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6,025 results for “science”
Unlocking the power of computer modelling and simulation across the life sciences product lifecycle
<p><strong>Unlocking the Power of Computer Modelling and Simulation Across the Life Sciences Product Lifecycle</strong></p> <p>In an era where technology continuously reshapes the boundaries of research and development, the field of life sciences stands at the cusp of a transformative shift. The potent combination of computer modelling and simulation has begun to unlock unprecedented opportunities across the product lifecycle in life sciences, promising to revolutionize everything from medicinal product development to clinical research. Let's delve into how these technological advancements are paving the way for groundbreaking progress in medicine and healthcare.</p> <p><strong>The Fusion of Technology and Life Sciences</strong></p> <p><em>In Silico Methods: A New Frontier in Medicine</em></p> <p>The term 'in silico' refers to computer simulations used in the study of biological and chemical processes. The video highlights the growing importance of in silico methods in the life sciences sector, particularly in the United Kingdom. These methods allow for the virtual testing of new medicinal products, significantly reducing the need for costly and time-consuming physical trials.</p> <p><em>Bridging the Gap with Computational Modeling</em></p> <p>Computational modeling is another key aspect discussed in the presentation. It involves the use of computer algorithms and mathematical models to simulate real-world medical data. This approach enables researchers to predict how medicinal products will behave in various scenarios, including their interaction with different types of patient data. As a result, computational modeling is instrumental in enhancing the precision of clinical research and improving medical equitability by considering a broader range of patient profiles.</p> <p><strong>The Impact on Clinical Research and Patient Care</strong></p> <p><em>Enhancing Precision and Efficiency</em></p> <p>One of the most notable benefits of integrating computer modelling and simulation into the life sciences is the enhanced precision and efficiency it brings to clinical research. By leveraging real-world medical data, researchers can obtain more accurate predictions about the efficacy and safety of new medicinal products. This not only accelerates the development process but also ensures that treatments are more tailored to individual patient needs.</p> <p><em>Promoting Medical Equitability</em></p> <p>The video underscores the role of these technologies in promoting medical equitability. Through the use of patient data simulations, it becomes possible to account for a wider array of genetic, environmental, and lifestyle factors that influence health outcomes. This inclusive approach ensures that the benefits of medical advancements are accessible to a diverse population, addressing disparities in healthcare access and treatment efficacy.</p> <p><strong>Conclusion: The Future is Now</strong></p> <p>The integration of computer modelling and simulation in the life sciences heralds a new era of medical research and patient care. As we continue to explore the potential of these technologies, it's clear that they hold the key to unlocking more efficient, precise, and equitable healthcare solutions. The journey towards fully realizing this potential is just beginning, but the promise it holds is immense. As we stand on the brink of this technological revolution, one thing is certain: the future of medicine and healthcare is being shaped here and now, and it's brighter than ever.</p>
DATASET OF RESPONSIBLE RESEARCH AND INNOVATION IN CITIZEN SCIENCE
<p><span>The research aim was to explore what aspects of citizen science (CS) make the involvement of researchers (the ones who implement CS projects) meaningful in terms of responsible research and innovation (RRI) principles. The following research questions were formulated:</span></p> <p><span>1) How does RRI contribute to the meaningfulness of CS projects and in which CS aspects?</span></p> <p><span>2) What motivates researchers to accommodate RRI principles in CS projects? </span></p> <p><span>3) What impedes researchers in accommodating RRI principles in CS projects?