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208 results for “research and development”
Dataset for the research paper "How and Why Developers Migrate Python Tests from unittest to pytest"
<p>This is the dataset for the proposed paper "How and Why Developers Migrate Python Tests from unittest to pytest". <br> <br> <br> The `10_systems` zip file contains the aggregated and intermediate files for the systems used for precision and recall analysis.</p> <p>The `top_100_systems` zip file contains the aggregated and intermediate files for the top 100 python systems analyzes.</p> <p>The `__rq_reason` contains data to assess the advantages and disadvantages found in 100 issues or pull requests. The second column indicates whether issues/PRs were selected to be analyzed and the following columns indicate if the advantages (A) or disadvantages (D) are present or not.</p>
Research on Universities Profile upon Entrepreneurship and Innovation Orientation: Case of Developing Countries
<p>This Data set is a part of a research project of "Research on Universities Profile upon Entrepreneurship and Innovation Orientation: Case of Developing Countries" </p>
Dataset for IDRC Project: Exploring the opportunities and challenges of implementing open research strategies within development institutions. International Development Research Center.
<p>Data Package: Exploring the opportunities and challenges of implementing open research strategies within development institutions</p> <p>DOI for this package: https://doi.org/10.5281/zenodo.844394<br> Project Description: https://doi.org/10.3897/rio.2.e8880<br> Data Management Plan: https://doi.org/10.3897/rio.3.e14672<br> Other Related Documents and Reports: https://riojournal.com/collection/18/<br> Funder: International Development Research Centre/Centre de rechereches pour le développement international, https://doi.org/10.13039/501100000193</p> <p><br> Abstract<br> ========</p> <p>This is the Data Package for the project "Exploring the opportunities and challenges of implementing open research strategies within development institutions" the proposal for which was published as https://doi.org/10.3897/rio.2.e8880. The research project conducted open data pilot case studies with seven IDRC grantees to develop and implement open data management and sharing plans. The results of the case studies served to refine guidelines for the implementation of development research funders’ open research data policies.</p> <p>Contents<br> ========</p> <p>The Data Package contains all the public data generated by the project. The package was curated and metadata generated, including an HTML Catalog using the Calcyte Tool (https://codeine.research.uts.edu.au/eresearch/calcyte) developed at University of Technology Sydney.</p> <p>The project had two major phases:</p> <p>1. A review, based on desk work and interviews with data management experts<br> 2. Case studies, based on implementing open data practices within seven IDRC funded research projects</p> <p>Review<br> ------</p> <p>The review, published at https://riojournal.com/article/14673/ was supported by desk work and interviews. The materials related to the interviews can be found in the directory:</p> <p>* Policy and Implementation Review Interviews</p> <p>Case Studies<br> ------------</p> <p>Seven IDRC-funded projects were contributed to the pilot project.</p> <p>The materials generated by the case studies and used to support the final report (to be published with the collection at https://riojournal.com/collection/18/) are found in the following directories.</p> <p>* Introductory_Data_Workshop_Materials<br> * Introductory_Workshop_Presentations<br> * Data Management Planning<br> * SciDataCon Presentations<br> * Final_Project_Workshop_Materials<br> * Final_Project_Workshop_Presentations</p> <p>The files are encoded with a three letter code that identifies the relevant contributing project in each case. The contributing projects were:</p> <p>* Crowd Sourcing Data to fight Social Crimes: Harassmap, Egypt (HMP)<br> * The Brazilian Virtual Herbarium: CRIA, Brazil(BVH)<br> * Strengthening the Economic Committee of the National Assembly in Vietnam: Centre for Analysis and Forecasting, Vietnam (ECV)<br> * The Impact of Copyright User Rights: Derechos Digitales, Columbia (DED)<br> * Establishing a clearinghouse for tobacco economic data in Africa: DataFirst, South Africa (TED)<br> * Les problèmes négligés des systèmes de santé en Afrique : une incitation aux réformes: LASDEL, Niger (NDF)<br> * Indigenous Knowledge in Climate Change: Natural Justice, South Africa (IKC)</p> <p>More details will be found in the Case Studies and in the Final Report (forthcoming at https://riojournal.com/collection/18/)</p> <p>References<br> ==========</p> <p>* Neylon C, Chan L (2016) Exploring the opportunities and challenges of implementing open research strategies within development institutions. Research Ideas and Outcomes 2: e8880. https://doi.org/10.3897/rio.2.e8880<br> * Neylon C (2017) Data Management Plan: IDRC Data Sharing Pilot Project. Research Ideas and Outcomes 3: e14672. https://doi.org/10.3897/rio.3.e14672<br> * Neylon C, Chan L (2016-17) Exploring the opportunities and challenges of implementing open research strategies within development institutions: A project of the International Development Research Center, Research Ideas and Outcomes Collection, https://riojournal.com/collection/18/</p> <p> </p>
Replication Package: The Past, Present, and Future of Research on the Continuous Development of AI
<p>Replication package for the publication regarding the <strong>The Past, Present, and Future of Research on the Continuous Development of AI.</strong></p> <p> </p> <p> </p>
Figure S1 in Development of experimental mesocosms for cicada nymphs Graptopsaltria nigrofuscata: methodology and research recommendations
Figure S1. Mean daily soil temperature during the mesocosm experiment (16 April to 6 July 2021).
