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1,532 results for “skill”
Accelerating Digital Skills for Music Researchers - Processing Text-Based Corpora for Musical Discourse Analysis - Episode 2
<p>Dataset containing three subgenre-specific .xlsx files for the exercises in Episode 2 of the <a href="https://acceleratingdigitalskills.github.io/Processing-Text-Based-Corpora/">Processing Text-Based Corpora for Musical Discourse Analysis</a> lesson of the <a href="https://acceleratingdigitalskills.org/">Accelerating Digital Skills for Music Researchers</a> project. The original data was collected from <a href="https://boomkat.com/">Boomkat.com</a> with permission.</p>
SIRIUS Project - Comparative dataset on skills, qualifications and the employability of post-2014 migrants, refugees and asylum seekers
<p>The dataset provides harmonized country data on the skills and qualifications of post-2014 migrants, refugees and asylum seekers in all SIRIUS countries (Czech Republic, Denmark, Finland, Greece, Italy, Switzerland, United Kingdom). </p> <p>This dataset and the related codebook have been put together within the framework of Work package 1 of the SIRIUS Project "Skills and Integration of Migrants, Refugees and Asylum Applicants in European Labour markets". The work package, titled “Labour market barriers and enablers", aimed to determine (1) the position of post- 2014 migrants, refugees and asylum seekers in the labour market of their host country, (2) the main features of the host countries’ labour markets focusing on the sectoral structure and the relevant skills and occupations.</p> <p> </p> <p><strong>Acknowledgments and disclaimers</strong><br> This research was conducted under the Horizon 2020 project ‘SIRIUS’ (770515).<br> The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein.</p>
Transmission vectors of essential Tsimane knowledge and skills: dataset and code.
<p>In our research report <strong><em>Cultural transmission vectors of essential knowledge and skills among Tsimane forager-farmers</em></strong>, we examine reported patterns of culture transmission contributing to 92 essential skills among a sample of 421 Tsimane forager-horticulturalists. We collected data for the study using a <em>Skills Survey </em>to identify vectors and types of influence responsible for the transmission of 92 skills important among Tsimane (Schniter et al. 2015). Here we provide the anonymized data for Schniter et al.’s 2022 study (as both a .csv file and as a .sav file) as well as both code and outputs (as an .spv file) for statistical analyses performed using IBM SPSS Version 24.</p> <p>For additional details about the <em>Skills Survey </em>see</p> <p>Schniter, E., Gurven, M., Kaplan, H. S., Wilcox, N. T., & Hooper, P. L. (2015). Skill ontogeny among Tsimane forager‐horticulturalists. <em>American journal of physical anthropology</em>, <em>158</em>(1), 3-18.</p> <p>Attached:</p> <p>TransmissionData.csv</p> <p>TransmissionData.sav</p> <p>Regressions&Frenquencies.spv</p>
Interception of virtual throws reveals predictive skills based on the visual processing of throwing kinematics - Dataset
<p>Dataset consists of a list of Matlab structures, one for each of the 21 participants. For each participant all the recorded trials are reported ("trials" field). For each trial, the dataset reports information about the associated experimental condition and the kinematics of the ball and the racket trajectories. Information about the experimental condition are specified in the "info" field, which provides the experimental phase (Training and Experimental), the visibility (AllVisible, ThrowerOnly, BallOnly), the target (1, 2, 3, 4), and the thrower ID (1, 2, 3, 4). The kinematics data, starting from the time of ball release, are given in the field "trajectories", which provides the time vector (in seconds), and the corresponding 3D positions of the racket and the ball (in meters) in the reference frame shown in Figure 1 of the paper.</p>
Dataset for the Article entitled "Dissociable effects of practice variability on learning motor and timing skills"
<p>Dataset collected for studying the effect of the amount and the schedule of task variability on motor and timing learning.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 2. Two Possible Progress Scenarios for How to Reach Towards Machines and Systems with Human- Level Cognitive Skills
<p>Having identified the need for novel methods for machine recognition, situation assessment, and decision making in order to advance further in different automation domains, an important question is by what means can we reach such sophisticated mechanisms. The long-term goal in<br> mind is to construct machines and systems showing performances comparable to or even beyond human skill levels. In a guest talk at the Vienna University of Technology in 2008, Prof. Etienne Barnard, an expert in the field of Artificial Intelligence, made an interesting “conceptual suggestion” for two possible progress scenarios to reach this goal which could be summarized as depicted in Figure 2.</p>
Dataset: Skillful Craftsman Education Technology Limited (EDTK) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Gamification for Enhancing Soft Skills Development: a Systematic Mapping Study (supplementary material) - Temporary Repository
