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115 results for “Scientific research”
Global scientific research commons under the Nagoya Protocol: Towards a collaborative economy model for the sharing of basic research assets
<p>This paper aims to get a better understanding of the motivational and transaction cost features of<br /> building global scientific research commons, with a view to contributing to the debate on the design of<br /> appropriate policy measures under the recently adopted Nagoya Protocol. For this purpose, the paper<br /> analyses the results of a world-wide survey of managers and users of microbial culture collections, which<br /> focused on the role of social and internalized motivations, organizational networks and external<br /> incentives in promoting the public availability of upstream research assets. Overall, the study confirms<br /> the hypotheses of the social production model of information and shareable goods, but it also shows the<br /> need to complete this model. For the sharing of materials, the underlying collaborative economy in<br /> excess capacity plays a key role in addition to the social production, while for data, competitive pressures<br /> amongst scientists tend to play a bigger role.</p>
(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>
Including Data Management in Research Culture Increases the Reproducibility of Scientific Results
<p><strong>General Information:</strong></p> <p>This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended. </p> <p>The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268<br> The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30</p> <p>This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data.</p> <p><strong>Run the code:</strong></p> <p>To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt". </p> <p>Alternatively, you can import the .ipyab file in the colab [2] and run it. </p> <p><br> [1]. https://www.anaconda.com/<br> [2]. https://research.google.com/colaboratory/<br> </p>
D1.2 Requirements and needs of scientific communities from ICT-based Research Infrastructures (Dataset)
<p>A user survey was conducted between December 2020 and January 2021 gathering inputs from potential SLICES users from the research community. The survey was distributed among the research community to identify the technological domains, the use cases, the requirements and other expectations from the future users of the SLICES research infrastructure. This dataset contains the results of the survey; 226 people participated.</p>
Improving access to and reuse of research results, publications and data for scientific purposes - Stakeholders' consultations results
<p>The data sets were created via data collection effort for the Horizon Europe-funded "study to evaluate the effects of the EU copyright framework on research and the effects of potential interventions and to identify and present relevant provisions for research in EU data and digital legislation, with a focus on rights and obligations". The study was contacted by DG RTD. </p> <p>This research project supports Action 2 objectives of the European Research Area (ERA) Policy Agenda 2022-2024, which aims to propose an EU legislative and regulatory framework for copyright and data that is fit for research. The report provides a comprehensive analysis of barriers to the access and reuse of publicly funded research, including scientific publications and data. It assesses existing EU copyright legislation and EU data and digital legislation. It also assesses regulatory frameworks and national initiatives and identifies potential areas for improvement.</p> <p>Using a methodological, evidence-based approach (including the survey results posted in this repository), the study presents possible legislative and non-legislative measures to improve the current EU copyright and data framework and align it with the needs of scientific research and open research data principles. </p> <p>The data sets include the raw data of the three surveys (survey 1 targeted at researchers, survey 2 targeted at research-performing organisations, and survey 3 targeted at publishers). All surveys have two major parts: one concerning copyright legislation and another concerning data and digital legislation. In addition, we provide interview notes, they are also organised into two parts: one concerning copyright legislation and another concerning data and digital legislation. </p> <p>The data collection effort was partially supported by our colleagues from the Institute for Information Law (IVIR) and KU Leuven CiTIP. </p>
Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software
<p>Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.</p>
Estimating heavy metal deposition in Germany using model calculations and biomonitoring data, link to research data and scientific software
<p>Research data and scientific software related to an investigation dealing with modelled data on Cd and Pb deposition (LOTOS-EUROS, EMEP/MSC-East) and monitoring data from the International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops (ICP Vegetation Moss Survey) and the German Environmental Specimen Bank (ESB) providing corresponding parameters on HM concentration in various biota. The study aimed at examining, whether an integrated use of model calculations and monitoring data can extend established methods for estimating and evaluating spatial patterns of atmospheric Pb and Cd deposition across Germany.</p>
Fuzzy modelling and mapping soil moisture in Germany, link to research data and scientific software
<p>Research data and scientific software related to spatio-temporal estimations of ecological soil moisture with available data covering the whole territory of Germany and the Kellerwald National Park (Hesse). Temporal trends of modelled soil moisture for the time period 1961–2070 were statistically analyzed. Soil moisture changes (drying-out) at both national and regional levels were mapped.</p>
Research Survey on 'Effects of the Coronavirus Pandemic on Scientific Research'
<p><strong>1. The aim of the research</strong></p><p>The survey aimed to collect opinions on how the pandemic has changed scientific research, especially in regards to the usage of different forms of digital tools. The onset of COVID-19 has had an immediate impact on how scientists were able to use their time and resources to do their work. However, there was a lack of real-time data on how they are responding to this event and how it has had differential impacts across scientific fields.