</span></p> <p><span>To answer these research questions, a qualitative research approach was employed using individual semi-structured interviews for data collection. Using a purposive criterion-based sample, inclusion criteria were the following:</span></p> <p><span>(i) European researchers (principal investigators/project managers) that are running (at least) one CS project; </span></p> <p><span>(ii) researchers who may represent different organisational settings with scientific orientation (e.g. academia, museums, and others) within Europe; </span></p> <p><span>(iii) the CS project, started before 2013 (year of introducing the concept of RRI into European Union Research and Innovation (EU R&I) policy) should be ongoing during the research conduct, or the CS project started in the period of 2014–2018 (the year 2014 was a starting point since it is the date of embedding RRI in the EU R&I policy as a mandatory component of all research activities) should be still ongoing; and </span></p> <p><span>(iv) the CS project covers any academic discipline.</span></p> <p><span>To identify potential informants, we used the list of CS projects publicised in Wikipedia (</span><span><a href="https://en.wikipedia.org/wiki/List_of_citizen_science_projects"><span>https://en.wikipedia.org/wiki/List_of_citizen_science_projects</span></a></span><span>) and added CS projects from authors’ home countries. In addition, we posted the invitation to participate in the study in a newsletter within the citizen science community (e.g. ECSA) and in social media targeting specific groups and using hashtags, namely on Facebook and Twitter.</span><span> </span><span>At the end, we identified 117 CS projects relevant to our research aim.</span><span> </span><span>20 CS projects (five females and fifteen males)</span><span> </span><span>consented to take part in the study.</span><span> </span><span>CS projects covered different academic disciplines, such as psychology, zoology, biology, ecology, linguistics, palaeontology, history and others.</span></p> <p><span>We constructed a questionnaire consisting of four items: self-identity and ties with CS, enablers of RRI in CS, limitations of RRI in CS and impact of RRI on CS. Interviews were conducted remotely. The interview language was English, except for one interview that was held in the participant’s first language and then translated into English. Though some interviews had minor language-specific flaws (for most informants English is not a native language), they did not interfere with understanding an informant.</span></p> <p><span>Each interview was audio-recorded, transcribed, and pseudonymized if such request was expressed in the informed consent. Average length of interview was 53 minutes. Non-pseudonymised full interviews contained an average of 6,409 words.</span></p> <p><span>Different strategies were used to validate all interview transcripts for purposes of data accuracy and clarifying inaudible responses (e.g. validation of half of transcripts involved two researchers, then validation of eleven transcripts involved interviewees). </span></p> <p><span>Nine informants allowed to publish pseudonymised transcripts while eight informants preferred to have non-pseudonymised transcripts published. Three informants disagreed to make publish a pseudonymised transcript as open research data.</span></p> <p><span> </span></p> <p><span>The complete research is published as Tauginienė, L., Butkevičienė, E., Heinisch, B., Massetti, L., Ugolini, F., Popov, S. (2024). Making Responsible Research and Innovation Meaningful in Citizen Science. </span><em><span>Science and Public Policy</span></em><span>. https://doi.org/10.1093/scipol/scae078 </span></p>
MARCSI - Inventory of Marine Citizen Science Initiatives and the FAIRness of the data they produce
<p>Inventory (data set) of Marine Citizen Science Intiatives collected and described in the publication entitled "Past and present marine citizen science around the globe: a cumulative inventory of initiatives and data produced" co-authored by Uta Wehn, Ane Bilbao, Luke Somerwill, Torsten Linders, Joan Maso, Stephen Parkinson, Christina Semasingha,<sup> </sup>Sasha Woods.</p>
Data of Survey on National Contributions to EOSC and Open Science 2023
<p>This is the data set of the annual survey on National Contributions to EOSC and Open Science 2023 for the EOSC Steering Board</p> <p>The annual survey on National Contributions to EOSC and Open Science was developed by the EOSC Future project and EOSC Steering Board to monitor policies, practices, and impacts related to EOSC and Open Science at national and institutional levels in Europe</p> <p>The annual survey for 2023 was published in the EOSC Open Science Observatory on 17 January 2024 and ran until 01 July 2024 whereby 32 European member states, associated countries, and other countries responded to the survey</p> <p>The data of the annual survey for 2023 is available and exploitable in the online dashboard of the EOSC Open Science Observatory developed by Technopolis Group and OpenAIRE in the EOSC Future project and continued in the EOSC Track project: [<a href="https://eoscobservatory.eosc-portal.eu">https://eoscobservatory.eosc-portal.eu</a>]</p> <p>Disclaimer 1: The annual survey on National Contributions to EOSC and Open Science is in an initial stage of implementation and will be improved in future iterations whereby the data should for now be taken as a best-effort attempt by participating countries</p> <p>Disclaimer 2: V1 of the data set included an error in the data set and has thus been restricted and replaced by an updated V2 of the data set</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>
