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45 results for “science management”
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.
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>
Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.
<p>Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.</p> <p>Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.</p>
Datasets from Analysis of the strategic management of science, research and innovation thesis
<p>Datasets were obtained from Czech R&D information system and used in my diploma thesis. The thesis (in Czech language) explores system of governance in R&D in the Czech republic and his impacts on the field of molecular biology in the period of 1995-2014.</p>
Research data management in the German-speaking Sports Sciences - Survey on the Status Quo
<p>The data set contains survey data on the status quo of research data management within the German-speaking sports science community. The survey was conducted as an online survey in the period from August 16<sup>th</sup> to September 30<sup>th</sup>, 2023.</p>
Data management in the social sciences in Macedonia [Webinar recording]
<p>This webinar aimed to introduce social science researchers to the basic principles of data management, including the creation of a Data management plan, which is an important tool for planning the research project.</p> <p>The webinar consisted of three parts. The first part introduced researchers with the basic principles of data management including the benefits of adopting Data management plans (DMPs). The DMP follows the research projects’ life cycle, starting with the initial phases of Planning and Organization and documentation of research data. This part also included a presentation of best practices for creation of appropriate structure of folders and data files, as well as instructions for their naming, documentation and organization.</p> <p>The second part of the webinar focused on the following three phases of the project life cycle: Data processing, Preservation and Protection. Contemporary social science presumes the respect of high level ethical standards during the handling of research data, in accordance with legal rules and best practices in this area.</p> <p>The last part of the webinar was dedicated to the phases of Publication - familiarizing the researchers with the possibilities of data preservation and publishing; and Data discovery - discussing the ways and means to acquire social science data, including the secondary use of data produced by other researchers.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=n05WTs58CMY"> the CESSDA Training YouTube channel</a>.</p>
Research Data Management and data protection in the Social Sciences [Workshop recording]
<p>This online workshop organized by The Austrian Social Science Data Archive (AUSSDA) focused on the Research Data Management basics, Data Management Plans and common data protection issues in the Social Sciences.</p> <p>The first part of the workshop was dedicated to RDM basics and Data Management Plans (DMPs). In many projects, DMPs are mandatory deliverables that need to be submitted at the beginning of a project and are updated throughout the project life cycle. During the workshop, it was explained which aspect funders expect to be part of DMPs in Social Sciences and how researchers can benefit from (writing) these documents.</p> <p>In the second part of the workshop, data protection issues that are common in Social Sciences were addressed and how they can be handled. In particular, differences in the curation of quantitative and qualitative data need in order to comply with data protection regulations in general and AUSSDA deposit guidelines in particular. Presentation on how AUSSDA scans quantitative data for potential data protection violations using STATA and gives participants the opportunity to test the code on their own data and devices.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=DhiL9J-Iwqg"> the CESSDA Training YouTube channel</a>.</p> <p> </p>
Illustrative Darwin core archive to input data on a citizen science platform from a collection management system
<p>Illustrative DwC archive to send data from a collection management system to a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG.</p> <p>Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a citizen science platformback to a collection management system, multi-determined case
<p>Illustrative example of data format following Darwin Core sent back from a citizen science platform to the relevant collection management system. Multi-determined herbarium sheet case : http://coldb.mnhn.fr/catalognumber/mnhn/p/p01978557</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a citizen science platformback to a collection management system, simple case
<p>Illustrative example of data format following Darwin Core sent back from a citizen science platform to the relevant collection management system. Simple case : http://coldb.mnhn.fr/catalognumber/mnhn/p/p03558024</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a collection management system to a citizen science platform, multi-imaged case
<p>Illustrative example of data format following Darwin Core to send from a collection management system to a citizen science platform. Multi-imaged vertebrate specimen case : http://coldb.mnhn.fr/catalognumber/mnhn/zo/2013-152</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a collection management system to a citizen science platform, simple case
<p>Illustrative example of data format following Darwin Core to send from a collection management system to a citizen science platform. Simple case : http://coldb.mnhn.fr/catalognumber/mnhn/p/p03558024</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Figures for RAE Editorial 'What Teachers and Researchers in the Area of Business Management Need to Know About Open Science'
<pre>This set of figures is related to an Editorial dedicated to Open Science, published by <a href="https://bibliotecadigital.fgv.br/ojs/index.php/rae/index">Revista de Administração de Empresas (RAE)</a>: </pre>
International Journal of Economics and Management Sciences
<table> <tbody> <tr> <td>International Journal of Economics and Management Sciences (IJEMS) is a double blinded reviewed open access journal published bimonthly. Our journal welcomes scientific productions in all areas of economics and management sciences, both theoretical and applied studies will be taken in consideration for evaluation and publication. All research methodologies are welcome. The main objective of our journal is to publish articles that have a significant managerial and theoretical contributions. <p>Our journal receives contributions from both researchers and professionals in the following areas:</p> <p>- Management <br> - Economics</p> </td> </tr> </tbody> </table>
Illustrative Darwin core archive to output data from a citizen science platform to a collection management system
<p>Illustrative DwC archive to send data back to a collection management system from a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG.</p> <p>Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Data from: Pest management science often disregards farming system complexities
<p><span>Since the 1940s, pesticide-intensive crop protection has sustained food security but also caused pervasive impacts on biodiversity, environmental integrity and human health. Here, we employ a systematic literature review to structurally analyze pest management science in 65 developing countries. Within a corpus of 3,407 publications, we find that taxonomic coverage is skewed towards a subset of 48 herbivores. Simplified contexts are commonplace: 48% of studies are performed within laboratory confines. 80% treat management tactics in an isolated rather than integrated fashion. 83% consider no more than two out of 15 farming system variables. Limited attention is devoted to pest-pathogen or pest-pollinator interplay, trophic interactions across ecosystem compartments or natural pest regulation. By overlooking social strata, the sizable scientific progress on agroecological management translates into slow farm-level uptake. We argue that the scientific enterprise should integrate system complexity to chart sustainable trajectories for global agriculture and achieve transformative change on the ground.</span></p>
[DATASET] Supporting Open Science Hardware in Academia: Policy Recommendations for Science Funders and University Managers
<p>Raw data from a real-time Delphi exercise used to write the policy brief: <a href="https://zenodo.org/record/8030029">Supporting Open Science Hardware in Academia: Policy Recommendations for Science Funders and University Managers</a></p> <p>Access to the Real-Time Delphi platform was kindly provided by <a href="https://4strat.de">4strat</a>. See tab "meta - start here" for description of the dataset.</p>
Assessing and managing environmental hazards of polymers: historical development, science advances and policy options
<p>Supporting information (open data) from Assessing and managing environmental hazards of polymers: historical development, science advances and policy options</p>
Data from: Pest management science often disregards farming system complexities
Open the record for dataset details and reuse information.
Life Sciences in the Fast Lane: Dan Qunitana on Rapid Feedback, Tweeting, and Time Management
<p><strong>Episode Summary:</strong></p> <p>In the last episode of 2019 we talk to Professor Dan Quintana from the University of Oslo about the advantages of sharing preprints and ideas online, fears about getting scooped, and lessons he has learnt about Twitter and time management. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://www.dsquintana.com/publication/">Dan Quintana</a></p> <ul> <li><a href="https://twitter.com/dsquintana">Dan Quintana Twitter</a></li> </ul> <p><a href="https://twitter.com/hertzpodcast">Everything Hertz Podcast</a></p> <p><a href="https://twitter.com/pb_cast">Physiology & Behavior Podcast</a></p> <p><strong>Episode Quotes: </strong></p> <p>"The great thing about social media is it is a great way to organise a lot of people who are like-minded"</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.