Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,036
datasets available to search
ShareScore release 0.9.0
Dataset results
1,036 results for “Motivation”
Variant Forks -- Motivations and Impediments
<p>Social coding platforms centred around git provide explicit facilities to share code between projects: forks, pull requests, cherry-picking to name but a few. Variant forks are an interesting phenomenon in that respect, as it permits for different projects to peacefully co-exist, yet explicitly acknowledge the common ancestry. Several researchers analysed forking practices on open source platforms and observed that variant forks get created frequently. However, today little is known on the motivations for launching such a variant fork. Is it mainly technical (e.g., diverging features), governance (e.g., diverging interests), legal (e.g., diverging licences), or do other factors come into play? In this paper we report the results of an exploratory qualitative analysis on the motivations behind variants creation and maintenance. We surveyed 105 maintainers of different active open source variant projects hosted on GitHub. Our study extends previous findings, identifying a number of fine-grained common motivations for launching a variant fork and listing concrete impediments for maintaining the co-existing projects.</p>
Quantitative raw data for D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science"
<p>This dataset presents the quantitative raw data that was collected under the H2020 INCENTIVE project for the D1.3 - “Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”. The dataset includes the answers that were provided by almost 2,000 participants from 4 pilot European countries (Greece, Lithuania, Spain, and the Netherlands) regarding the general public's perceptions, attitudes, concerns, motivational factors and obstacles with regard to participation in Citizen Science activities. The original survey questionnaire was created and disseminated through the EUSurvey platform, and data collection took place from April to June 2021. For the statistical analysis of the data and the conclusions drawn from the analysis, you can access the D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”.</p> <p>Under INCENTIVE, four Citizen Science Hubs will be established and tested during the life-span of the project in the facilities of four Research Performing and Funding Organisations (RPFOs): University of Twente (the Netherlands), Autonomous University of Barcelona (Spain), Aristotle University of Thessaloniki (Greece) and Vilnius Gediminas Technical University (Lithuania). Essentially, the Hubs will aim to bring different stakeholders together and bridge society with science under the emerging paradigm of Citizen Science, in an institutionalised way.</p>
Data set for "Cortical sensory processing across motivational states during goal-directed behavior"
<p>Data set for: Matteucci G, Guyoton M, Mayrhofer JM, Auffret M, Foustoukos G, Petersen CCH, El-Boustani S, Cortical sensory processing across motivational states during goal-directed behavior (2022).</p> <p>Neuron https://doi.org/10.1016/j.neuron.2022.09.032</p> <p>There are 2 files in this upload:</p> <p>1. The file named "Matteucci2022.pdf" is the Open Access pdf file of the manuscript published in Neuron.</p> <p>2. The file named "Matteucci_data_code.zip" (~26.5 GB) is a zipped version of a folder "Matteucci_data_code" (~33 GB), which contains the data analysed in the study along with Matlab code used to generate all main figures of the paper. The analysis code is in a subfolder named "code". This subfolder in turn has three subfolders "analysis_scripts", “analysis_functions” (containing the original code for intermediate data processing) and “paper_figures_scripts” (containing the code for generating each figure panel from pre-processed data). The main script “reproduce_figures.m” will call the subscripts contained in the “paper_figures_scripts” folder to reproduce the plots contained in all main figures of the paper (and take care of adding the relevant code and data folders and subfolders to Matlab file path). The raw and pre-processed data analysed in the study can be found in the folder named "data". A “README.txt” file provides further details on the content of each subfolder.</p>
Supplementary material 1 from: Schmeller D, Henle K, Loyau A, Besnard A, Henry P (2012) Bird-monitoring in Europe – a first overview of practices, motivations and aims. Nature Conservation 2: 41-57. https://doi.org/10.3897/natureconservation.2.3644
The questionnaire was designed to assess how biodiversity monitoring schemes were carried out and what the motivation was to launch that scheme.
BRAIN Journal-Pros and Cons Gamification and Gaming in Classroom-Figure 1. Game-design elements and motives (Blohm, I. & Leimeister, J. M., 2013).