Survey Study about Motivation for Participants in Citizen Science Projects
<p>The survey study about motivation for participants in Citizen Science projects was designed within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON). Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc.<br> Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement.<br> The goal of the study is to analyse the motivation to participate of a specific citizen science community and the structure of the survey proposed shuold be customised considering the topic of the activities.</p> <p><br> This research object describes the studies performed within the ACTION project to investigate motivations of different citizen science communities: the TESS Network (<a href="https://tess.stars4all.eu/">https://tess.stars4all.eu/</a>); the 6 ACTION pilots (<a href="https://actionproject.eu/citizen-science-pilots">https://actionproject.eu/citizen-science-pilots</a>) Mapping Mobility, Open Soil Atlas, Water Sentinels, Restart Data Workbench, Wow Nature, Walk Up Aniene.<br> The surveys were designed and administered through Coney (<a href="https://coney.cefriel.com">https://coney.cefriel.com</a>) and made available as linked data exploiting the Survey Ontology (<a href="https://w3id.org/survey-ontology">https://w3id.org/survey-ontology</a>).</p> <p>The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are:</p> <ul> <li><em>*-procedure.ttl</em> contains the RDF representation of the <strong>template</strong> structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-<em>mean-var-motivating-questions.csv </em>contains the computed mean and average for each question considered (observable variables) comparing all the surveys performed</li> <li>*-<em>mean-var-motivating-factor.csv </em>contains the computed mean and average for each motivation factor considered (latent variables) comparing all the surveys performed</li> <li>*-<em>correlation-factors-global-motivation.csv </em>contains the correlation analysis between each motivation factor and the global motivation comparing all the surveys performed</li> </ul> <p>The Research Object also references all the RO-Crates describing the different survey motivation studies in details.</p>
Supplementary data to Dating the timbers from the 'Sparrow-Hawk', a shipwreck from Cape Cod, USA. Journal of Archaeological Science: Reports 103374
<p>This record gives access to all supplementary data that forms the background to the paper: Daly, A., Hocker, F. & Mires, C., 2022. Dating the timbers from the ‘Sparrow-Hawk’, a shipwreck from Cape Cod, USA. Journal of Archaeological Science: Reports https://doi.org/10.1016/j.jasrep.2022.103374</p> <p>In 1626, a vessel making its way to Virginia was forced off course and damaged in a storm, which drove the ship onto the eastern shore of the Cape Cod peninsula, Massachusetts. Onboard were two English merchants and some servants and farmers, many of whom were Irish. In 1863, a storm exposed the weathered remains of a vessel at Old Ship Harbor. At the time, it was hailed as the same ship that had brought the Virginia-bound passengers to Plymouth in 1626. Recent wiggle-match C14 dating and dendrochronology suggests that this is indeed a ship from the early seventeenth century.</p>
Science 2015 Farley et al Otx-a Library
<p>This dataset represents SEL-seq data generated by first constructing a dictionary of unique barcode tag-enhancer pairs by allowing 2 bp mismatches in the ~69 bp enhancers to buffer the sequencing mistakes. If more than one barcode tag was associated with a single enhancer the maximum reads between these tags were used. Barcoded tags that were attached to multiple enhancers were removed. The resulting dictionary contains 2,534,802 enhancers that are uniquely mapped to one or more barcode tags. 2 biological replicates were used in this experiment and the reads per million total reads (RPM) was used for each tag. In total 163,708 enhancers were detected by RNA-seq and 21,799 of them were defined as active enhancers by RPM ≥ 4 in either of the 2 replicates.</p> <p>Citation: Farley EK, Olson KM, Zhang W, Brandt AJ, Rokhsar DS, Levine MS. 2015. Suboptimization of developmental enhancers. <em>Science</em> <strong>350</strong>:325–328. doi:10.1126/science.aac6948</p> <p>Paper download link: https://www.science.org/doi/suppl/10.1126/science.aac6948/suppl_file/aac6948_tables_s1_and_s2.xlsx</p>