Digital publishing of Indic manuscripts and inscriptions using the READ Workbench corpus development, research and publishing framework
<p>Paper presented on Friday 11 June 2021 at the Digital Medievalist Global Symposium <em>The past, present, and future of Digital Medieval Studies</em> for the Asia & Oceania Panel, in the session Reading Indic and Japanese scripts.</p> <p> </p>
logo innovation, sustainability and business development research group
<p>Logo research group. Innovación, Sostenibilidad y Desarrollo Empresarial (ISDE)</p>
Dataset for the paper "Trends in studies developed in Europe about inclusion and diversity in schools: A systematic research projects review"
<p>Resources for the Systematic Research Projects Review (SRPR) about European research projects on school inclusion and diversity. The SRPR is related to the project "Gamified Values Education For Fostering Migrant Integration at Schools (GAMIGRATION)" funded by Erasmus+ programme of the European Union (ref. 2021-1-ES01-KA220-SCH-000032607).</p> <p>Search conducted on CORDIS and Erasmus+ platform.</p>
Data from: Novel methods to define invasive procedures at the end-of-life were developed to improve quality of end of life care research: A population-based cohort study in colorectal cancer
<p><strong>Background</strong></p> <p>Understanding the use of invasive procedures (IPs) at the end-of-life (EoL) is important to avoid under- and overtreatment, but epidemiologic analysis is hampered by limited methods to define treatment intent and EoL phase. This study applied novel methods to report IPs at the EoL using a colorectal cancer (CRC) case study.</p> <p><strong>Methods</strong></p> <p>An English population-based cohort of adult patients diagnosed between 2013 and 2015 was used with follow-up to 2018. Procedure intent (curative, non-curative, diagnostic) by cancer site and stage at diagnosis was classified by two surgeons independently. Joinpoint regression modelled weekly rates of IPs for 36 sub-cohorts of patients with incremental survival of 0-36 months. EoL phase was defined by a significant IP rate change before death. Zero-inflated Poisson regression explored associations between IP rates and clinical/sociodemographic variables.</p> <p><strong>Results</strong></p> <p>Of 87,731 patients included, 41,972 (48%) died. 9,492 procedures were classified by intent (interrater agreement 99.8%). Patients received 502,895 IPs (1.39 and 3.36 per person year for survivors and decedents). Joinpoint regression identified significant increases in IPs four weeks before death in those living 3-6 months, and eight weeks before death in those living 7–36 months from diagnosis. 7,908 (18.8%) patients underwent IPs at the EoL, with stoma formation the most common major procedure. Younger age, early-stage disease, men, lower comorbidity, those receiving chemotherapy and living longer from diagnosis were associated with IPs.</p> <p><strong>Conclusions</strong></p> <p>Methods to identify and classify IPs at the EoL were developed and tested within a CRC population. This approach can be now extended and validated to identify potential under- and overtreatment. </p>
Data from: Novel methods to define invasive procedures at the end-of-life were developed to improve quality of end of life care research: A population-based cohort study in colorectal cancer
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Role and practice of research software development at DLR
<p>The deposit contains the results of a survey concerning the role and practice of research software development at the German Aerospace Center (DLR). The survey started end of November 2018 and ended mid-February 2019. We received 773 answers which gave us interesting insights concerning developer demographics, tool usage, documentation, testing as well as software citation at DLR.</p>
Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals (SDGs)"
<p><strong>This package contains data on five text analysis types (term extraction, contract analysis, topic modeling, network mapping), based on the survey data where researchers selected research output that are related to the 17 Sustainable Development Goals (SDGs). This is used as input to improve the current SDG classification model v4.0 to v5.0</strong></p> <p><a href="https://sustainabledevelopment.un.org/sdgs">Sustainable Development Goals</a> are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">Aurora SDG dashboard</a> is the initial outcome as <em>proof of practice</em>.)</p> <p>The initiative started from the Aurora Universities Network in 2017, in the working group "<a href="https://aurora-network.global/activity/societal-impact-and-relevance-of-research-sirr/">Societal Impact and Relevance of Research</a>", to investigate and to make visible 1. what research is done that are relevant to topics or challenges that live in society (for the proof of practice this has been scoped down to the SDGs), and 2. what the effect or impact is of implementing those research outcomes to those societal challenges (this also have been scoped down to research output being cited in policy documents from national and local governments an NGO's).