<p>Presenting gamification mechanics during classes by implementing them into grade system can be easily obtained by using eLearning environments hybridized with immersive interactive scenarios, like in Lifesaver- a learn by doing model to teach the basic steps in responding to a situation where a person suffers a heart attack or choking (Gamification in eLearning, 2017). The proper use of narrative layers can improve engagement of user and points can be gained using short assignments (missions). The students can choose the assignments as they like to obtain enough points to pass the classes. Obviously, for harder tasks they will get more points, but none of the tasks are obligatory. </p> <p>Other gamification elements include avatars, badges, levels, reputation level, tasks, etc. Details are presented in Figure 1. Making the rewards for accomplishing tasks visible to other players or providing leaderboards are ways of encouraging players to compete. </p>
Figure 5 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province
Figure 5. Challenges (n = 56) faced by individual volunteers in the management of invasive alien plant management in Western Cape, South Africa.
Figure 4 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province
Figure 4. Reasons for initial engagement (n = 71) in volunteering and the current motivations (n = 86) for volunteers to be involved in the management of invasive alien plant species in Western Cape, South Africa.
Figure 2 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province
Figure 2. Motivations (n = 35) for forming volunteer groups that remove alien invasive plants in Western Cape, South Africa.
Figure 1 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province
Figure 1. Identified volunteer groups (52) in Western Cape of South Africa. Groups that participated in the survey (26) are indicated by circles that also show group sizes (individual members per group). Groups that did not participate in the survey are indicated by blue circles. The green area on the map represents the fynbos biome.
Figure 3 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province
Figure 3. Challenges (n = 26) faced by volunteering by groups in the management of invasive alien plants in Western Cape, South Africa.
Extended data integration of motivational aspects in gamification and game-based learning educational designs
<p>This is the extended data for a systematic review article about the integration of motivational aspects in gamification and game-based learning educational designs related to teacher´s training and teacher´s professional development.</p>
Enhancing Motivation in Software Engineering Education through Gamified Agile Project-based Learning
<p>Project-based learning (PBL), e.g., student software development projects, is an essential part of today's Software Engineering (SE) education. They allow students to work on real-world projects and gain practical experience as a team. However, several challenges arise in such projects, including learning new technologies and dealing with communication and coordination issues within the team. These factors can lead to a lack of motivation to contribute to the project and a decrease in productivity, potentially resulting in an insufficient project outcome. This paper aims to promote student motivation in PBL and increase team productivity by applying gamification. We conducted a user and requirements analysis to identify the needs of students and supervisors of such projects. Based on the insights, we designed and implemented DinoDev, a gamified project management tool that combines project management features with gamification elements. The DinoDev concept was evaluated in a student project, indicating increased motivation and team productivity. The findings are valuable for advancing research on using gamification in PBL and for lecturers to improve their students' motivation and team productivity in SE education.</p>
Data sets - The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities
<p>Data sets </p> <p>The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities. <br>In the period from February to October 2024, a survey of 112 computer science teachers in Kazakhstan (Pavlodar region) was conducted to determine attitudes to inclusive education, motivation to teach, and perception of the possible impact of computer science on students with mental disabilities.</p> <p>Questionnaire <br>https://docs.google.com/document/d/1LzukKSqW_mHMZXbMtN0ecmmU4cKJiwgf0laTWBHQSng/edit?usp=sharing</p> <p><strong>This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP14872400).</strong></p>
Intrinsic motivation, perceived competence, negative feelings, and math academic performance.
