StakeHoldersRole (text, ItemList{fund,create,facilitate,consume,support})<br> InstitutionName (text)<br> City (text)<br> Zipcode (text)<br> Address (text)<br> URL (text-web address)<br> Latitude (number.decimal(2,7))<br> Longitude (number.decimal(2,7))</p> <p>Data from this dataset have been quality-checked by the NI4OS-Europe project team. We recommend these data for further use.<br> <br> The dataset was used to generate an interactive map: https://ni4os.eu/os-stakeholders-map//</p> <p>****Dataset license****<br> The dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0), <a href="https://creativecommons.org/licenses/by/4.0">https://creativecommons.org/licenses/by/4.0</a></p>
Science meets industry: Joint ArMoR Cluster Meeting
<p>The joint event took place in Wageningen, Netherlands on the 16th of February 2023. </p> <p>Supported by the European Commission, Horizon Dissemination Booster (HRB) contributes to an effective transfer of research and innovation project results to policy makers, industry and society by offering various services as dissemination, exploitation strategy and business plan development to projects. Within Horizon Results Booster programme (HRB), 7 research projects AMRILS, AVANT, BM-FARM, FARMCARE, DISARM, HealthyLivestock and ROADMAP have formed the "ArMoR Cluster" to develop a conceptual framework to improve understanding of AMR in livestock systems.</p> <p>The video is available on YouTube: <a href="https://youtu.be/uSE20DHrE7E">https://youtu.be/uSE20DHrE7E</a></p> <p>For any further questions please contact us at:</p> <ul> <li><strong><a href="https://zenodo.org/record/avant@rtds-group.com">avant@rtds-group.com</a></strong> (project AVANT)</li> </ul>
Studies Rejected and Selected on the Systematic Mapping of Open Science Practices in Software Engineering
<p>Files in .xlsx format, describing the rejected and selected studies, recovered in a Systematic Mapping about Open Science Practices in Software Engineering.</p>
Web of Science data for literature related to ecology and chemical pollution
<p>This table lists the numerical results of Web of Science searches for papers published in the period between 2017 and 2021 in the “ecology” category (as defined by Web of Science), related to “global change” + selected factors (Temperature, Water, CO2) or pollution (represented by the terms “Synthetic chemical”, “Chemical pollution”, or “Contaminant”), or “biodiversity” + selected drivers for biodiversity loss (Land use, Climate change, Invasive species, Logging) or pollution (represented by the terms “Synthetic chemical”, “Chemical pollution”, or “Contaminant”)</p>
Diversity of Expertise is Key to Scientific Impact: a Large Scale Analysis in the Field of Computer Science
<p><strong>This repository is companion to a research paper with the following abstract:</strong></p> <p>Understanding the relationship between the composition of a research team and the potential impact of their research papers is crucial as it can steer the development of new science policies for improving the research enterprise. Numerous studies assess how the characteristics and diversity of research teams can influence their performance across several dimensions: ethnicity, internationality, size, and others. In this paper, we explore the impact of diversity in terms of the authors’ expertise and skills. To this purpose, we retrieved 114K papers in the field of Computer Science and analysed how the diversity of research fields within a research team relates to the number of citations their papers received in the upcoming 5 years. The results show that two different metrics reflecting the diversity of expertise are directly associated with the number of citations. This suggests that, at least in Computer Science, diversity of expertise is key to scientific impact.</p>
FIG. 2 in The Utility of Acoustic Citizen Science Data in Understanding Geographic Distributions of Morphologically Conserved Species: Frogs in the Litoria phyllochroa Species Group
FIG. 2. Map of eastern Australia including the states of Queensland (QLD), New South Wales (NSW), Australian Capital Territory (ACT), and Victoria (VIC) showing the mapped ranges of known extant species in the Litoria phyllochroa group as informed by data from FrogID and the Atlas of Living Australia, including areas where species' ranges may overlap.
FIG. 1 in The Utility of Acoustic Citizen Science Data in Understanding Geographic Distributions of Morphologically Conserved Species: Frogs in the Litoria phyllochroa Species Group
FIG. 1. Map of eastern Australia including the states of Queensland (QLD), New South Wales (NSW), Australian Capital Territory (ACT), and Victoria (VIC) showing records of known extant species of the Litoria phyllochroa group from the Atlas of Living Australia (ALA; A), FrogID (B), and a combined dataset containing both FrogID records and those deemed spatially and taxonomically reliable from ALA (C).
Supplementary Data for "Volcanic phosphorus supply boosted Mesozoic terrestrial biotas in northern China" in Science Bulletin.
<p>Supplementary data tables for Ma et al. (2023) associated with the paper entitled "Volcanic phosphorus supply boosted Mesozoic terrestrial biotas in northern China" published in <em>Sci. Bull.</em></p>
Open Science resources by research discipline
<p>This is the underlying data set to our map of Open Science resources by discipline: <a href="https://kumu.io/access2perspectives/open-science#disciplines">https://kumu.io/access2perspectives/open-science#disciplines</a></p> <p>For context and related resources please refer to <a href="https://access2perspectives.pubpub.org/open-science">https://access2perspectives.pubpub.org/open-science</a></p> <p>Explore the map by clicking on individual nodes and see descriptions, related websites, and other information about the resources.<br> With the navigation buttons to the right, you can zoom in and out, and select and focus on specific elements.</p> <p>Learn more about our work at <a href="http://access2perspectives.org">access2perspectives.org</a><br> If you have comments, questions or suggestions for improvements on this map email us at info@access2perspectives.org.</p> <p>LICENSE: <strong>Creative Commons <a href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a></strong></p>
Supporting information for the RNAct Data Science Wizard (DSW) knowledge model for early-stage researchers.