</p> <p><strong>Context of this dataset | classification model improvement workflow</strong></p> <p>The classification model we have used are 17 different search queries on the Scopus database.</p> <ul> <li>SDG search queries version 4.0 (SQv4) have been created, Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3817443"><em>Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v4.0</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817443</a></li> </ul> </li> <li>A survey has been distributed to senior researchers to test the robustness of SQv4. Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3798385"><em>Survey data of "Mapping Research output to the Sustainable Development Goals SDGs"</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3798385</a></li> </ul> </li> <li>This text analysis has been made as one of the inputs to improve the classification model. Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3832090"><em>Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals SDGs"</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3832090</a></li> </ul> </li> <li>Improved SDG search queries version 5.0 (SQv5) have been created, Published here: <ul> <li><a href="https://doi.org/10.5281/zenodo.3817445"><em>Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v5.0</em> by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817445</a></li> </ul> </li> </ul> <p><strong>Methods used to do the text analysis</strong></p> <ol> <li><strong>Term Extraction</strong>: after text normalisation (stemming, etc) we extracted 2 terms in bigrams and trigrams that co-occurred the most per document, in the title, abstract and keyword</li> <li><strong>Contrast analysis</strong>: the co-occurring terms in publications (title, abstract, keywords), of the papers that respondents have indicated relate to this SDG (y-axis: True), and that have been rejected (x-axis: False). In the top left you'll see term co-occurrences that a clearly relate to this SDG. The bottom-right are terms that are appear in papers that have been rejected for this SDG. The top-right terms appear frequently in both and cannot be used to discriminate between the two groups.</li> <li><strong>Network map</strong>: This diagram shows the cluster-network of terms co-occurring in the publications related to this SDG, selected by the respondents (accepted publications only).</li> <li><strong>Topic model</strong>: This diagram shows the topics, and the related terms that make up that topic. The number of topics is related to the number of of targets of this SDG.</li> <li><strong>Contingency matrix</strong>: This diagram shows the top 10 of co-occurring terms that correlate the most.</li> </ol> <p><strong>Software used to do the text analyses</strong></p> <p>CorTexT: The <a href="https://www.cortext.net/">CorTexT Platform</a> is the digital platform of LISIS Unit and a project launched and sustained by IFRIS and INRAE. This platform aims at empowering open research and studies in humanities about the dynamic of science, technology, innovation and knowledge production.</p> <p><strong>Resource with interactive visualisations</strong></p> <p>Based on the text analysis data we have created a website that puts all the SDG interactive diagrams together. For you to scrall through. <a href="https://sites.google.com/vu.nl/sdg-survey-analysis-results/">https://sites.google.com/vu.nl/sdg-survey-analysis-results/</a></p> <p><strong>Data set content</strong></p> <p>In the dataset root you'll find the following folders and files:</p> <ul> <li><strong>/sdg01-17/</strong> <ul> <li>This contains the text analysis for all the individual SDG surveys.</li> </ul> </li> <li><strong>/methods/</strong> <ul> <li>This contains the step-by-step explanations of the text analysis methods using Cortext.</li> </ul> </li> <li><strong>/images/</strong> <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li><strong>LICENSE.md</strong> <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li><strong>README.md</strong> <ul> <li>description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.</li> </ul> </li> </ul> <p>Inside an <strong>/sdg01-17/</strong>-folder you'll find the following:</p> <ul> <li>This contains the step-by-step explanations of the text analysis methods using Cortext.</li> <li><strong>/sdg01-17/sdg04-sdg-survey-selected-publications-combined.db</strong> <ul> <li>his contains the title, abstract, keywords, fo the publications in the survey, including the and accept or rejection status and the number of respondents</li> </ul> </li> <li><strong>/sdg01-17/sdg04-sdg-survey-selected-publications-combined-accepted-accepted-custom-filtered.db</strong> <ul> <li>same as above, but only the accepted papers</li> </ul> </li> <li><strong>/sdg01-17/extracted-terms-list-top1000.csv</strong> <ul> <li>the aggregated list of co-occuring terms (bigrams and trigrams) extracted per paper.