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6,025 results for “Science of science”
Supplementary material 3 from: Howard L, van Rees CB, Dahlquist Z, Luikart G, Hand BK (2022) A review of invasive species reporting apps for citizen science and opportunities for innovation. NeoBiota 71: 165-188. https://doi.org/10.3897/neobiota.71.79597
Table S3. App Metadata
Supplementary material 1 from: Howard L, van Rees CB, Dahlquist Z, Luikart G, Hand BK (2022) A review of invasive species reporting apps for citizen science and opportunities for innovation. NeoBiota 71: 165-188. https://doi.org/10.3897/neobiota.71.79597
Table S1. Search parameters
MeadoWatch: a long-term community-science database of wildflower phenology in Mount Rainier National Park
<p>We present a long-term and high-resolution phenological dataset from 17 wildflower species collected in Mt. Rainier National Park, as part of the MeadoWatch (MW) community science project. Since 2013, 500+ unique volunteers and scientists have gathered data on the timing of four key reproductive phenophases (budding, flowering, fruiting, and seeding) in 28 plots over two elevational gradients alongside popular park trails. Trained volunteers (87.2%) and UW scientists (12.8%) collected data 3-9 times/week during the growing season, using a standardized method. Taxonomic assessments were highly consistent between scientists and volunteers, with high accuracy and specificity across phenophases and species. Sensitivity, on the other hand, was lower than accuracy and specificity, suggesting that a few species might be challenging to reliably identify in community-science projects. Up to date, the MW database includes 42,000+ individual phenological observations from 17 species, between 2013 and 2019. However, MW is a living dataset that will be updated through continued contributions by volunteers, and made available for its use by the wider ecological community.</p>
Metadata schemes for materials science data
Metadata schemes for materials science data in JSON representation as implemented in TTL representation in the application profiles in the version from 01.05.2022 (<a href="https://git.rwth-aachen.de/coscine/graphs/applicationprofiles/-/commit/bfa60f39c481d8a511001437412afd5e99f475ad">gitlab</a>) of the research data management platform <a href="https://www.coscine.de/">CoScInE</a>. The schema are actively developed in the SFB1394 with the aim to construct defect phase diagrams in an automated fashion using all data from advanced experimental characterization and computer simulations produced in this project.
Supplementary material 3 from: Miya M, Sado T, Oka S-i, Fukuchi T (2022) The use of citizen science in fish eDNA metabarcoding for evaluating regional biodiversity in a coastal marine region: A pilot study. Metabarcoding and Metagenomics 6: e80444. https://doi.org/10.3897/mbmg.6.80444
Table S3
Supplementary material 1 from: Miya M, Sado T, Oka S-i, Fukuchi T (2022) The use of citizen science in fish eDNA metabarcoding for evaluating regional biodiversity in a coastal marine region: A pilot study. Metabarcoding and Metagenomics 6: e80444. https://doi.org/10.3897/mbmg.6.80444
Table S1
Supplementary material 2 from: Miya M, Sado T, Oka S-i, Fukuchi T (2022) The use of citizen science in fish eDNA metabarcoding for evaluating regional biodiversity in a coastal marine region: A pilot study. Metabarcoding and Metagenomics 6: e80444. https://doi.org/10.3897/mbmg.6.80444
Table S2
Supplementary material 4 from: Miya M, Sado T, Oka S-i, Fukuchi T (2022) The use of citizen science in fish eDNA metabarcoding for evaluating regional biodiversity in a coastal marine region: A pilot study. Metabarcoding and Metagenomics 6: e80444. https://doi.org/10.3897/mbmg.6.80444
Table S4
Supplementary material 5 from: Miya M, Sado T, Oka S-i, Fukuchi T (2022) The use of citizen science in fish eDNA metabarcoding for evaluating regional biodiversity in a coastal marine region: A pilot study. Metabarcoding and Metagenomics 6: e80444. https://doi.org/10.3897/mbmg.6.80444
Supplementary methods
Neophocaena asiaeorientalis Shimonoseki Marine Science Museum, Yamaguchi, Japan. Photo: Grant Abel in Phocoenidae
Neophocaena asiaeorientalis Shimonoseki Marine Science Museum, Yamaguchi, Japan. Photo: Grant Abel
Open Science materials of the paper "Automatically Recognizing the Semantic Elements from UML Class Diagram Images"
