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18 results for “Scientific knowledge”
(Rawdata) How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study: a descriptive study
<p>Rawdata used in the article 'How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study', from the project Comscienciaeduspain (FCT-20-15761), executed with the collaboration of the Spanish Foundation for Science and Technology – Ministry of Science and Innovation.</p>
(Processed data) How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study: a descriptive study
<p>Processed data used in the article 'How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study', from the project Comscienciaeduspain (FCT-20-15761), executed with the collaboration of the Spanish Foundation for Science and Technology – Ministry of Science and Innovation.</p>
PubGraph: A Large-Scale Scientific Knowledge Graph
<p>We present PubGraph, a new resource for studying scientific progress that takes the form of a large-scale knowledge graph (KG) with more than 385M entities, 13B main edges, and 1.5B qualifier edges. PubGraph is comprehensive and unifies data from various sources, including Wikidata, OpenAlex, and Semantic Scholar, using the Wikidata ontology. Beyond the metadata available from these sources, PubGraph includes outputs from auxiliary community detection algorithms and large language models. To further support studies on reasoning over scientific networks, we create several large-scale benchmarks extracted from PubGraph for the core task of knowledge graph completion (KGC). These benchmarks present many challenges for knowledge graph embedding models, including an adversarial community-based KGC evaluation setting, zero-shot inductive learning, and large-scale learning. All of the aforementioned resources are accessible at <a href="https://purl.archive.org/pubgraph">https://purl.archive.org/pubgraph</a> and released under the CC-BY-SA license. We plan to update PubGraph quarterly to accommodate the release of new publications.</p>
Dataset for "Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis"
<p>JCIM paper link: <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.2c00359">https://pubs.acs.org/doi/10.1021/acs.jcim.2c00359</a><br><br>Github repo: <a href="https://github.com/nsndimt/CatalysisIE">https://github.com/nsndimt/CatalysisIE</a><br><br><br>Dataset Content:</p> <ul> <li>Pretrained BERT: <code>scibert_domain_adaption.tar.gz</code> extract it to <em>pretrained</em> directory</li> <li>Cross-Validation Checkpoint: <code>cross_validation_checkpoint.tar.gz</code> extract it to <em>checkpoint</em> directory</li> <li>Annotated Data: <code>data.jsonl</code> and <code>split.jsonl</code> put it under <em>data</em> directory</li> </ul>
Instance of the learning ecosystem metamodel: model of the ecosystem for scientific knowledge management in a PhD programme
<p>The learning ecosystem modeled from the Ecosystems Metamodel is oriented to manage the scientific knowledge generated in the scope of the PhD Program on Education in the Knowledge Society at the University of Salamanca (<a href="https://knowledgesociety.usal.es">https://knowledgesociety.usal.es</a>).</p> <p>The model (M1-level) has been divided in three packages: the ecosystem tools model; the ecosystem users model; and the ecosystem services model. Furthermore, the classes from the metamodel (M2-level) are represented to indicate which classes are used to model the example.</p>
LLM-Based Knowledge Graph Construction from Materials Research Scientific Literature
<p>This dataset was constructed by creating a benchmark of 349 manually annotated triples, which were extracted from four different research articles in the field of materials science.</p>
Decision analysis rooted in Indigenous and Western scientific knowledge identifies cost-effective strategies for managing hyperabundant deer to restore keystone places
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International Journal of Hidden Data Mining and Scientific Knowledge Discovery
<p>Welcome to the first issue of the International Journal of Hidden Data Mining and Scientific Knowledge Discovery: http://scholarpage.org/journal</p> <p>We decided to use the following acronym HDSKD for the journal. Hidden data represents data or information that is more or less hidden, unseen or encrypted so that it becomes difficult to extract it. Scientific knowledge represents every information that comes by a scientific way, such as theories, experimental observations or scientific evidences of some phenomenon. The last term in the journal title, namely "Discovery" has a great importance, since it means that we are looking for scientific information particularly and hidden data in general.<br /> Authors are encouraged to submit their latest research results to the HDSKD journal Hence, we are proud to announce the opening of our first journal issue in hidden data mining and scientific knowledge discovery (a peer reviewed journal) and encourage authors to submit their latest research results to HDSKD.<br /> The criteria of selection are based on the review decision, which will lead to the acceptance or reject of the paper.<br /> The readers’ comments are also welcome and everyone is invited to read or download the papers for a scientific or academic purposes. The journal is a non-profit academic source of knowledge and does not permit any commercial use of the articles.</p> <p><br /> The Editorial Board</p>
Decoding Knowledge Claims: the Evaluation of Scientific Publication Contributions through Semantic Analysis
