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6,025 results for “Science”
Survey of Pacific Northwest public land managers science and values project, 2023
This dataset records survey data about public land managers who work in Oregon and Washington (Forest Service, Bureau of Land Management, Fish and Wildlife Service, National Park Service, Oregon Department of Forestry, Washington Department of Natural Resources). Data was collected in 2023 via the online survey platform Qualtrics. Data collection is complete. The dataset includes measures of managers beliefs about 1) variable density thinning of mature growth forests, 2) salvage logging of burned areas, 3) translocation of plant species from hotter and drier seed zones to adapt to climate change. It includes how managers evaluate the usefulness of scientific evidence and the soundness of action prescriptions for each of the three management issues Respondents were randomly assigned to either receive long-term or short-term studies, and positive or negative results. The dataset includes measures of sense of belonging (how much managers believe they belong at their workplace) and measures of public support/public threat (how much they believe the public understands and supports the actions they take on the landscape). The dataset includes respondent agency.
Wildlands and Woodlands Stewardship Science Vegetation Plots in New England 2009-2015
The Wildlands and Woodlands (W&W) initiative is a broad, collaborative effort to protect 70% of New England in forest over the next 50 years. At the heart of this initiative is the awareness that our wooded landscapes provide immeasurable economic, environmental, and cultural benefits and the conviction that we should understand these systems better, manage them wisely, and conserve them for the future. As part of W&W, Stewardship Science seeks to encourage widespread application of an accessible approach to monitoring forests that interested landowners or conservation-minded individuals can use to track changes in their woods over time. Whether the motivation is active management for timber, understanding how forests are being shaped by factors ranging from climate change and ice storms to insect pests, or simple pleasure in observing nature’s dynamics, anyone equipped with a notebook, tape measure, pencil, and the willingness to puzzle through a book of tree identification can readily develop a robust and valuable set of observations. This idea is not new. For over 150 years, leading conservationists and ecological thinkers beginning with Henry David Thoreau have argued that there is much to be learned through simple, long-term measurements of forest growth and change. Yet there are still remarkably few examples of private landowners, land trusts, timber companies, or conservation organizations that base their understanding and management practices on a regular system of observations and measurements. Because the vast majority of forestland in New England is privately owned, most of these lands remain unmonitored, and management plans are often drawn up from casual rather than systematic observation. For more background information on the project, please see the Wildlands & Woodlands Stewardship Science manual. This data package contains vegetation and environmental data on 64 20x20m plots set up in four areas across New England by staff and summer field crews fro
Spatial variability in water chemistry of four Wisconsin aquatic ecosystems - High speed limnology Environmental Science and Technology datasets
Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. We developed a new sensor platform to continuously samples surface water at a range of speeds (0 to > 45 km hr-1) resulting in high-density, meso-scale spatial data. Here, we archive data associated with an Environmental Science and Technology publication. Data include a single spatial survey of the following aquatic ecosystems: Lake Mendota, Allequash Creek, Pool 8 of the Upper Mississippi River, and Trout Bog. Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected).
Madison community science field campaign to assess abundance and distribution of invasive jumping worms.
Asian pheretimoid earthworms of the genera Amynthas and Metaphire (jumping worms) are leading a new wave of co-invasion into Northeastern and Midwestern states, with potential consequences for native organisms and ecosystem processes. However, little is known about their distribution, abundance, and habitat preferences in urban landscapes – areas which likely influence range expansion via human-driven spread. We led a participatory field campaign to assess jumping worm distribution and abundance in Madison, Wisconsin in September of 2017. By compressing 250 person-hours of sampling effort into a single day, we quantified presence and abundance of three jumping worm species across different land-cover types (forest, grassland, open space, residential lawns and gardens), finding that urban green spaces differed in invasibility. We show that community science can be powerful for researching invasive species while engaging the public in conservation. This approach was particularly effective here, where broad spatial sampling was required within a short temporal window.