<p>This dataset includes information about Primary School students' perceived competence, negative feelings, and intrinsic motivation with homework. The relationship between these variables and academic performance has been studied.</p>
Motivation and harvesting behaviour of fishers in a specialized fishery targeting a top predator species at risk
<p>Effective management of wildlife resources depends on understanding and cooperating with the human users of the resource, particularly as policies may be rejected if user satisfactions are not met. In Australia, recreational anglers can legally target a migratory top predator, the shortfin mako shark (<em>Isurus oxyrinchus</em>), that is also a species at risk. It is assumed that most of the sharks are released and population remains minimally impacted; yet, the actual release rate of this species is unknown and little information is available on the motivations and satisfactions of anglers that participate in this fishery. The rate of catch-and-release fishing was ascertained by a web survey of recreational shark anglers from three south-eastern Australian states. Respondents reported that ~70% of the captured makos were released, with significant geographic variation in release rates between states. Differences in harvesting behaviour between states could be attributed to the varying value assigned to shortfin mako as a sport fish and table fish among regions. Additionally, higher rates of release among anglers from New South Wales may be linked to increased opportunity for resource substitution (i.e. greater diversity of game fish species) and established norms driven by current catch-and-release fisheries in that region. Increased participation in catch-and-release fishing may be achieved by establishing behavioural norms by the provision of more desirable incentives to release sharks during fishing competitions. Data on regional variation in release rates yields important information for managers to target specialized fishers to incentivize catch-and-release fishing with an objective of changing behaviour. Information on natural resource user motivations and satisfactions, such as studied here, has the potential to guide management actions and the ways in which managers interact with resource users.</p>
[Dataset] Is it a match? Motivations on citizen science volunteers and recruitment arguments in project descriptions
<p>This dataset contains the necessary details to reproduce the experiments of the paper:</p> <p><em>Kai Nils, W., Gutiérrez Páez,N.F., Sabel, O. and Hämäläinen, R. (2022) “Is It a Match? Motivations on Citizen Science Volunteers and Recruitment Arguments in Project Descriptions.” In Proceedings of the ECSA2022 conference: Citizen Science for Planetary Health, 69–70. <a href="https://2022.ecsa-conference.eu/files/ecsa/Bilder/ECSA2022_Conference_Proceedings.pdf">https://2022.ecsa-conference.eu/files/ecsa/Bilder/ECSA2022_Conference_Proceedings.pdf</a></em></p> <p>Data has been collected by quantitative triangulation. 1076 participants in citizen science projects answered a survey about the 12 motivational factors for participating. They had access to the survey by social media posts or email invitations sent to people in charge of projects. Data regarding motivational arguments in recruitment come from quantitative content analysis of 367 project descriptions of the website Zooniverse. The content analysis of the project descriptions was done manually by two coders independently. Then, both coders analysed their codings and reached consensus.</p>
Spatio-temporal dynamics of the proton motive force on single bacteria - dataset
<p>Data set used in our manuscript "Spatio-temporal dynamics of the proton motive force on single bacteria" [<a href="https://www.biorxiv.org/content/10.1101/2023.04.03.535353v1">Biorxiv</a>].</p> <p> </p> <p>To produce fig1 and fig2, unzip file in bash:</p> <pre><code class="language-bash">$ 7z e data_fig1_fig2.7z</code></pre> <p>Open fig1 data in python:</p> <pre><code class="language-python">> b = pickle.load(open('fig1.p', 'rb')) > b {'speed_Hz': array([-34.01139986, 13.07744575, 79.66060694, ..., -4.79440373, -4.88539275, -0.1366687 ]), 'speed_Hz_f': array([-11.11722186, 15.74953016, 32.24685265, ..., 20.11726694, -3.54263861, -36.89432049]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 5000.0}</code></pre> <p>where</p> <p>b['speed_Hz'] : speed trace in Hz</p> <p>b['speed_Hz_f'] : speed trace in Hz, savgol filtered (5th order, 41 points)</p> <p>b['laser'] : laser trace in