<p>This data were used to compute the RWMD distance as described in the study submitted for the STI 2024 conference, Berlin.</p>
Knowledge needed for large-scale landscape restoration projects: scientific and experiential. 13.2B Session, Society for Ecological Restoration Conference 2024, Room Hurt, 16:30 -18:00, Thursday, 29th August
<p><strong>Disclaimer: </strong>The DOI of this video does not apply to the content of the slides of the contributors' presentations during this session. The authors of each communication maintain their copyright on the content of the presentations' slides.</p> <p><strong>The creators' roles were as follows:</strong> I. V. A., chair, session coordinator, and presenter; B. S., session coordinator; E. P. M., session coordinator, and presenter; H. J., presenter; F. S., presenter; W. E., presenter; G. M., presenter; and P. I., presenter.</p> <p><strong>Session description</strong></p> <p>This session aimed to explore the critical capacities and functions relevant to the large-scale ecosystem restoration (LER) economy and point out the complementarity of scientific and experiential knowledge and the key role of case studies in increasing this resilience. The restoration economy can be defined as “the market consisting of a network of businesses, investors, consumers, and government initiatives engaging in or driving the economic activity related to ecological restoration.” Its segment dealing with LER can be delimited by the large complexity of the system related to the natural, legislative, institutional, funding, project structure, and organizational network to implement the project, as well as of the economic sectors involved in implementing the projects. Success or failure depends on the interplay between the variables describing the environment of the project, its structure, and its functioning. For success, the uptake of scientific information is as important as the experiential (tacit) knowledge of practitioners, stakeholders, and policymakers. Scientific knowledge can be delivered to the LER economy by structures (models) and data (variables and measurements/estimations of their values). Still, in practice, it is often not a limiting resource. Experiential knowledge can be transferred to some extent by narratives (stories, opinions, examples in common language). The role of case studies for LER is to provide hints about what worked and did not work in other situations and, on this basis, to prepare the adaptive management and increase the resilience of each new project. The session provided opportunities for scientists to interact with practitioners about the problem of LER adaptive management and illustrate the issues with examples of scientific knowledge transfer to current or potential projects and case studies. The session was conceived as crosscutting the missions of the LER and European sections of the Society of Ecological Restoration. It is relevant for the development of the restoration economy in the EU through the implementation of the future European Nature Restoration Law, its effectiveness, and its efficiency.</p> <p> </p> <p><strong>Session report</strong></p> <p><em><strong>Three key elements emerged from the session:</strong></em></p> <p>· Even though there is a widespread need for this kind of knowledge in all phases of project and program cycle development, the role of experiential knowledge/ know-how in ecological restoration is not explicitly treated.</p> <p>· The proprietary know-how can be included in portfolios of patents for technologies and services needed in landscape restoration, which are open-access or undisclosed by practitioners. These types are functionally complementary in the restoration industry and appropriate for specific circumstances.</p> <p>· One can test hypotheses about the role of experiential knowledge in the success of landscape restoration through changes in human resources and human resource formation practice by including people or practices that increase the role of experiential knowledge in the overall knowledge capital available in the social ecosystem implementing the restoration programs and projects.</p> <p> </p> <p><em><strong>Narrative of the session:</strong></em></p> <p>The session showed exciting complementarity between various restoration project phases and provided solid arguments about the importance of experiential knowledge [1]. In the first communication, Jeoelen showed us social science research about what factors positively and negatively control cooperation in the restoration ecosystem of individuals, private organizations, NGOs, and public institutions. Then, she provided information about guidelines for practitioners about how to build trustful relations. She was asked what solutions one has when there are people in the team with undesirable traits, and the answer was to avoid such persons from the very beginning or, if one needs that in the team for other qualities, to discuss with them explicitly the problems. The second speaker, Sebastian, contributed with a complex process-based socio-ecological model presentation and its use in decision-making. His question was about the possibility of including details of cost-benefit analysis in the models of stakeholders' decisions, and the answer was that, in principle, it is possible. However, one must also consider the model's optimal complexity in terms of incertitude propagation. In the third presentation, Pal and Erik presented the practical approach to peatland restoration in catchments from Norway. In their context, it seemed feasible because of data available for hydrological modeling from other state monitoring systems and the fact that most restoration projects occurred on public land.