Database of local seismicity registered on ocean bottom seismometers (OBS). Database related to Bornstein et al. (accepted in Earth and Space Science), PICKBLUE
<p>We assembled a database of Ocean Bottom Seismometer (OBS) waveforms and manual P and S picks from local seismicity, on which we trained PickBlue, a deep-learning picker, using the seismometer data and the hydrophone channel. The dataset belongs to Bornstein et al. (accepted 2023 in Earth and Space Science). The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.</p><p>The complete database is also accessible with SEISBENCH: <br><a href="https://seisbench.readthedocs.io">https://seisbench.readthedocs.io</a><br>SEISBENCH on github:<br><a href="https://github.com/seisbench">https://github.com/seisbench</a></p><p>Related paper:</p><p>Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock., A., Barcheck, G., Grevemeyer, I., Tilmann, F. (accepted 2023 in Earth and Space Science). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning, Earth and Space Science. </p>
Scripts for Patton et al 2020; Science DOI: 10.1126/science.abb9772
<p>Scripts used for all data anaysis for Patton et al. 2020 <em>Science </em>3<span>70: eabb9772. </span><span>DOI: 10.1126/science.abb9772</span></p>
Organized actors at the biodiversity science-policy-society interface
<p>This database was developed in the context of the Deliverable 2.1 of the BioAgora project 'Developing the Science Service for European Research and Biodiversity Policymaking' (<a href="https://bioagora.eu/)">https://bioagora.eu/)</a>. BioAgora is a collaborative European project funded by the Horizon Europe programme (Horizon Europe research and innovation programme, grant agreement No. 101059438). The project's main outcome is intended to be the development of a Science Service for Biodiversity, the principal EU mechanism to connect research and knowledge on biodiversity to the needs of policy making through a continuous dialogue. The ultimate goal of BioAgora and of the Science Service is to support the implementation of the Biodiversity Strategy for 2030, and more broadly the sustainability transition required by the EU Green Deal. The BioAgora project was launched in July 2022 for a duration of 5 years. It gathers a Consortium of 22 partners, from 13 European countries, led the Finnish Environment Institute (Syke). Partners represent a diversity of actors coming from academia, public authorities, SMEs, and associations. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. </p> <p>In order to develop the database, a thorough desk search was conducted to compile an extensive, albeit not exhaustive, list of organizations operating at the science-policy-society interface in the context of biodiversity and sustainability. In collecting the list, we focused on actors operating at EU level, although we also included particularly relevant international, regional or national organized actors. The desk search built upon the work already developed in the context of two pan-European projects, funded by the Seventh framework programme of the European Community: ‘Developing a Knowledge Network for European Expertise on biodiversity and ecosystem services to inform policy making and economic sectors (KNEU, 2010-2014, grant 265299) and ‘Establishing a European Knowledge and Learning Mechanism to Improve the Policy-Science-Society Interface on Biodiversity and Ecosystem Services’ (Eklipse, 2016-2020, grant 690474). The two above-mentioned projects preceded the BioAgora project in that they aimed at understanding and improving the effectiveness of the biodiversity science-policy(-society) interface in Europe. Such projects had thus already compiled extensive databases of relevant organizations in Europe (including national and international actors, in addition to EU level actors), and quantified the relevance of such organizations based on votes cast by project members and based on interviews with key organizations. The database developed through the desk search conducted was further refined with suggestions for relevant organizations provided by BioAgora’s participants and by the representatives of the organizations interviewed during the other steps of the data collection. The data collection processes started in September 2022 and was updated until June 2024. Note that the categories for network types (Columns E-F) are not mutually exclusive. For further details about the development of the database please see Deliverable 2.1 (<a href="https://bioagora.eu/deliverables/">https://bioagora.eu/deliverables/</a>). </p>
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Optimizing laboratory cultures of <i>Gammarus fossarum</i> (Crustacea: Amphipoda) as a study organism in environmental sciences and ecotoxicology
<p>Supplemental code and data for Alther, Krähenbühl, Bucher & Altermatt (2022) 'Optimizing laboratory cultures of <em>Gammarus fossarum</em> (Crustacea: Amphipoda) as a study organism in environmental sciences and ecotoxicology' (DOI: 10.1016/j.scitotenv.2022.158730). The repository folder contains three text files and a corresponding R script.</p> <p>Rerunning the analysis and producing figures requires two raw data files: LabdataAK_v6_210616_Daylength_input.txt and Nutrition_Exp_KaplanMeier_v1_input.txt. In order to reproduce the analysis and figures, run 'AmphipodHusbandry_20220919.R'. Make sure that your working directory is the folder containing all data files, easily achieved by (re)starting R (or R Studio) by double-clicking the R script file in the folder. The analysis script will produce all the figures from the paper, organized in a folder 'Results' and a subfolder 'Supplement'. Figures are prepared as pixel graphics (PNG).</p> <p>The R script was tested in R ver. 4.1.1 (Windows 10, version 21H1), 4.1.3 (macOS 11.6), and 4.2.0 (Ubuntu 22.04. Required packages are survival (version 3.2-13 worked), survminer (version 0.4.9 worked), and vioplot (version 0.3.7 worked).</p>