<p>This submission contains such files:</p> <p>1. "questionnaire.docx": The questionnaire for the survey. The file contains all the questions and answers.<br> 2. "raw data collected from participants.xlsx": The raw data collected from participants. Each row in the file represents a participant's answers to all questions, including the date, source, IP, and answers.<br> 3. "raw data collected from open-source community.xlsx": The raw data collected from open-source community (the UML diagram usage). It contains the repositories and GitHub URLs, the UML diagrams and the corresponding links, and some statistics about the UML diagram usage.<br> 4. "an implementation of ReSECDI.zip", "utility source code.zip", "utility compiled JAR.zip", and ".m2.zip": An implementation of ReSECDI, and its dependencies. The implementation is in Java, and it requires JDK11 or higher. It depends on a project named "utility", in addition to other dependencies. The source code of "utility" is provided in "utility source code.zip", the compiled JAR file is in "utility compiled JAR.zip", and the maven dependency files are provided in ".m2.zip". The ways to add the "utility" to the implementation's dependencies are explained in the "readme.txt".<br> 5. "instructions for how to use the artifacts.docx": The instructions for how to use the implementation of ReSECDI. It mainly explains the key components of the implementation, and how to set the parameters.<br> 6. "diagrams used for its evaluation.zip": The diagrams used for the evaluation. There are 50 diagrams collected from the open-source community. Each diagram's name represents its belonging repository.<br> 7. "raw data collected during the evaluation.xlsx": The raw data collected during the evaluation. It contains the statistics of the classes and relationships for each diagram, and the recognition results.<br> 8. "Manuscript.pdf": The manuscript explaining our approach.<br> 9. "readme.txt": The readme file explaining details about each file.</p>
Engagement and social impact in tech-based Citizen Science initiatives for achieving the SDGs : A Systematic Literature Review with a perspective on complex thinking
<p>Data set</p>
Supplementary material 2 from: Woodburn M, Buschbom J, Droege G, Grant S, Groom Q, Jones J, Trekels M, Vincent S, Webbink K (2022) Latimer Core: A new data standard for collection descriptions. Biodiversity Information Science and Standards 6: e91159. https://doi.org/10.3897/biss.6.91159
Standards with LtC alignments
Supplementary material 1 from: Woodburn M, Buschbom J, Droege G, Grant S, Groom Q, Jones J, Trekels M, Vincent S, Webbink K (2022) Latimer Core: A new data standard for collection descriptions. Biodiversity Information Science and Standards 6: e91159. https://doi.org/10.3897/biss.6.91159
A summary of the classes in the Latimer Core standard.
Supplementary material 1 from: Martin-Cabrera P, Perez Perez R, Irrison J-O, Lombard F, Ove Möller K, Rühl S, Creach V, Lindh M, Stemmann L, Schepers L (2022) Establishing Plankton Imagery Dataflows Towards International Biodiversity Data Aggregators. Biodiversity Information Science and Standards 6: e94196. https://doi.org/10.3897/biss.6.94196
Imagery dataset example
Figure 1 in The diversity of polychaetes (Annelida: Polychaeta) in a longterm pollution monitoring study from the Levantine coast of Turkey (Eastern Mediterranean), with the descriptions of four species new to science and two species new to the Mediterranean fauna
Figure 1. Map of the study area with the location of monitoring stations.
FIGURE 9 in Unearthing the diversity of Japanese Magelona (Annelida: Magelonidae); three species new to science, and a redescription of Magelona japonica
FIGURE 9. Known distribution records for Magelona japonica.
Sedgwick Museum of Earth Sciences door
52.203066, 0.122016 Source: Objaverse 1.0 / Sketchfab
Supplementary material 1 from: Cardoso A, Tsiamis K, Gervasini E, Schade S, Taucer F, Adriaens T, Copas K, Flevaris S, Galiay P, Jennings E, Josefsson M, López B, Magan J, Marchante E, Montani E, Roy H, von Schomberg R, See L, Quintas M (2017) Citizen Science and Open Data: a model for Invasive Alien Species in Europe. Research Ideas and Outcomes 3: e14811. https://doi.org/10.3897/rio.3.e14811
Appendix 2.
Supplementary material 1 from: Tiago P, Gouveia MJ, Capinha C, Santos-Reis M, Pereira HM (2017) The influence of motivational factors on the frequency of participation in citizen science activities. Nature Conservation 18: 61-78. https://doi.org/10.3897/natureconservation.18.13429
BioDiversity4All Project Survey :
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.