<p>This TEMPORARY repository contains supplementary material for the manuscript "Gamification for Enhancing Soft Skills Development: a Systematic Mapping Study":</p> <ul> <li>Gamification for Enhancing Soft Skills Development.CSV: Meta-data and raw data extracted from papers inlcuding in the mapping study</li> </ul>
RAGE pilot data from 1st evaluation of the Sports Team Manager game on soft skills for employability
<p><strong>General description: </strong>The dataset includes data from the first evaluation pilot which tested the Sports team manager game developed by PlayGen for the Okkam use case.</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human Computer Interaction (HCI) design and evaluation methods<br> PsycINFO Classification: 3620 Personnel Management & Selection & Training; 2228 Occupational & Employment Testing</p> <p><strong>Name entitites</strong><br> Organizational information: OKKAM, in collaboration with University of Trento<br> Geographical information: Italy<br> Time information: May-June 2017</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong> end users: other user groups</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Sports Team Manager game<br> Methodology/design: within subjects design<br> Evaluation variables: usability, user experience, learning</p> <p><strong>Instruments:</strong> 1. Questionnaire on Usability Game User Experience Satisfaction Scale (GUESS; Phan, Keebler, & Chaparro, 2016) – Usability subscale; 2. questionnaire on User Experience including 3 subscales: Enjoyment (GUESS -Enjoyment subscale); Usefulness (Intrinsic Motivation Questionnaire, IMI; Ryan, 1982) - Subscale Value/Usefulness; Flow (Flow Short Scale, FSS, Rheinberg et al., 2003; Vollmeyer & Rheinberg, 2006); 3. Pre-post questionnaire on learning; 4. Focus interview</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: cognitive skills: evaluating, analysing; affective skills: interpersonal skills<br> ESCO skills: social interaction (<a href="http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330">http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330)</a>; accept constructive criticism (<a href="http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30">http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30</a>); work in teams (<a href="http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0">http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0</a>); negotiate compromise <a href="http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74">(http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74)</a>; lead others (<a href="http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207">http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207</a>); motivate others <a href="http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507">(http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507</a>); support colleagues (<a href="http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224">http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224</a>); manage time <a href="http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8">(http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8</a>); make decisions (<a href="http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47">http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47</a>); develop strategies to solve problems (<a href="http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800">http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800</a>); evaluate information (<a href="http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9">http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9)</a><br> <br> <strong>Relationships</strong>: D8.3 First RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.2564742">10.5281/zenodo.2564742</a></p>
RAGE pilot data from 2nd evaluation of the Sports Team Manager game on soft skills for employability
<p><strong>General description: </strong>The dataset includes data from the second evaluation pilot which tested the Sports team manager game developed by PlayGen for the Okkam use case.</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human Computer Interaction (HCI) design and evaluation methods<br> PsycINFO Classification: 3620 Personnel Management & Selection & Training; 2228 Occupational & Employment Testing</p> <p><strong>Name entitites</strong><br> Organizational information: OKKAM, in collaboration with University of Trento<br> Geographical information: Italy<br> Time information: December 2017- November 2018</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong> end users: other user groups</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Sports Team Manager game<br> Methodology/design: within subjects design for learning<br> Evaluation variables: usability, user experience, learning</p> <p><strong>Instruments:</strong> 1. Questionnaire on Usability Game User Experience Satisfaction Scale (GUESS; Phan, Keebler, & Chaparro, 2016) – Usability subscale; 2. questionnaire on User Experience including 3 subscales: Enjoyment (GUESS -Enjoyment subscale); Usefulness (Intrinsic Motivation Questionnaire, IMI; Ryan, 