GESIS - Leibniz Institute for the Social Sciences data access categories
<p>Replication code for extracting and analysing data access categories from the oai-pmh feed provided by the GESIS - Leibniz Institute for the Social Sciences DBK data catalogue. The code utilises the dc_oai-de feed to extract metadata about objects in the data catalogue, this is then edited to retain and summarise information on the four data access categories used by the archive. The oai-pmh metadata is available from GESIS under a CC0 licence.</p> <p>The .csv files extracted from the oai-pmh feed and edited to correct for missing records is also included for replication.</p>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 35 known metabolites(all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in one Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and one organism part (annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable STATO terms. The measurements over these metabolites, which were made in 2 distinct experiments, were extracted from: a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018 a supplementary material table available as a pdf from 'Biosynthesis of monoterpene scent compounds in roses' by Magnard et al, Science 03 Jul 2015 identified by the following doi: https://doi.org/10.1126/science.aab0696. This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR)and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.It is associated to the following project: https://github.com/proccaserra/rose2018ng-notebook with all the necessaryinformation, executable code and tutorials in the form of Jupyter notebooks.</p>
Data for "Breaking the Paywall: The role of Open Journal System as key Open Science infrastructure"
<h3><strong>Context</strong></h3> <p>This research was conducted within the NSF-SEEKCommons Project, a research initiative dedicated to supporting Open Science and Open Access in disciplinary research. The project has a special interest in understanding the role that critical infrastructure has in supporting open initiatives. The Open Journal System (OJS) serves as a long-standing fundamental piece for Open Access throughout the globe. Hence, it provides valuable information about experiences developing, deploying, and maintaining open technologies. </p> <h3><strong>Methods<br></strong></h3> <div> <div>We used mixed methods for our research, triangulating repository data, installation data, interviews, and documentary analysis. We collected repository data using a report generator (Kopp [2018] 2024) that uses repository metadata to present general statistics about a Git project. The resulting information was manually curated, disambiguated, and annotated to have a homogeneous set of developers with information about their institutional affiliation and country. </div> <div> </div> <div>Names are normalized based on the information in qualitative interviews and by browsing the full-extent commits in the GitHub repository. Other sources for this were the institutional materials (available in current and archived versions of the PKP website), meeting minutes, the user forum, and further project documentation available online. GitHub handles are homologated to their most comprehensive version. For institutional and country affiliation, we resorted to GitHub profiles, PKP documentation and forums, institutional domains available in emails, and researchers' ORCID IDs. </div> </div> <h3><strong>Available files</strong></h3> <ol> <li><strong>Information about the codebase</strong> (number of files, lines of code, and timestamp) organized by <strong>month, quarter, and semester. </strong><br>See file: OJS_GitStats_04-24.csv</li> <li>Information about the historical evolution of the codebase (number of files, lines of code, and timestamp), including <strong>a description of the top committers for each month</strong>. Commiters are described by including their institutional affiliation and country of origin. <br>See file: OJS_DevStats_Institution-Country_1.tsv</li> <li>Information about the <strong>historical evolution of the codebase </strong>focusing on <strong>top committers</strong>, along with their institution and country. This file is formatted to map the co-occurrence of developers and attributes by month between 2004-2024.<br>See file: OJS_DevStats_Institution-Country_2.tsv</li> <li>Selected fields to describe<strong> working and regularly maintained plugins for OJS as of October 2024.</strong> Includes name of the plugin, homepage, description, maintainer, and institutional affiliation. <br>See file: OJS_Plugins_2024_Processed.tsv</li> <li>Details of the aggregated <strong>information</strong> included in <strong>Table</strong> <strong>5</strong> of the article.<br>See file: OJS_Plugins_2024_Table5.tsv</li> <li><strong>Snapshot</strong> to XML information of the <strong>plugin gallery of OJS </strong>(October 21) retrieved from PKP website (Smecher 2024)<br>See file: OJS_Plugins_2024.csv</li> </ol> <h3>Funding</h3> <p><span>The SEEKCommons Project is funded by the U.S. National Science Foundation (NSF), grant #2226425</span></p>