</p><p><strong>2. Research instrument and subject</strong></p><p>A survey questionnaire prepared in English was developed for collecting empirical data from academics in Poland and abroad. We used the same version of the survey questionnaire both in Poland and in other countries in the world. The survey questionnaire consisted of several parts. Its first part included questions on demographic information. The second part of the survey questionnaire contained questions about how respondents' work hours were allocated for different activities during, and after the coronavirus pandemic outbreak and the predicted changes in future publication and funding related to spending on researching, writing during and after the coronavirus pandemic. The last part of the survey questionnaire included questions about scientific works, their quality, and support by ICTs during the coronavirus pandemic, as well as the implications of the pandemic for these issues in the future. At the end of the survey questionnaire, we asked respondents about their opinions with regard to the forecast situation in research and education after the coronavirus pandemic. Selecting a sample is a fundamental element of a quantitative study. Stratified sampling was used to obtain the sample, which can be taken to be true for the whole population. The strata were identified based on country, age, gender, position type, and research discipline. To gather a substantial number of respondents, snowball sampling was pursued, which involved daily and routine distribution (social media and e-mail posting) of an introductory e-letter and survey-link requesting participation in the research. To increase response rates, the following methods were used: involving academics (encouraging colleagues), pushing the survey (providing respondents with the survey URL in e-mails sent directly to them), publishing the project with a link for the basic questionnaire on the ResearchGate website and Facebook fan pages and providing frequent reminders.1</p><p><strong>3. Data collection</strong></p><p>Based on several analyses showing that surveys conducted over the internet provide results that are as valid as more "traditional" methods and due to social distancing caused by the coronavirus pandemic, we used the Computer Assisted Web Interview (CAWI) method for recruiting respondents and collecting data. The LimeSurvey tool was employed for recruiting reliable samples. The data were collected during a two-month period of work, between June 11, 2020, and August 18, 2020. This led to 982 responses. After screening the responses and excluding outliers, 476 usable, correct, and complete responses were collected. This uploded dataset is a subset of a larger dataset that the authors collected for their research project titled "The Effects of the Coronavirus Pandemic on Scientific Research and University Teaching". It includes the questionnaire responses related to research during and after the COVID-19 pandemic. The questions were designed to gather information about the impact of the pandemic on academic scientists' research.1</p><p><strong>4. Results</strong></p><p>Our results may suggest targeted policies to alleviate the disruptions experienced by specific scientific fields. The findings of the past research were documented in three articles, which can be found below: </p><p>1. Ziemba, E. W., & Eisenbardt, M. (2022). The effect of the Covid-19 pandemic on ICT usage by academics. Journal of Computer Information Systems, 62(6), 1154-1168. DOI: 10.1080/08874417.2021.1992806 </p><p>2. Wartini-Twardowska, J., Grabara, D., & Ziemba, E. W. (2021). The Influence of the COVID-19 Pandemic on the Use of Digital Technologies by Scientists: A Comparison Between Poland and Abroad. Problemy Zarządzania, 19(3/2021 (93), 12-31. DOI:10.7172/1644-9584.93.1 </p><p>3. Maruszewska, E. W., Eisenbardt, M., & Tuszkiewicz, M. (2022). COVID Pandemic as a disruptive factor enhancing ICT USE in social sciences' teaching practices. Scientific Papers of Silesian University of Technology. Organization & Management/Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie, (160). DOI: 10.29119/1641-3466.2022.160.25 </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>
Search results for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)
<p>This datasets is the result of the search for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)</p>
Dataset of "Raising Awareness for Inertial Sensors-based Keylogging on Smartphones" scientific research
<p>Dataset for the article</p> <p>Federico Montori, Luca Sciullo, and Luca Bedogni. 2024. Raising Awareness for Inertial Sensors-based Keylogging on Smartphones. In Proceedings of the 2024 International Conference on Information Technology for Social Good (GoodIT '24). Association for Computing Machinery, New York, NY, USA, 14–21. https://doi.org/10.1145/3677525.3678634</p> <p>Please cite the above paper if you are using this dataset.</p>
Supplementary data for "Undergraduate Gender Diversity and the Direction of Scientific Research" (PART 3)
<p>Supplementary data for "Undergraduate Gender Diversity and the Direction of Scientific Research" (PART 3)</p> <p>Contains publicly available data from:</p> <ul> <li>Higher Education Research Institute. 1966–2006. “Cooperative Institutional Research Program(CIRP) Data Archives of the Freshman Survey Trends from 1966 to 2006.”</li> <li>National Center for Science and Engineering Statistics. 1972–1990. “Higher Education Research and Development Survey (HERD).</li> <li>U.S. National Center for Education Statistics. 1993. 120 Years of American Education: A Statistical Portrait. Washington, DC:U.S. Department of Education.</li> <li>U.S. National Center for Education Statistics. 2005. “Digest of Education Statistics, 2005.”</li> <li>U.S. National Center for Education Statistics. 2019. “Digest of Education Statistics, 2019.”</li> </ul> <p>To replicate the results in "Undergraduate Gender Diversity and the Direction of Scientific Research", unzip all folders in this repository and place all files and folders (except "PapersFieldsofStudy.txt.gz") in "data/raw". AEA Data and Code Repository openicpsr-204361 shows the expected file structure. </p> <p>Additionally, this repository contains "PapersFieldsofStudy.txt.gz" from Microsoft Academic Graph data (April 2018 Extract) used for "Undergraduate Gender Diversity and the Direction of Scientific Research." To replicate the results in "Undergraduate Gender Diversity and the Direction of Scientific Research", please decompress and place "PapersFieldsofStudy.txt.gz" into "data/raw/MAG/". The associated code repository can be found AEA Data and Code Repository openicpsr-204361.</p> <p>The attribution license of the Microsoft Academic Graph is <a href="https://opendatacommons.org/licenses/by/1-0/">ODC-BY</a></p> <p>Please cite the following paper in publications and reports using the Microsoft Academic Graph:</p> <p>Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839</p> <p> </p>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Public and Stakeholder Engagement
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Ethics and Ethical Governance
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
The impact of the COVID-19 pandemic on scientific research in the life sciences
<p>This repository contains the code to replicate the analysis for the paper: "The impact of the COVID-19 pandemic on scientific research in the fife sciences." Detailed instructions on how to run the code are available in the README.md</p>
Fig. 3 in Scientific note A portable bio-amplifier for electric fish research: design and construction
Fig. 3. Dipole electrodes designed for field (a) and laboratory (b, c) use. For each, positive pole (silver ring) is marked +, with ground pole in middle and negative pole at opposite end. The ground pole from b is magnified in c. Note more robust cable in the field electrode.