Number of Attendees of Hungarian Open Science Forum Events, and Number of Visitors of KIFÜ Open Science Newsfeed
<p>Number of attendees of Hungarian Open Science Forum events, which took place on 28/5/2021, 24/9/2021, 19/1/2022, and 28/4/2022.</p> <p>Number of visitors of KIFÜ open science newsfeed till 30/6/2022. Altogether 133 posts that were published between 5 May 2021 and 7 April 2022.</p>
Annual Article Processing Charges (APCs) and number of gold and hybrid open access articles in Web of Science indexed journals published by Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley 2015-2018
<p><strong>Dataset of annual Article Processing Charges (APCs) for 6,252 journals from 2015 to 2018. </strong>The dataset contains annual APCs for journals indexed in the Web of Science (WoS) and published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley). It also includes an estimate of the total APCs paid by the academic community based on the number of gold and hybrid articles published between 2015 and 2018. The dataset was created using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets (<a href="https://doi.org/10.5281/ZENODO.3841568">Matthias, 2020</a>; <a href="https://doi.org/10.5683/SP2/84PNSG">Morrison, 2021</a>) and historical fees retrieved via the Internet Archive Wayback Machine. </p> <p>Detailed methods and findings are reported in the following journal article</p> <p>Butler, L.-A., Matthias, L., Simard, M.-A., Mongeon, P., & Haustein, S. (2023). The Oligopoly's Shift to Open Access. How the Big Five Academic Publishers Profit from Article Processing Charges. <em>Quantitative Science Studies</em>. Preprint: <a href="https://doi.org/10.5281/zenodo.8322555">https://doi.org/10.5281/zenodo.8322555</a></p> <p><strong>Description of included files (v1):</strong></p> <p><em>APCs.csv: </em>contains the annual APCs for gold and hybrid OA journals indexed in Web of Science published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley) between 2015 and 2018 including the total estimate of APCs paid per journal per year. It contains APC data for 18,846 journal-year-OA status combinations.</p> <p><em>countries.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs paid per country per journal per year.</p> <p><em>oecd.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs per discipline per journal per year.</p> <p><em>ReadMe.csv</em>: contains a description of the variables used in <em>APCs.csv</em>, <em>countries.csv</em> and <em>oecd.csv</em>.</p> <p> </p>
Dataset for the publication "The TACS Model: Understanding Teachers' Adoption of Computer Science Pedagogical Content in Primary School"
<p>This dataset contains the quantitative teacher data used to analyse an in service teacher training program for Computer Science that took place from September 2019 to March 2020 in the Canton Vaud in Switzerland. Approximately 180 teachers from the the 5th and 6th grade in primary school (ages 9-11) participated in 3 days of training sessions. At the end of each training session, teachers were asked to fill in a web-based questionnaire providing information relating to their perception of the training sessions and adoption of the computer science activities. The surveys were analysed from three perspectives which are detailed in the corresponding article (the professional development program's perspective, the activities' perspective, the teacher's perspective). The present repository thus contains three csv files, one per analysis. A README is included and provides additional information regarding :</p> <p>- the requirements for re-use. </p> <p>- the survey instrument used</p> <p>- the specific content of the 3 csv files</p>
Citizen Science projects on Alien Species in Europe
<p><strong>Context</strong></p> <p>This survey relates to COST (European Cooperation in Science and Technology) Action CA17122 - Alien CSI - Increasing understanding of alien species through citizen science (see https://alien-csi.eu/). The main aim of this survey was to collect information on Citizen Science projects/initiatives involving alien species in European Member States and some neighbouring countries. The survey was performed using a google forms. Survey respondents/contributors are mentioned in this dataset as data collectors. </p> <p><strong>Definitions</strong></p> <p>We defined Citizen Science projects as project which actively involved citizens in scientific enquiry generating new knowledge or understanding on alien species. Citizens may act as contributors, collaborators, or as project leader and have a meaningful role in the project. 