COSN paper data (The Chinese Open Science Network (COSN): Building an Open Science community from scratch)
<p>This is the dataset for generating figure1 and figure 3 in the manuscript <em>The Chinese Open Science Network (COSN): Building an Open Science community from scratch </em>(Accepted by AMPPS). Preprint at: <a href="https://doi.org/10.31234/osf.io/ac9by">https://doi.org/10.31234/osf.io/ac9by</a>.</p> <p>All the data and codes are available in repo: <a href="https://github.com/OpenSci-CN/COSN_AMPPS_Paper">COSN_AMPPS_Paper</a> Accepted Version.</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding raw data for the analyses described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains three files:</p> <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.</p>
Data on a citation context analysis focusing on natural sciences and social sciences and humanities
<p>This dataset contains data on citation context analysis between natural sciences (NS) and social sciences and humanities (SSH). In particular, the data were created through manual coding of each citation between papers related to SDG7 (renewable energy) and SDG13 (climate change) and papers cited by them. This dataset consists of 9 files, associated with the article: Nishikawa, K. How and why are citations between disciplines made? A citation context analysis focusing on natural sciences and social sciences and humanities. Scientometrics (2023). <a href="https://doi.org/10.1007/s11192-023-04664-y">https://doi.org/10.1007/s11192-023-04664-y</a></p> <p> </p> <p>The files are numbered as follows:</p> <ul> <li>00 – README</li> <li>01 – Data by citation pair for SDG7 (original)</li> <li>02 – Data by citation pair for SDG13 (original)</li> <li>03 – Data by mention location for SDG7 (original)</li> <li>04 – Data by mention location for SDG13 (original)</li> <li>05 – Data by citation pair for SDG7 (additional)</li> <li>06 – Data by citation pair for SDG13 (additional)</li> <li>07 – Data by mention location for SDG7 (additional)</li> <li>08 – Data by mention location for SDG13 (additional)</li> </ul> <p>See README for more information.</p>
Open Science Quest
<p>The Open Science Quest was an activity organised as part of a national Open Science event in Luxembourg at the university library and was displayed for two weeks from 12 to 23 November 2018. Its aim was for library users (mainly Bachelor and Master students) and the event’s attendees (early-career and senior researchers, librarians and research support staff) to explore and discover Open Science practices at their own pace.</p> <p><br> The activity was stand-alone and promoted independent learning – once set up no external help was needed apart from issuing the diploma and prize for completing the Quest. The aim was also to make the activity as informative and engaging as possible by requiring participants to use a mix of information gathering techniques – text, images and videos presented on the displays, websearch and online tools, (very simple) puzzle-solving. The Quest was created and displayed in such a way that allowed various types of individual learning goals – each display provided knowledge without requiring to do the Quest and the Quest itself could be completed by grasping a minimal of concepts, while allowing participants to get more in-depth knowledge of each subject if they wanted to.</p> <p>All resources and materials used to prepare and showcase the Quest can be found here.<strong> Please read the 'User Guide' and the README files</strong> included in each folder and sub-folder to get more detailed information. You may also be interested in the blogpost: <a href="https://www.openaire.eu/blogs/open-science-quest">https://www.openaire.eu/blogs/open-science-quest</a></p> <p>Share, reuse, adapt and organise your own Open Science Quest! #OpenScienceQuest</p>
Austrian Science Fund (FWF) Publication Cost Data 2014
<p>Following 2013 (http://dx.doi.org/10.6084/m9.figshare.988754), the Austrian Science Fund (FWF) makes its publication costs spent in 2014 (esp. for Open Access) publically available.</p> <p>The dataset includes payments for publications of authors funded by the Austrian Science Fund (FWF) via following programmes:</p> <p>"Peer-Reviewed Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/peer-reviewed-publications/</p> <p>"Stand-Alone Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/</p> <p>In addition to 2013, this dataset includes also costs for Open Access books and other venues.</p>
ALL-READY Questionnare on potential drivers and barriers to the adoption of innovation management, open science, and Intellectual Property Rights (IPR) among the members of the Pilot Network
