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73 results for “science journalism”
Open access journals in the field of library and information sciences (LIS)
<p>List of foreign open access journals in the field of LIS is presented</p>
Open access practices of selected library science journals
Open the record for dataset details and reuse information.
Leveraging metrics to drive data sharing at the Science journals
Open the record for dataset details and reuse information.
Dataset accompanying Andersen et al. 2020 Journal of Vegetation Science
<p>The data presented here is the data used in the paper 'Turnover and change in plant species composition in a shielded salt marsh following variation in precipitation and temperature' by Andersen et al. 2020 Journal of Vegetation Science.</p> <p>The study was funded by Aage V. Jensens Naturfond (AVJNF) [Project title (in Danish): <em>Udvikling af en forvaltningsstrategi, der tilgodeser hele økosystemet i De Østlige Vejler</em>].</p> <p>The data is arranged in four spreadsheets:</p> <p>Plot values:</p> <p>- Contains information for the 40 sampling plot sampled in each of 2017 and 2018.</p> <p>- The results represent mean values per sampling plot. </p> <p>- Ellenberg values are caluculated based on the species present in the 5 m circle.</p> <p>- Units provided along with column name.</p> <p>Presence absence 5m: </p> <p>- Species names listed in latin.</p> <p>- '1' denotes that the species was present in the 5 m circle, '0' denotes its absence.</p> <p>Pin point frequency:</p> <p>- Species names in latin.</p> <p>- The numeric values indicates how many times the given species was registred within the pinpoint frame. </p> <p>GPS coordinates:</p> <p>- Coordinates are in WGS 1984 format.</p>
Supplementary Table 1. Characteristics of the Group 1 data journals that publish in the fields of biology, environmental science, chemistry, medicine, and health sciences
<p>Supplementary Table 1. Characteristics of the Group 1 data journals that publish in the fields of biology, environmental science, chemistry, medicine, and health sciences.</p> <p>This file is a digital supplement to William H. Walters, "Data journals: incentivizing data access and documentation within the scholarly communication system," <em>Insights: The UKSG Journal</em> 33, article 18 (June 10, 2020), 1–20, <a href="http://doi.org/10.1629/uksg.510">http://doi.org/10.1629/uksg.510</a>.</p>
Supporting Data for "The extent of counterion dissociation at the interface of cationic diblock copolymer nanoparticles in non-polar solvents" (Journal of Colloid and Interface Science, 2020)
<p>NMR measurement files (1D 19F, 1H diffusion, and 19F diffusion) as specified in the filename on dispersions of diblock copolymer nano-particles as specified in the filename.</p>
Raw and aggregated data for the study introduced in the article "Citing and referencing habits in Medicine and Social Sciences journals in 2019"
<p>This dataset contains all the raw data and aggregated data subject of the study introduced in the article "Citing and referencing habits in Medicine and Social Sciences journals in 2019". The study is based on the bibliographic and citation data contained in 213 articles published in 46 journals in two subject areas: Medicine and Social Sciences. The articles contained a total amount of 9,911 bibliographic references and 16,193 mentions and quotations overall. In particular, the raw data are about 27 journals, 132 articles, 5,340 bibliographic references, 8,400 mentions and 5 quotations in Medicine, and 19 journals, 81 articles, 4,571 bibliographic references, 6,953 mentions and 835 quotations in Social Sciences.</p> <p>The dataset is composed of three files:</p> <ul> <li>the file "medicine-journals.csv" contains the raw data of the articles published in Medicine journals;</li> <li>the file "social-sciences-journals.csv" contains the raw data of the articles published in Social Sciences journals;</li> <li>the file "aggregated-data-medicine-social-sciences.csv" contains the aggregated data created considering the raw data in the previous files, which have been used to creating all the tables and figures in the article.</li> </ul>
Dataset: Species presence, trait and philogeny database. Data from: Shift from trait convergence to divergence along old field succession. Journal of Vegetation Science.
<p>This dataset was use in the manuscript "Shift from trait convergence to divergence along old field succession" publish at Journal of Vegetation Science. It contains the data of species presence at three different plots and species trait values (SLA, LDMC, height, seed weight and Phylogeny). We show that at the initial stage of succession, there is a scale-invariant pattern of trait convergence. On the contrary, in the late and intermediate stages of succession we observed that trait divergence became frequent at smaller grains and extents. The trend along the chronosequence is consistent with the theoretical hypothesis that predicts a shift from convergence to divergence along succession.</p>
Codes and data related to the article: Renard (2023). Use of a National Flood Mark Database to Estimate Flood Hazard in the Distant Past. Hydrological Sciences Journal.