</li> </ul> </li> <li><strong>/sdg01-17/contrast-analysis/</strong> <ul> <li>This contains the data and visualisation of the terms appearing in papers that have been accepted (true) and rejected (false) to be relating to this SDG.</li> </ul> </li> <li><strong>/sdg01-17/topic-modelling/</strong> <ul> <li>This contains the data and visualisation of the terms clustered in the same number of topics as there are 'targets' within that SDG.</li> </ul> </li> <li><strong>/sdg01-17/network-mapping/</strong> <ul> <li>This contains the data and visualisation of the terms clustered in co-occuring proximation of appearance in papers</li> </ul> </li> <li><strong>/sdg01-17/contingency-matrix/</strong> <ul> <li>This contains the data and visualisation of the top 10 terms co-occuring</li> </ul> </li> </ul> <p>note: the .csv files are actually tab-separated.</p> <p><strong>Contribute and improve the SDG Search Queries</strong></p> <p>We welcome you to join the Github community and to fork, branch, improve and make a pull request to add your improvements to the new version of the SDG queries. <strong><a href="https://github.com/Aurora-Network-Global/sdg-queries">https://github.com/Aurora-Network-Global/sdg-queries</a></strong></p>
OPEN-WINDOW: SOUND EVENT DATABASE FOR RESEARCH AND DEVELOPMENT
<p><strong>(1) Background:</strong></p> <p>Situated in the domain of urban sound scene classification by humans and machines, the research in this project will be a first step towards mapping urban noise pollution experienced indoors and finding ways to reduce its negative impact in peoples' homes. The acoustic distinction between outdoor and indoor scenes is an active research field and can be automated with some success. A much subtler difference is the change in the indoor soundscape induced by an open window. Being able to determine this, however, would allow applications in warning systems and be a prerequisite for an app-based urban sound mapping project.</p> <p>Acoustic detection requires neither line of sight nor sensors at the window frame or knowledge of the number of windows or their size. The task, however, varies substantially in difficulty with the amount of sound inside and outside. From the point of machine classification, the lack of specificity is the most problematic aspect: Very few sounds if any can be assumed to originate exclusively from outside <em>and</em> be present at all times to aid automatic detection. The required generalisation ability, however, can be assumed for humans, who might also use very subtle cues in the change of reverberations.</p> <p> </p> <p><strong>(2) Dataset</strong></p> <p><em>(a) Recording locations</em></p> <p>The recordings have been made at three different locations. </p> <ul> <li>Farm: A farm in Brook, Surrey, United Kingdom. The recordings were made in an open-plan studio flat area in the centre of the farm. The recordings in this location have the lowest levels of background noise, due mainly to a quiet environmental surrounding.</li> <li>Office 1: An office at the University of Surrey, Guildford, United Kingdom. The recordings were made in an open-plan office located on the first floor, at the Centre for Vision, Speech and Signal Processing (CVSSP). Since this office accommodates 16 researchers, recordings in this location have the highest level of background noise</li> <li>Office 2: An office at the University of Surrey, Guildford, United Kingdom. The recordings were made in a small size open-plan office at the CVSSP. This office accommodates 8 researchers and the recordings made in this office considered to have a medium level of background noise.</li> </ul> <p><em>(b) Recording equipment</em></p> <p>The recordings made at the two offices and a studio flat in a farm used a dedicated laptop, Focusrite Clarett 4pre USB external sound card (44,100 Hz sample rate at 16 bits per sample) 1, and a Behringer ECM 8000 microphone.</p> <p><em>(c) Recording setup</em></p> <p>The Behringer ECM 8000 microphone is connected to the External Line Return (XLR) input of the Focusrite Clarett external sound<br> card via an XLR cable. The external sound card is connected to the dedicated laptop and controlled using Ableton Live 10 software for setting configurations and exporting the recorded audio files. The microphone is located approximately 10 cm away from the<br> window and fixed using a microphone holder. At each location 90 audio sessions are recorded; 60 one minute recordings for static state setup and 30 fifteen seconds recordings for transitional state setup.</p> <p><em>(d) File naming conventions</em></p> <p>The naming convention for audio recording is as follows:<br> [Location] [State] [Time] [IDX]<br> [State] will be one of the following: “O stands for open, C stands for Close, OC means a transition from Open to Close and CO stands for a transition from Close to Open.” [Time] stamp will be one of the following: “AM stands for morning between 9:00 to 12:00, N stands for noon which is between 13:00 to 15:00 and PM which stands for an afternoon which is between 17:00 to 20:00.” [IDX] is<br> representing the file ID number. For example, “Farm C PM 01.wav”, means this file is recorded at the farm and in the afternoon when the window is closed and the file ID is 01.