'Alien Species' are defined as any live specimen of a species, subspecies or lower taxon of animals, plants, fungi or micro-organisms introduced outside its natural range; it includes any part, gametes, seeds, eggs or propagules of such species, as well as any hybrids, varieties or breeds that might survive and subsequently reproduce. Alien Species thus includes both species that are invasive and species that are alien but not invasive. An 'Invasive Alien Species' is defined as an alien species whose introduction or spread has been found to threaten or adversely impact upon biodiversity and/or related ecosystem services.</p> <p><strong>Survey methodology</strong></p> <p>The survey was made available on Google Forms and disseminated online, collecting responses from June 27, 2019 to April 6, 2020. It was shared with all COST Action CA17122 participants and in each country one person coordinated contacts with existing citizen science projects involving alien and/or invasive species and requested that they complete the survey. Thus, all projects were active in EU member states and neighbouring countries, though some may also be active outside of Europe. To increase reach, the survey was also disseminated through the European Citizen Science Association (ECSA) newsletter and mailing list and respondents were asked to share it with colleagues and local networks via snowball sampling.</p> <p><strong>Questions and attribute values</strong></p> <p>Survey questions and attribute values were developed using JRC metadata standards for CS projects (Bio Innovation Service 2018) and the project metadata model of PPSR Core, a set of global, transdisciplinary data and metadata standards for Public Participation in Scientific Research (https://core.citizenscience.org/). The survey included 62 questions in nine sections:</p> <ol> <li>Contact information of the respondent;</li> <li>General characterization of the project, including a brief summary, geographical scope, time scale, hosting entities, funding, etc.; </li> <li>Information on project scope, including target audience, taxonomic and environmental scope, project aims, type of data collected, etc.;</li> <li>Policy-related information, namely if the project has policy relevance and inclusion of species listed in the EU IAS Regulation;</li> <li>Information on engagement, such as type of involvement of citizens in the design of the project, engagement methods and social media used, skills needed to participate and frequency of contributions;</li> <li>Information on feedback and support provided to participants by the project, e.g., if projects provide materials for species identification, guidelines, training activities, information on how data from the project are used, feedback mechanisms and support; </li> <li>Data quality and data management, namely validation mechanism for records, registration type, methods of recording, whether data are open and accessible to citizen scientists, data form used to store data, data standards and data licence used, whether a public data management plan was drafted for the project, and the vocabulary used with respect to biological invasions (origin, occurrence status, degree of establishment and pathway of introduction);</li> <li>Performance indicators of projects, namely, usage of apps, number of participants and number of records, whether learning is assessed, number and type of publications using data from the project; </li> <li>Notes and remarks.</li> </ol> <p><strong>Files</strong></p> <ul> <li><strong>raw_data.xlsx</strong>: includes the non-processed survey responses, supplemented with a project_ID. All GDPR sensitive data such as email addresses were omitted. Each row represents one project.</li> <li><strong>projects_excluded.csv: </strong>includes all projects that were omitted from the analysis and the specific criteria for this exclusion. </li> <li><strong>processed_data.csv</strong>: includes the cleaned, processed survey responses, used for analysis. The R code used for the analysis is available on <a href="https://github.com/alien-csi/inventory-analysis/blob/master/src/analysis.Rmd">this github repository</a>.</li> <li><strong>survey.pdf: </strong>a pdf extract from the original Google Forms, including all questions and their specifications. </li> <li><strong>analysis.Rmd</strong>: Rmarkdown script for statistical analysis. Also available on <a href="https://github.com/alien-csi/inventory-analysis/blob/master/src/analysis.Rmd">this github repository</a>.</li> </ul> <p> </p> <p> </p>
ENLIGHT Open Science Surveys datasets