Fig. 2 in Scientific note A portable bio-amplifier for electric fish research: design and construction
Fig. 2. External (a) and internal (b) views of the bio-amplifier. a: 1, dipole input; 2, power switch; 3, power LED; 4, BNC output to oscilloscope/digitizer; 5, earphone jack; 6, fine gain control; 7, covered ground stub; 8, coarse gain switch; 9, dipole reversal switch; 10, earphone volume control. b: 1, battery bay (rechargeable batteries removed); 2, battery terminals; 3, circuit board; 4, operational amplifier; 5, resistor; 6, trimmer potentiometer; 7, mount to bracket; 8, electrolytic capacitor; 9, ceramic disk capacitor; 10, 4pF ceramic disk capacitor across R20 as optional high-frequency attenuator.
Fig. 1. Circuit plan for bio-amplifier. For stages A - H in Scientific note A portable bio-amplifier for electric fish research: design and construction
Fig. 1. Circuit plan for bio-amplifier. For stages A - H refer to text. Boxes with dashed lines refer to components external to circuit board. Symbols follow universal engineering protocol.
A phenotyping weeds image dataset for open scientific research
<p>This in-house-built image dataset consists of 10810 weed images captured through a dedicated phenotyping activity in quasi-field conditions. The targets are seven of the most widespread and hard-to-control weeds in wheat (but also in other winter cereals) in the Mediterranean environment.</p> <p>In the framework of open scientific research, our aim is to share low-cost and high-resolution images representing challenging agricultural environments where weather, lighting and other factors can change by the hour and affect the quality of images. This way the dataset could be used to train Artificial Intelligence architectures designed for weed recognition, allowing the implementation of tools directly available in the field for farmers and technicians for effective and timely weed management.</p> <p>The dataset encompasses weed images ranging from the post-emergence phase (i.e. the complete cotyledons unfolding) until the pre-flowering stage. The weed selection was made by considering (i) bottom-up information and specific requests by farmers and technicians, (ii) weed susceptibility to commercial formulations for chemical control <50%, reported at least twice by field technicians, (iii) the difficulty of control considering any methods, and (iv) the type of growing season (overlapping or not with wheat). The final weeds selection encompassed both monocots (<em>Avena sterilis</em> and <em>Lolium multiflorum</em>) and dicots (<em>Convolvulus arvensis</em>, <em>Fumaria officinalis</em>, <em>Papaver rhoeas</em>, <em>Veronica persica</em> and <em>Vicia sativa</em>).</p> <p>Image acquisition was facilitated by using a white panel as a background; this helped to (i) spread the light and thereby make the plants well-illuminated, while still avoiding strong shadows when using the flash and (ii) simplify image processing. The images were acquired with a Canon EOS 700D hand-held camera set in the macro mode with aperture, shutter speed, ISO and flash in auto mode. Photo capture timing, target distances and light conditions did not have a fixed pattern but were deliberately programmed to vary in such a way as to mimic field conditions. For image shooting at various times of the day, the only precaution was to frame the subject with homogeneous light conditions (full sunlight/full shade). The varied outdoor conditions (light, distance, timing) and camera type (RGB) with auto mode were essential features to make the images photos look similar to those that a user can take in a field, for example with a smartphone camera.</p> <p>After selection and categorization, images were cropped to select the region of interest following the 1:1 ratio but maintaining a minimum size of 512 x 512 pixels.</p> <p> </p> <p>More details on the dataset and its use for weed recognition tasks will be soon available in the proceedings of the forthcoming ECPA conference (2-6 July 2023, Bologna, Italy).</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.