1982) - Subscale Value/Usefulness; Flow (Flow Short Scale, FSS, Rheinberg et al., 2003; Vollmeyer & Rheinberg, 2006); 3. Pre-post questionnaire on learning; 4. Focus interview</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: cognitive skills: evaluating, analysing; affective skills: interpersonal skills<br> ESCO skills: social interaction: <a href="http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330">http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330</a>; accept constructive criticism: <a href="http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30">http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30</a>; work in teams: <a href="http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0">http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0</a>; negotiate compromise: <a href="http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74">http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74</a>; lead others: <a href="http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207">http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207</a>; motivate others: <a href="http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507">http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507</a>; support colleagues: <a href="http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224">http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224</a>; manage time: <a href="http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8">http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8</a>; make decisions: <a href="http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47">http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47</a>; develop strategies to solve problems: <a href="http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800">http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800</a>; evaluate information: <a href="http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9">http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9</a><br> <br> <strong>Relationships</strong>: D8.4 Second RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.1209206">10.5281/zenodo.1209206</a></p>
Acquiring musculoskeletal skills with curriculum-based reinforcement learning - model weights
<p><strong>Acquiring musculoskeletal skills with curriculum-based reinforcement learning, Neuron 2024</strong></p> <p>Here we provide the weights of the neural network policies used for the analysis presented in our article.</p> <p>The archives whose names start with a number (01 - 32) correspond to the 32 curriculum steps to train the Baoding Balls policy which ranked first at the MyoChallenge 2022. The code used for the training and which can be used to test the policies can be found at https://github.com/amathislab/myochallenge.</p> <p>The archives <em>hand_pose, hand_reach, pen </em>and <em>reorient</em> correspond to the other policies used in the article. They were developed in the paper <em>Latent exploration for reinforcement learning</em>, Chiappa et al., NeurIPS 2023. They can be loaded and tested with the code at https://github.com/amathislab/lattice.</p> <p>The archive <em>datasets</em> includes three subfolders: <em>rollouts, umap</em> and <em>csi</em>.</p> <ul> <li>The files in <em>rollouts </em>are the datasets of transitions resulting from the interaction between a policy and the environment. </li> <li>The files in <em>umap</em> are the pre-computed projections of specific subsets fo the datasets included in <em>rollouts</em> using UMAP.</li> <li>The files in <em>csi</em> report the performance of the policies described in our paper when applying Control Subspace Inactivation (CSI).</li> </ul> <p>These datasets are necessary to run the notebooks to reproduce the paper's figures and main results, with the code at https://github.com/amathislab/MyoChallengeAnalysis</p> <p>If you find these weights useful, please cite:</p> <div> <div>@article{chiappa2024acquiring,<br>title = {Acquiring musculoskeletal skills with curriculum-based reinforcement learning},<br>journal = {Neuron},<br>volume = {112},<br>number = {23},<br>pages = {3969-3983.e5},<br>year = {2024},<br>issn = {0896-6273},<br>doi = {https://doi.org/10.1016/j.neuron.2024.09.002},<br>url = {https://www.sciencedirect.com/science/article/pii/S0896627324006500},<br>author = {Alberto Silvio Chiappa and Pablo Tano and Nisheet Patel and Abigaïl Ingster and Alexandre Pouget and Alexander Mathis},<br>keywords = {motor control, motor learning, reinforcement learning, curriculum learning, motor skills, musculoskeletal control, muscle synergies},<br>}</div> <div> </div> <div>@article{chiappa2024latent,</div> <div>title={Latent exploration for reinforcement learning},</div> <div>author={Chiappa, Alberto Silvio and Marin Vargas, Alessandro and Huang, Ann and Mathis, Alexander},</div> <div>journal={Advances in Neural Information Processing Systems},</div> <div>volume={36},</div> <div>year={2024}</div> <div>}</div> </div>
HAND Skills demOnstrated by Multi-subjEcts (HANDSOME) Dataset