<p><strong>Background & Summary</strong>: </p><p>The ALL-READY project unites a diverse consortium of Research Infrastructures (RI) and Living Labs, instrumental in developing new methodologies and technologies in agroecology. The project focuses on effective management of innovation, adherence to open science principles, and strategic application of Intellectual Property Rights (IPR). Task 6.4 of the project, which concentrates on Innovation and IPR Management, seeks to understand the dynamics influencing the adoption of these practices among its members. Recognizing the need for end-to-end data management, the project emphasizes standardized data collection and management while adhering to FAIR principles.</p><p><strong>Methods</strong>: </p><p>The questionnaire was developed by LifeWatch ERIC to capture data reflecting current practices and perceptions in agroecology. It included 26 questions divided into four sections, focusing on existing practices, potential drivers, and barriers in innovation management, open science, and IPR. The survey was disseminated via an online platform to the ALLREADY Pilot Network, ensuring a representative sample from diverse organizations. The data collection process was closely monitored, and the responses were analyzed using a mixed-methods approach to extract meaningful insights.</p><p><strong>Data Records of the ALLREADY Project Questionnaire</strong>: </p><p>The dataset, collected through an online survey platform, underwent a meticulous process of data preparation, download, formatting, and anonymization. It consists of one text file containing metadata (Readme.txt) and a single CSV file encompassing all questionnaire responses. The dataset provides a comprehensive view of innovation management, open science adoption, and IPR handling within the agroecology sector, particularly among the network of RIs and Living Labs involved in the project.</p><p><strong>Technical Validation of the ALLREADY Project Questionnaire</strong>: </p><p>Several critical steps were taken to ensure the accuracy, reliability, and overall quality of the data collected. This included development and testing of the questionnaire, rigorous monitoring of the data collection process, and thorough checks for data quality and completeness. The representativeness of the sample was analyzed specifically with respect to the Pilot Network rather than the broader population involved in agroecology. Strategies were employed to counter survey fatigue and maintain respondent engagement.</p><p><strong>Usage Notes for the ALLREADY Project Questionnaire</strong>: </p><p>The dataset's proper usage is vital for ensuring the validity and reproducibility of research. Researchers are advised to consider the nature of the data, the representativeness of the dataset, and its generalizability. The dataset allows for comprehensive analysis and integration of different sections, and analysts have the flexibility to handle open and write-in responses according to their research needs. Additional information to facilitate analysis is provided in a separate documentation file.</p><p> </p>
2020 Citizen Science Event Book: #LongCovid
<p>The Chinese Version "2020 年公民科學事件簿:#長新冠(#Long Covid) ": <a href="https://pansci.asia/archives/370282">https://pansci.asia/archives/370282</a></p><p>The English full text : <a href="https://details-or-fragments.blogspot.com/2023/12/LCEventBook.html">https://details-or-fragments.blogspot.com/2023/12/LCEventBook.html</a></p><p>Long Covid comes from a "patient-created term" in the spring of 2020. On October 6, 2021, the WHO announced its official definition. Although it used "post-COVID-19 condition", the Long Covid is still the most common term. This bottom-up grassroots movement of public participation in scientific concepts in online communities has reached the social conscience of the public and driven scientific development, and finally led to the establishment of relevant policies and scientific progress. This is what sociologists called "citizen science". </p><ul><li>How did it all begin?</li><li>Patient symptom stories: COVID-19 affects more than just the lungs</li><li>Long COVID Citizen Campaign: Responses from health services</li><li>The openness of online social media</li></ul><p>The positive actions of these online community and the collective consensus reached are enough to convincingly prove to medical institutions, including the WHO, that Long Covid is a real disease despite the lack of traditional evidence-based medicine. A group of online citizens collectively wrote the first textbook on Long Covid in 2020. At this moment, we are witnessing the mass power of the online community, which not only promotes real changes in the real world, ensures recognition of medical care supply, but also stimulate a new scientific <i>research</i> stage.</p>
Wild for Orchids Citizen Science Campaign Records 2020 - 2023
<p>This dataset represents the geographic distribution of wild orchids in the Maltese Islands, as recorded by the Wild For Orchids Citizen Science Initiative between January 2020 and December 2023. It includes data obtained through citizen science contributions and has undergone rigorous two-stage quality control for species identification and GPS accuracy. Species identification was carried out according to Mifsud (2018). The location data of each records is provided as a shapefile format projected in ETRS89-extended / LAEA Europe (EPSG:3035), and exact GPS location were transformed in 1 km square grid according to the <span>European Forum for Geography and Statistics (EFGS).<br></span></p> <p><span>Wild for Orchids is a Citizen Science Initiative designed and managed by Green House Malta.</span></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.