</p> <p>Several examples demonstrated the approach's success in a catchment near Oslo, where the public's interest in recreation is high, and there is support for restoration. The question from the audience related their work to the first presentation of the session topic, namely human resources and trust building with stakeholders. In their context, the usual interactions and roundtables with stakeholders before the start of the projects, as well as continuous formal and informal communication in the implementation phase, were enough to build the needed social capital for a successful restoration outcome. In the first online presentation, Martina provided details about non-formal education projects in marine areas that need restoration ecology. The target group was children, but the projects also engaged adults and environmental professionals from the local socio-ecological systems, thus propagating the informed trust building to the whole community. Her details pointed out the experiential approach in education. Iulia from WWF Romania underlined the importance of case studies in communicating restoration's positive and negative aspects to practitioners. She touched on critical points of experiential knowledge related to the genuine national-scale social interest in restoration, despite the significant interest of local users and local authorities and even the existence of funding for restoration. The lack of cooperation between institutions is a limiting factor for up-scaling the restoration to the full Lower Danube River scale. Even in these conditions, WWF-Romania managed to implement many restoration projects on the Danube River and built a solid relationship with the local people by putting an equal accent on nature and people in the design and implementation phase of the projects. Iulia was asked what they do when there are problems with environmental management (such as illegal garbage disposal) that are not formally relevant to the restoration project but occur in the communities they work with. The answer was that they adopted a proactive and solution-oriented approach and started discussions with the people to identify potential solutions and even develop new projects in cooperation that targeted those problems. Finally, Virgil referred to the audience to the content of this supplementary material, as available on the WhatsApp group of the conference in its preliminary form, and communicated about the cross-cutting session 13.0 from Friday as a framework for further discussion of the issues and disentangling opportunities and challenges in landscape restoration. His presentation showed the relationship between scientific knowledge and experiential one (know-how) in the technology readiness level system. It illustrated this notion by developing a TRL6 environmental service for the cumulative impact assessment of multiple active management actions in contaminated river basins, including restoration of buffer zones and control of mining point sources by remediation projects [2].</p> <p> </p> <p><em><strong>Operational concept of experiential knowledge</strong>:</em></p> <p>· Experiential knowledge is undiscursive knowledge needed for practical activities, particularly ecological restoration.</p> <p>· Related terms: know-how, tacit knowledge.</p> <p>· We acquire it by working with experienced people, and the chains of transfer lead to practical traditions.</p> <p>· It can be transferred to some extent by narratives, films, etc, formally communicated by case studies in professional societies, but personal interactions are decisive for the successful transfer.</p> <p>· It complements scientific knowledge, forming the knowledge capital needed for ecological restoration.</p> <p>· It can be proprietary, non-proprietary, disclosed, and undisclosed.</p> <p>· Its availability geographically and in specific phases of the restoration programs and project cycles limits the success of the restoration.</p> <p>· Due to its complexity, large-scale ecosystem/landscape restoration is highly sensitive to the impact of a lack of experiential knowledge.</p> <p>· Experiential knowledge is needed at the first level in all phases of the program and project cycles and in all themes associated with landscape restoration (at general objectives formulation, implementation/monitoring, financing, and supporting the local economies). Appendix 1 of the full report [1] shows a potential distribution of the contributions to landscape restoration sessions and workshops by these themes, referring in several cases to experiential knowledge).</p> <p>· An extra level of experiential knowledge is needed to successfully transfer first-level know-how/ experiential knowledge.</p> <p>· Despite its unstructured and informal/unprocedural reality, one can test hypotheses about the role of experiential knowledge in the success of landscape restoration through changes in human resources and human resource formation practice by including people or practices that increase the role of experiential knowledge in the overall knowledge capital available in the social ecosystem implementing the restoration programs and projects. This complements the challenge of hypotheses testing at scientific standards of active measures in restoring ecological networks (large-scale ecosystems, landscapes).