<p>The HANDSOME (HAND Skills demOnstrated by Multi-subjEcts) dataset is designed to provide reliable hands and objects motion data during human demonstrations of manual activities. This dataset was originally collected to study interactions between hands and objects in various contexts and automatically map the resulting task representations into robot plans. However, it can be utilized for any application requiring hands and objects detection from RGB video.<br><br>The setup involved an RGB camera (Intel RealSense D435i) positioned in a top-down (bird's eye) view, with the image plane aligned parallel to the working plane.<br>To enable robust detection of the 3D pose of objects and hands, we employed a marker-based detection system. ArUco markers were attached to the back of the hand and strategically positioned on the objects, preserving natural movements during manipulation.<br>We involved 10 participants, comprising 5 males and 5 females with an average age of 28.4 +/- 2.4 years. Among them, 8 were right-handed and 2 were left-handed. We asked subjects to perform both unimanual and bimanual activities, for a total of 400 recordings, in two different contexts (kitchen and workshop).</p> <p>The whole experimental procedure was carried out in accordance with the Declaration of Helsinki and the protocol was approved by the ethics committee azienda sanitaria locale (ASL) Genovese N.3 (Protocol IIT_HRII_ERGOLEAN 156/2020).</p>
The Collaborative Skills Scale
<p>The Collaborative Skills Scale is an 18-item scale with three subscales: Participation, Perspective taking and Social regulation. These subscales are devided under nine subscales overall, in the original version every subskill had four items, 36 items in sum. Participation consists of 12 items for Action, Interaction and Task completion, Perspective taking is composed by 8 items for Adaptive responsiveness and Audience awareness, Social regulation has 16 items for Negotiation, Self evaluation, Transactive memory and Responsibility initiative in the data file. The scale applies 7-point Likert-type questions where 1 means Does not describe me at all, 7 means Completely desribes me.</p>
Survey - digital skills and forests visit in Warsaw agglomeration
<p>The database contains the results of a survey conducted among a sample of over 1,400 people living in the Warsaw agglomeration. The study focused on determining the digital skills of residents and the use of digital tools when planning and visiting forest areas. It also examined which applications are used to share content about spending time in forests and how this content is shared.</p>
THE IMPACT OF PAEDIATRIC EMERGENCY MANAGEMENT SKILLS ON PATIENT OUTCOMES IN ZARIA
<p><span>Paediatric emergencies represent a critical challenge in Nigeria, where approximately 2,300 children under the age of five die daily, predominantly from preventable and treatable conditions such as pneumonia, malaria, and diarrheal diseases. Mortality rates are disproportionately higher in rural areas, where primary health care (PHC) centers often serve as the only point of access to medical services. The effectiveness of these centers is largely dependent on the emergency management skills of their healthcare workers. Despite their importance, there has been limited research on how the skills of PHC workers in managing paediatric emergencies impact patient outcomes.</span></p> <p><span>This study aimed to assess the proficiency of healthcare workers in primary healthcare centers in Zaria in managing common paediatric emergencies. A cross-sectional descriptive study was conducted among 139 health workers selected through a multi-stage sampling technique in Sabon Gari and Zaria Local Government Areas. Data collection was carried out using a pre-tested semi-structured self-administered questionnaire. The data were analyzed using SPSS version 21, and associations were tested using the chi-square test, with results presented in tables and charts.</span></p> <p><span>The findings revealed a significant deficiency in the emergency management skills of healthcare workers. A majority of respondents demonstrated inadequate skills in handling paediatric emergencies, with 66 (48.5%) exhibiting very poor competency in the management of common paediatric emergencies, and 32 (23.5%) displaying poor competency. Only a small fraction, 4 (2.9%) of respondents, demonstrated excellent skills in emergency management. Similarly, 46.7% of the respondents had very poor skills in the treatment of common paediatric emergencies, while only 3.0% exhibited excellent treatment skills. A statistically significant relationship was observed between the level of academic qualification and emergency management skills (p < 0.05).</span></p> <p><span>The results underscore the critical need to enhance the skill levels of healthcare workers in PHCs through continuous professional development. This could be achieved through collaborative efforts between the Department of Paediatrics at Ahmadu Bello University Teaching Hospital and the Primary Health Care Departments of Zaria and Sabon Gari local governments, focusing on regular training and workshops. Improving the skills of healthcare workers in managing paediatric emergencies is essential for reducing child mortality and improving health outcomes in the region.</span></p>
Digital Skills and Training Needs of 50+. A Study Beyond the Digital Divide