<p>The two attached Excel sheets contain information about the datasets collected by the Early Stage Researchers (ESRs) in the RNAct MSCA-ITN project (RNAct_datasets.xlsx) and on the questionnaire that was put to the ESRs in relation to the Data Science Wizard (DSW) knowledge model that was developed as part of RNAct. </p> <p>The zip file contains data in relation to the Data Science Wizard template development:</p> <p>- DMP_1stRound_introductory_presentation.pdf: Presentation for the ESRs to prepare them for filling in the first version of the DMP</p> <p>- DMPs_1stRound, DMPs_2ndRound: The filled in DMPs by the ESRs in the first and second round</p> <p>- RNAct-ESRtraining-KM_1.0.3.km: The first version of the knowledge model to create the DMP</p> <p>- RNAct-ESRtraining-KM_1.0.14.km: The second and final version of the knowledge model to create the DMP</p> <p>- TemplateDMP_RNAct-ESRtraining-KM_1.0.14.pdf: A PDF overview of the second and final version of the knowledge model</p>
Article dataset: Taxonomy of Open Science: revised and expanded
<p>Conjunto de dados do artigo: <strong>Taxonomia da Ciência Aberta: revisada e ampliada </strong></p>
Supplementary material 2 from: Colombari F, Battisti A (2023) Citizen science at school increases awareness of biological invasions and contributes to the detection of exotic ambrosia beetles. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 211-229. https://doi.org/10.3897/neobiota.84.95177
Slides of the lecture on 'Monitoring of insect species harmful to trees and forests' and link to educational videos
An annotated list of horizon scanned technologies with potential for application in alien species citizen science projects
<p><strong>Context</strong></p> <p>The contribution of volunteers in recording invasive alien species (IAS) has been fostered by technological developments such as social media, apps, low-cost sensors, search engines and predictive analytics. These technology developments, an increased attention to citizen science and a cultural change towards collaboration and openness in research within the policy agenda should increase the contribution of volunteer recording. Within the framework of the COST Action CA17122 <a href="https://www.ceh.ac.uk/our-science/projects/alien-csi"><em>Increasing Understanding of Alien Species through Citizen Science</em></a> (<a href="https://doi.org/10.3897/rio.4.e31412">Roy et al. 2018</a>) a group of researchers explored the value of emerging technologies for citizen science in the context of alien species, recognizing the contribution of volunteers and reviewing their potential to engage broad audiences, motivate volunteers, improve data collection, increase data quality etc.</p> <p><strong>Survey</strong></p> <p>The following criteria were then used to evaluate the potential of these technologies for alien species citizen science through a dedicated <a href="https://forms.gle/9GQJctnAbPLKyxDE7">survey</a>:</p> <p>● <em><strong>Audience</strong></em>: the technology can attract new target audiences for IAS citizen science and/or support more inclusivity in IAS citizen science (can overcome inequalities in participation, attract under-privileged audiences/those underrepresented in the scientific enterprise, allow participation of sensory/cognitive/otherwise impaired...)<br> ● <em><strong>Engagement </strong></em>with others: the technology supports better connections with other participants, helpful in building a community<br> ● <em><strong>Engagement via feedback</strong></em>: the technology increases the quality, amount or rate of feedback (including supporting learning) to participants<br> ● <strong><em>Application</em></strong>: the technology can be embedded in everyday life and therefore has the potential for wide, generic application</p> <p>● <em><strong>New data</strong></em>: the technology yields new types of data that would not be available without the technology (improved the detectability of IAS, new types of data, species interactions, new information sources)<br> ● <strong><em>Extends data</em></strong>: the technology expands the scope of data collection or analysis (e.g. better coverage spatially, temporally)<br> ● <strong><em>Improves data quality</em></strong>: the technology improves species ID, reduces uncertainty, improves validation<br> ● <strong><em>Improves the flow of data</em></strong>: the technology increases the speed of record transmission (e.g. for early warning)<br> ● <strong><em>Improves the curation of data</em></strong>: the technology itself allows for improved data curation (better metadata, sustainability and long term preservation data, open data, tracked provenance of data, FAIR data management, enable to better credit citizen scientists for their data contributions)</p> <p><strong>Dataset description</strong></p> <p>This dataset represents the list of technologies (in the broadest sense, including approaches) that were identified collectively by the experts as being relevant technologies in the framework of (alien species) citizen science. The dataset includes the following fields:</p> <ul> <li><em>Name</em>: name of the approach/technology</li> <li><em>Category</em>: broad categorisation of the approach/technology (Hardware and infrastructure, data collection and analysis tools, tools to improve user experience). If some approaches are combinations this is mentioned in description.</li> <li><em>Description</em>: a definition and/or description of the approach/technology</li> <li><em>Reference</em>: a reference on the approach/technology (e.g. paper, online reference), mostly with a doi</li> <li><em>Example</em>: an example of the approach/technology, mostly with reference to an (alien species) citizen science project that applied it</li> <li><em>Notes: </em>any further remarks</li> </ul>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.