</p> <p><em><strong>Notes</strong></em></p> <p>[1] For the place of the contributions of this session in the general landscape of the conference, one can download the full report here: <a href="https://virgiliordache.com/2024/08/31/scientific-vs-experiential-knowledge-13-2b-session-report-sere-2024-tartu-estonia/">https://virgiliordache.com/2024/08/31/scientific-vs-experiential-knowledge-13-2b-session-report-sere-2024-tartu-estonia/</a></p> <p>[2] The pdf of his presentation is available for download at <a href="https://virgiliordache.com/2024/08/24/grounding-the-landscape-scale-restoration-for-biogeochemical-services-on-cumulative-impact-assessment-a-process-based-approach/">https://virgiliordache.com/2024/08/24/grounding-the-landscape-scale-restoration-for-biogeochemical-services-on-cumulative-impact-assessment-a-process-based-approach/</a></p> <p><strong>Acknowledgments</strong></p> <p>We thank the organizers of the SERE 2024 conference and the technical team involved in broadcasting and registering this video. We also thank to the persons from the audience who contributed with questions and comments.</p>
Interesting Scientific Idea Generation Using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders
<p>Dataset for the Knowledge graph used in the paper "<a href="https://arxiv.org/abs/2405.17044">Interesting Scientific Idea Generation Using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders</a>" by Xueme Gu and Mario Krenn. </p> <p>Nodes represent scientific concepts extracted from 2.44 million paper titles and abstracts and edges are formed when two concepts co-occur in titles or abstracts of over 58 million papers from OpenAlex, augmented with citation information. </p>
Submission to Special Issue of Quantitative Science Studies "Scientific Knowledge Graphs and Research Impact Assessment"
<p>To be added</p>
Climate, caribou and human needs linked by analysis of Indigenous and scientific knowledge
<p><span>Migratory tundra caribou are ecologically and culturally critical in the circumpolar North. However, they are declining almost everywhere in North America, likely due to natural variation exacerbated by climate change and human activities. Yet, the interconnectedness between climate, caribou, and human well-being has received little attention. To address this gap, we bridged</span><span> </span><span>Indigenous and scientific knowledge in a single model, using as an example the Porcupine caribou herd social-ecological system. Our analysis, involving </span><span>688 (fall season) and 616 (spring season) interviews conducted over nine years with 405 (fall season) and 390 (spring season) Indigenous hunters </span><span>from nine communities, demonstrates that </span><span>environmental conditions, </span><span>large-scale temporal changes associated with caribou demography, and cultural practices </span><span>affect hunters' capacity to meet their needs in caribou. </span><span>Our quantitative approach </span><span>bolsters our understanding of the complex relationships between ecosystems and human welfare in environments exposed to rapid climate change and shows </span><span>the benefits of long-term participatory research methods implemented by Indigenous and scientific partners.</span></p>
Publication data for article "Scientific and technological knowledge grows linearly over time"
<p>This dataset includes metadata for academic publications used in the article <em>Scientific and technological knowledge grows linearly over time</em>. The dataset contains citation relationships, publication dates, and academic fields for 213,715,816 publications from 1800 to 2020. These publications cover 292 secondary subjects in 19 major disciplines, including Economics, Biology, Computer Science, Physics, and more. The data are requested from Acemap (https://www.acemap.info, Shanghai Jiao Tong University) and sourced from the last snapshot of Microsoft Academic Graph (MAG) as of December 31, 2021.</p> <p>The dataset includes two gzip-compressed files, which contain all data in CSV format after decompression. Sample data is presented below:</p> <ol> <li>paper_date_refs.csv (paper_date_refs.tar.gz) <ol> <li>paper_id</li> <li>date</li> <li>reference_ids (separated by comma)</li> </ol> </li> <li>field_paper (field_paper.tar.gz) <ol> <li>field_paper/Computer science.csv <ol> <li>paper_id</li> </ol> </li> <li>field_paper/Biology.csv <ol> <li>paper_id</li> </ol> </li> <li>...</li> </ol> </li> </ol>
Climate, caribou and human needs linked by analysis of Indigenous and scientific knowledge
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Merging indigenous and scientific knowledge links climate with the growth of a large migratory caribou population
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Test set - 4023 PubMed abstracts (for manuscript: Extracting knowledge networks from plant scientific literature: Potato tuber flesh color as an exemplary trait )
<p>A .zip archive containing the set of abstracts used in the test set (4023 abstracts from PubMed) in .txt format.</p> <p>This archive contains supplementary files for the manuscript Extracting knowledge networks from plant scientific literature: Potato tuber flesh color as an exemplary trait.</p>
WheatGenomicsSLKG: Wheat Genomics Scientific Literature Knowledge Graph
<p>The <strong>Wheat Genomics Scientific Literature Knowledge Graph </strong>(<strong>WheatGenomicsSLKG</strong>) is a FAIR knowledge graph that exploits the Semantic Web technologies to integrate information about Named Entities (NE) extracted automatically from a corpus of PubMed scientific papers on wheat genetics and genomics.</p> <p>Code an details: https://github.com/Wimmics/WheatGenomicsKG</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.