arbitrary units</p> <p>b['FramesPerSecond'] : camera frame acquisition rate</p> <p> </p> <p>Open fig2 data:</p> <pre><code class="language-python">> a = pickle.load(open('fig2.p','rb')) > a {11: {'speed_Hz': array([ 16.2828179 , 38.42508915, 94.68772452, ..., -12.24519659, 160.68335892, 114.39589274]), 'speed_Hz_f': array([ 25.8833788 , 31.87792607, 37.33062434, ..., 66.97264585, 91.38908923, 122.73732123]), 'laser': array([555950., 554818., 555193., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 12: {'speed_Hz': array([ 57.41541418, 24.4936895 , 248.68571544, ..., 86.08667522, -46.11733599, 18.16993041]), 'speed_Hz_f': array([102.04557049, 91.82376088, 84.51288552, ..., 33.63965888, 16.31075159, -5.36834171]), 'laser': array([555950., 554818., 555193., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 21: {'speed_Hz': array([ -57.50587524, 74.55546084, 65.87878605, ..., -197.26554361, 140.95754077, 52.72452236]), 'speed_Hz_f': array([-24.36465769, 22.9146126 , 49.48957346, ..., 47.67536653, 43.44702708, 36.80127664]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 22: {'speed_Hz': array([ 56.78413066, -147.32742392, -14.28783413, ..., -29.51455248, 15.66125098, 41.38001828]), 'speed_Hz_f': array([-23.57073686, -18.61154574, -15.85582681, ..., 7.05157621, 18.45809539, 37.52083946]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}} </code></pre> <p>where</p> <p>a[11] : dictionary for motor 1 trace, laser on motor 1, composed as above.</p> <p>a[12] : dictionary for motor 1 trace, laser on motor 2.</p> <p>a[21] : dictionary for motor 2 trace, laser on motor 1.</p> <p>a[22] : dictionary for motor 2 trace, laser on motor 2.</p>
Figure 3 in Absence of visual cues motivates desert ants to build their own landmarks
Figure 3. Ants build their own landmark by increasing their nest hill's height (A) Experimental design. Nest hills were first measured (height) and removed from 16 salt pan nests of which only eight were afterward provided with two artificial landmarks each. 3 days later, we re-measured rebuilt nest hills. (B) Photograph of the artificial landmarks added to a nest. (C) Percentage of the original nest rebuilt at nests with landmarks present or absent (unpaired t test, *p <0.05, bars indicate median ± SD).
Figure 2 in Absence of visual cues motivates desert ants to build their own landmarks
Figure 2. Absence of a nest hill impairs the navigation of salt pan ants (A–F) Trajectories of zero-vector ants of a salt pan nest (A–C) or shore nest (D–F) with nest hill present (left) or absent (right) after 5, 7.5, and 10 m displacement (colored circumferences indicate each displacement distance). Highlighted in bold is a trajectory exemplar of an ant for each displacement distance. (G–I) Straightness scores (i.e., run length divided by beeline) of homing ants of both nest types for the different displacement distances (Kruskal-Wallis test with Dunn's multiple comparisons test for selected pairs, **p <0.01; ***p <0.001, bars indicate median ± SD). (J–L) Success rates of homing ants of both nest types for the different displacement distances (Fisher's exact test, *p <0.05). Ants that during nest search left the tracking grid (radius, 20 m) around the nest entrance were classified as ''unsuccessful'' and are represented as crosses at the edges of the arenas.
Figure 1 in Absence of visual cues motivates desert ants to build their own landmarks
Figure 1. Accuracy of long homing runs and survey of nests in a salt pan (A) GPS tracks of long foraging runs (Geo Tracker, Google Earth) (see also Figure S1). Each black dot represents a different nest. Black lines, successful homing runs; red lines, unsuccessful homing runs that resulted in the ant's death. The track that leads to colony E is displaced to fit closer to the other paths for visualization purposes. (B) Photographs of a typical salt pan nest (left) and a typical shore nest (right, photo credit: Cornelia Buehlmann). (C) Map of the salt pan with dominant environmental features (i.e., vegetation and terrain elevations) and classifications of two nest types. Nests located at least 60 m away from any shoreline are classified as salt pan nests (blue) and nests less than 40 m away from any shoreline are classified as shore nests (green). Nests situated between 40 and 60 m from any shoreline are not included in the analysis. (D) Height of nest hills at salt pan nests and shore nests (unpaired t test, n = 33; ****p <0.0001; bars indicate median and SD). See also Table S1.
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.