<p>This data was collected in the context of Open Science surveys of the <a href="https://enlight-eu.org/" rel="nofollow">ENLIGHT</a> European university alliance.</p> <p>ENLIGHT RISE collected data from its partner universities:</p> <ul> <li> <p>Information on research data management (RDM) policies, support services and other activities was collected in November 2021. The dataset contains information from 9 partner universities.</p> </li> <li> <p>Information on Open Science (OS) activities, infrastructure and support, skills and knowledge, community activities, policy, recognition and rewards, and environment was collected from 11 December 2021 until 31 January 2022. The dataset contains information from 9 partner universities.</p> </li> <li> <p>Updated information on OS and RDM policies, support and other activities was collected in February/March 2024. Moreover, views on joint achievements and possible future actions were investigated. The 2024 dataset contains information from 10 universities (one additional partner joined on 1 December 2023).</p> </li> </ul>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in Asia
<p>Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In Asia, a diverse array of research outputs and resources are produced and published in repositories. However, a significant number of these repositories, and outputs remain undiscoverable in global registries and aggregators. <br><br>These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in Asian countries. It includes detailed records from different registry sources and repository platforms.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in Asia' at <a href="https://doi.org/10.5281/zenodo.12566244">https://doi.org/10.5281/zenodo.12566244</a></p>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </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 (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. </p> <p>The data were extracted from:</p> <ul> <li>a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018</li> <li>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: <a href="https://doi.org/10.1126/science.aab0696">https://doi.org/10.1126/science.aab0696</a></li> </ul> <p>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.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p> <p> </p>
Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)
<p>This data set contains intracranial EEG data, analysis code and results associated with the manuscript, "<strong>Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans</strong>". [DOI: 10.1126/science.aax1030]</p> <p>Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: “Norman_et_al_2019_data_and_code_zenodo.zip".</p> <p>The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.</p> <p>Matlab's Signal Processing Toolbox is required.</p> <p><strong>General notes:</strong></p> <p>1) The data does not contain identifying details about the patients, nor voice recordings.</p> <p>2) Before running the analyses, make sure you set the correct paths in the "startup_script.m" located in the main folder where the zip file was extracted.</p> <p>3) To run the code, the following open-source toolboxes are required:</p> <ul> <li><strong>EEGLAB</strong> (<a href="https://sccn.ucsd.edu/eeglab/download.php">https://sccn.ucsd.edu/eeglab/download.php</a>), version: "eeglab14_1_2b". <ul> <li>A. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. <em>J. Neurosci. Methods</em>. <strong>134</strong>, 9–21 (2004).</li> </ul> </li> <li><strong>Mass Univariate ERP Toolbox</strong> (<a href="https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox">https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox</a>), version: "dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4". <ul> <li>D. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. <em>Psychophysiology</em>. <strong>48</strong>, 1711–1725 (2011).</li> </ul> </li> </ul> <p>*** Make sure you download the relevant toolboxes and save them in the "path_to_toolboxes" before running the analysis scripts (see "startup_script.m")</p> <p>4) Code developed by other authors (redistributed here as part of the analysis code):</p> <ul> <li>DRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b. <ul> <li>L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. <strong>Dimensionality Reduction: A Comparative Review</strong>. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.</li> </ul> </li> <li>Scott Lowe / superbar (<a href="https://github.com/scottclowe/superbar">https://github.com/scottclowe/superbar</a>), version: 1.5.0.</li> <li>Oliver J. Woodford, Yair M. Altman / export_fig (<a href="https://github.com/altmany/export_fig">https://github.com/altmany/export_fig</a>).</li> </ul>
Gaia Photometric Science Alerts Crossmatch with Gaia DR3 - Feb. 2024
<p>The <a href="http://gsaweb.ast.cam.ac.uk/alerts/home">Gaia Photometric Science Alerts</a> were crossmatched to the Gaia DR3 catalog on <strong>February 23rd, 2024</strong>. We assumed a 1" separation for each crossmatch. The crossmatch was conducted using the <a href="https://lsdb.readthedocs.io/en/latest/">Large Survey Database</a> (LSDB). </p>