<p>Anonymized raw data set of a representative study on digital skills and training needs of people 50+ in Switzerland. The telephone interviews were conducted by the market research institute DemoSCOPE in July 2021.</p>
Database: Frameworks 21 century skills for development future skills in education 4.0: Systematic Literature Review
<p> Los datos revelan (a) la literatura de los frameworks destacan estudios de casos y estrategias de enseñanza aprendizaje vinculadas con las competencias del siglo XXI; (b) se ubican áreas de oportunidad para evidenciar publicaciones que aborden competencias de carácter y de metalearning; (c) los componentes de educación 4.0 que han sido abordados en los frameworks son: research strategies to apply knowledge and reflection strategies to encourage self system thinking; (d) las dimensiones de aprendizaje donde se han ubicado los frameworks son Skills and Knowledge y, (e ) destaca ausencia de frameworks dirigidos hacia profesores y a escuelas</p>
WRF output for the Geophysical Research Letters publication "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific"
<p>This dataset is the Weather Research and Forecasting (WRF) model output for the <em>Geophysical Research Letters</em> publication entitled "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific". The dataset includes the azimuthal-averaged parameters with 0.2°resolution, three-day forecast, and two experiments for all cases analyzed in the publication. Due to the data size, only the variables used in the figures are uploaded (i.e., relative humidity, relative vorticity, temperature, and water vapor mixing ratio). Detailed information, composite calculation, and model settings can be found in the publication.</p>
Dry Bench Skills for the Researcher
<p>The practice of biological inquiry has evolved so that a biological researcher's training must include both wet-lab and dry lab experimental and computational approaches. This course seeks to lay a broad foundation for the dry bench researcher. It is clear that "data literacy is a key skill in the modern world." This course will introduce the trainee to the computational techniques that enable data analysis locally on their laptop and as well as how to enable these same analysis techniques in the cloud.</p> <p>In this workshop, we will also introduce the learner to the concept of team science. The learner will obtain critical skills in effective collaboration that is imperative in a world where the amount of data available is beyond a single lab's walls.</p> <p>Over 10 hours of instruction, spread over two weeks, we seek to introduce the learner to RNAseq analysis and demystify concepts in RNAseq analysis. By the end of the course, the learner can successfully use tools involved in these analyses collaboratively and independently. This course aims to go broad and point the learner to learn more and take their knowledge deeper. Through their participation in the class, learners will acquire experience in a full suite of tools essential to any bioinformatics analysis. They will learn and understand FAIR best practices. They will obtain and use their GitHub, Docker, and ORCID ids. They will know where to find Nextflow workflows and run them to generate results that are further explored through interactive Jupyter Lab notebooks. They will learn about publicly available datasets downloadable from cloud resources and the ever-growing datasets that may aid their research. They will learn about using flexible, powerful, and community-based workflow languages such as Nextflow and how the resulting workflows use containerization to modularize and simplify bioinformatic analysis. Such workflows will be run using cloud-accessible data in the cloud and on their laptop. The course's final day will focus on pulling together all these resources into a single end-to-end example to empower the trainee to apply new knowledge towards their specific use case.</p>
The systematization of pre-professional practice and its articulation with research skills
<p>This article presents a study on the systematization of pre-professional practice and aims to outline a theoretical-methodological model based on the Oscar Jara proposal, contributions from Neuroscience and Critical-Complex Thinking; to publicize the results of the evaluation of the systematization processes, their level of efficiency, and their articulation with the research skills in 1,150 students from four universities and a pedagogical institute in Peru; and finally, to socialize the results of the application of the model in experimentation in 289 students of said population. The methodology used descriptive-correlative and experimental designs, with data collection through a virtual survey and an evaluation rubric. The results evidenced the precarious level of systematization and investigative capacity, the relationship between both variables, and the impact of the application of the model in an experimental group. Therefore, expanding spaces during pre-professional practice in institutions is recommended to generate an efficient systematization that guarantees the development of research skills in students and prepares them to carry out their research work.</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.