</p> <p><em>(e) Dataset acquisition:</em></p> <p>A recording kit consisting of a dedicated laptop and microphone will be given to volunteers. Custom-programmed software will remind the user to specify the window state (establishing the so-called ground truth).</p> <p><em>(f) Specifications</em><br> - Open-Window contains 270 audio recordings totalling 3.37 hours of audio.<br> - Each audio recording belongs to one of the four classes representing the window states; two stationary states (Open, Close) and two transitional states (Open-Close, Close-Open).<br> - The recordings were carried out in different locations and at different times of the day.<br> - Three locations: Office1, Office2, Farm<br> - Three periods of the day: Morning, Afternoon, Evening<br> - The recordings are split into six-folds.<br> - Fold 1 is the test set.<br> - Fold 2 is the validation set.<br> - Folds 3-6 comprise the training set.<br> Each fold is balanced in terms of the class and location distribution.<br> - The annotations/metadata can be found in annotations.csv.<br> - The recordings for the stationary states are approximately 60 seconds, while the recordings for the transitional states are approximate 15 seconds.<br> - The format of the recordings is 2-channel 16-bit PCM sampled at 44.1 kHz.</p>
Data from: A social-ecological database to advance research on infrastructure development impacts in the Brazilian Amazon
Recognized as one of the world's most vital natural and cultural resources, the Amazon faces a wide variety of threats from natural resource and infrastructure development. Within this context, rigorous scientific study of the region's complex social-ecological system is critical to inform and direct decision-making toward more sustainable environmental and social outcomes. Given the Amazon's tightly linked social and ecological components and the scope of potential development impacts, effective study of this system requires an easily accessible resource that provides a broad and reliable data baseline. This paper brings together multiple datasets from diverse disciplines (including human health, socio-economics, environment, hydrology, and energy) to provide investigators with a variety of baseline data to explore the multiple long-term effects of infrastructure development in the Brazilian Amazon.
Products developed through the "What About Model Data?, Determining Best Practices for Preservation and Replicability, EarthCube Research Coordination Network" project
This dataset includes products developed through the "What About Model Data? Determining Best Practices for Preservation and Replicability, EarthCube Research Coordination Network (RCN)" project. Products include: 1) a rubric worksheet to assist researchers in deciding what simulation output needs to be preserved in a trusted, community repository to communicate knowledge and satisfy publisher and funder requirements, 2) instructions on how to use the rubric worksheet, which include reference use cases, and 3) outputs and presentations from the three project workshops.
ICITS'24 - A Systematic Mapping Study on the Use and Development of Research Software
<p>Artifacts used for data collection and analysis of the article accepted for publication in ICITS'24.</p><p>Mourão, E., Trevisan, D., Viterbo, J. and Pantoja, C.E. (2024). A Systematic Mapping Study on the Use and Development of Research Software. In: ICITS'24 - 7th International Conference on Information Technology & Systems. Lecture Notes in Networks and Systems. Springer, Cham.</p>
Application of Artificial Intelligence in the Development of Smart Cities: An Analysis of Trends and the Research Agenda
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Supplementary material 2 from: Srygley RB, Senior LB (2024) Illustrated review of Mormon cricket Anabrus simplex (Tettigoniidae, Tettigoniinae) embryonic development. Journal of Orthoptera Research 33(1): 87-93. https://doi.org/10.3897/jor.33.98763
Supplementary material 2 from: Srygley RB, Senior LB (2024) Illustrated review of Mormon cricket Anabrus simplex (Tettigoniidae, Tettigoniinae) embryonic development. Journal of Orthoptera Research 33(1): 87-93. https://doi.org/10.3897/jor.33.98763
Supplementary material 1 from: Srygley RB, Senior LB (2024) Illustrated review of Mormon cricket Anabrus simplex (Tettigoniidae, Tettigoniinae) embryonic development. Journal of Orthoptera Research 33(1): 87-93. https://doi.org/10.3897/jor.33.98763
Supplementary material 1 from: Srygley RB, Senior LB (2024) Illustrated review of Mormon cricket Anabrus simplex (Tettigoniidae, Tettigoniinae) embryonic development. Journal of Orthoptera Research 33(1): 87-93. https://doi.org/10.3897/jor.33.98763
Research trends on sustainable development in smart cities
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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.