<p>This package contains R codes and data related to the article:</p> <p>B. Renard. 2023. Use of a National Flood Mark Database to Estimate Flood Hazard in the Distant Past<em>. Hydrological Sciences Journal</em>. DOI: <a href="https://doi.org/10.1080/02626667.2023.2212165">10.1080/02626667.2023.2212165</a></p> <p><strong>CONTENTS</strong></p> <p>R scripts used to set up models, analyse results and prepare figures.</p> <ul> <li>France207_MAX.RData contain monthly maxima at 207 stations. See references below for the original sources.</li> <li>Folder data_raw/ contain exports from the flood marks database. See references below for the original sources.</li> <li>funk.R is a library of functions used by other scripts.</li> <li>0_setUp.R prepares the runs to estimate the model for one component. Estimation is performed using the RSTooDs package (<a href="https://zenodo.org/record/5075760">https://zenodo.org/record/5075760</a>). This script extracts the data, specifies the probabilitic models and write STooDs configuration files.</li> <li>1_margin.R analyses the runs that have been performed and estimates marginal distributions at each station. Note that the runs set up in the previous point need to have been performed before using this script.</li> <li>2_probMap.R computes estimated flood probabilities over the whole 1705-2015 period. The previous script needs to have been run before.</li> <li>3_sensitivityAnalysis.R prepares configuration files for sensitivity analysis experiments.</li> <li>4_ZenodoRelease.R prepares the released data and probability maps. The previous scripts needs to have been run before.</li> <li>Scripts named fig_XXX.R create the figures shown in the article.</li> </ul> <p><strong>REFERENCES</strong><br> Streamflow series at stations: <a href="https://www.hydro.eaufrance.fr/">https://www.hydro.eaufrance.fr/</a><br> developed by the "Service central d’hydrométéorologie et d’appui à la prévision des inondations" (Schapi) from the Ministry of the Ecological Transition</p> <p>Flood marks database: <a href="https://www.reperesdecrues.developpement-durable.gouv.fr/">https://www.reperesdecrues.developpement-durable.gouv.fr/</a><br> developed by the flood forecasting network "Vigicrues" from the Ministry of the Ecological Transition</p> <p> </p>
Figure 1 from: Filter M, Candela L, Guillier L, Nauta M, Georgiev T, Stoev P, Penev L (2019) Open Science meets Food Modelling: Introducing the Food Modelling Journal (FMJ). Food Modelling Journal 1: e46561. https://doi.org/10.3897/fmj.1.46561
Figure 1 - Workflow for conversion of FSK-ML metadata into Model manuscripts.
Cognitive maps examples for behavioural science journal
<p>Cognitive maps examples for behavioural science journal</p>
Supplemental Figures and Video for the manuscript Leben et al, 2019, International Journal of Molecular Sciences
<p>Supplemental Figures and Video for the manuscript Leben et al, 2019 to IJMS, section "Molecular Immunology"</p>
Digital Accessibility of Life Science Data Portals and Journal Websites
<p>Enhancing the diversity and inclusion of the life sciences workforce has become an important problem as highlighted by many organizations in the US, including NIH, NHGRI, and NSF. People with visual impairments are one of the groups that face barriers to access to the biology workforce. To overcome this challenge, it is important to understand their current barriers in biological research and education. The most common assistive technology used by people with visual impairments is the screen reader (45.2%). However, multiple studies found that many websites largely fail to meet accessibility guidelines, making it challenging or even impossible for screen reader users to access existing resources. To help gain better insights into how well people with visual impairments can access existing biological resources, we evaluated the digital accessibility of two essential resources for data-driven studies—data portals and journal websites. Using an automated evaluation tool, we collected accessibility evaluation data for a large corpus of resources (<i>N</i>=3,943). In addition, we collected metadata of individual resources (e.g., geospatial, temporal, and impact score data) for a more insightful analysis. All datasets, as well as the entire source code, are available online on Zenodo and GitHub under a CC-BY and MIT license, respectively.</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.