Vortex Catalog from Jovian Vortex Hunter Zooniverse citizen science project
<p>This dataset contains the aggregated results from the <a href="https://www.zooniverse.org/projects/ramanakumars/jovian-vortex-hunter/" target="_blank" rel="noopener">Jovian Vortex Hunter citizen science project</a>, where citizen science volunteers labeled images from the JunoCam instrument and determined locations and sizes of vortices.</p> <p>The CSV file contains results from the first workflow and details the consensus from volunteers on different features in each image (given by the Zooniverse Subject ID). There are five categories to choose from:</p> <ol> <li>Vortex</li> <li>Turbulent (i.e. Folded Filamentary Regions: FFRs)</li> <li>Cloud bands</li> <li>Blurry (i.e., image issues)</li> <li>Featureless (there is no discernable feature in the image)</li> </ol> <p>The CSV file also contains an additional column defining the number of classifications of the subject.</p> <p>The JSON file contains the aggregated catalog of vortices and their properties from the second workflow. The format of the JSON file is as below:</p> <p>Each entry contains a dictionary of vortex properties, as shown below. The entry is determined by aggregating the vortex properties across multiple Zooniverse images which share the vortex, and building a consensus from multiple volunteer responses.</p> <pre><code>{ "subject_ids": [array of Zooniverse subject ID for each vortex], "perijove": the perijove corresponding to the image from this vortex was determined "color": the aggregated color of the vortex determined from volunteer responses, "lon": System III planetographic longitude [degree], "lat": planetographic latitude [degree], "x0", "y0": reference coordinate on the Zooniverse crop image for longitude/latitude, "x", "y": coordinate of the vortex center on the Zooniverse crop image "rx", "ry": radius of the vortex in pixel coordinates on the Zooniverse crop image "angle": angle in degree from horizontal of the orientation of the vortex in the Zooniverse crop image, "sigma": 1sigma error in the scale of the vortex, "angular_width": width of the vortex in degrees on the planet, "angular_height": height of the vortex in degrees on the planet, "physical_width", "physical width": width/height of the vortex in km, "extracts": [ array of dictionaries consisting of individual ellipses that make up the vortex consensus ] "colors": { "brown": consensus on the vortex being brown [0-1], "red": consensus on the vortex being red [0-1], "dark": consensus on the vortex being dark [0-1], "white": consensus on the vortex being white [0-1], "white-brown": consensus on the vortex being white and brown [0-1], "white-red": consensus on the vortex being white and red [0-1], "red-brown": consensus on the vortex being red and brown [0-1], } }</code></pre> <p>Each extract is a dictionary containing the following properties. An extract is a single aggregated ellipse on a single Zooniverse image.</p> <pre><code>{ "subject_id": Zooniverse subject ID, "perijove": the perijove when the JunoCam image was taken, "color": the color of the vortex with the highest vote fraction, "lon": System III longitude of the vortex [degree], "lat": planetographic latitude of the vortex [degree], "x0", "y0": reference coordinate on the Zooniverse crop image for longitude/latitude, "x", "y": coordinate of the vortex center on the Zooniverse crop image "rx", "ry": radius of the vortex in pixel coordinates on the Zooniverse crop image "angle": angle in degree from horizontal of the orientation of the vortex in the Zooniverse crop image, "probability": the 1sigma error in the scale of the vortex, "angular_width": width of the vortex in degrees on the planet, "angular_height": height of the vortex in degrees on the planet, "physical_width", "physical width": width/height of the vortex in km, }</code></pre>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in the Middle East and North Africa (MENA)
<div> <p>Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In the Middle East and North Africa (MENA) region, a diverse array of research outputs and resources are produced and published in repositories. However, a significant number of these repositories, and outputs remain undiscoverable in global registries and aggregators. <br><br>These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in MENA countries. It includes detailed records from different registry sources and repository platforms.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in MENA' at <a href="https://doi.org/10.5281/zenodo.11370031">https://doi.org/10.5281/zenodo.11370031</a></p> </div>
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.