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3,225 results for “Case of study”
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 9. Proportion of indicators used in the case studies
<p>Figure 9 presents the indicators used for evaluating the effectiveness of mobile and ubiquitous learning practices. Learning achievements (64%) and perceived usefulness (56%) were the two most frequently used, followed by motivation (26%), ease of use (26%) and satisfaction (24%).<br> cognitive load (12%), system usage (8%), self-efficacy (6%) and social engagement (2%). Those indicators were usually adopted in the studies using qualitative methods for data collection. The results suggest a possible relationship between the data collection methods and the indicators. The choices of indicators represent, in principle, how the effectiveness of mobile and ubiquitous learning practices can be most appropriately evaluated and presented using particular study methods.</p>
Success in Mobile and Ubiquitous Learning: Indicators of Effectiveness-Figure 1. Geographical distribution of the case studies
<p>The 50 cases of mobile and ubiquitous learning practices covered 14 countries/regions, including China, Japan, Taiwan, Korea, Sri Lanka, Turkey, Spain, Greece, the Netherlands, Britain, Australia, New Zealand, South Africa and the USA. Figure 1 shows the geographical distribution of the cases. Among the 50 cases, 70% were conducted in Asia, 16% in Europe, 4% in Oceania, 4% in Africa, and 2% in North America. Therefore, the results of this study represent more of the situation in Asia.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 3. Screenshots of the new VC app
<p>The user will be able to stay logged in inside the app and to receive different types of notifications. The application will have the ability to be set up for working offline, while users have the possibility to change the application language, which can be useful for foreign or Erasmus students. Additionally, users will be able to customize the app based on some preferences available in a settings screen, check their grades, send direct messages to other students or professors and also save important documents under “My files” section. During the beta phase of development, a focus group with teachers will be organized in order to present the new apps and to understand what new features are needed to support the courses facilitation on the go.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figure 2. Screenshots of the Virtual Campus mobile application (d, e)
<p>The VC app was downloaded and used by 350 students, representing a percentage of 5% of the total number of the students enrolled on the platform courses. The application has features corresponding to the desktop version of the platform. In developing it, there were applied principles related to mobile learning usability and design, trying to provide enhanced possibilities for learning on-the-go and also specific notifications (Machun et al., 2012; Harrison et al., 2013; Mocofan, 2017). Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.</p>
A Critical Analysis of Mobile Applications for Learning. Study Case: Virtual Campus App-Figire 1. Screenshots of the Virtual Campus mobile application (a, b, c)
<p>Figure 1 presents a series of screenshots of the VC app. After signing in the app, a student can view and visit all the courses in which he or she is enrolled (Fig.1a), from where can manually download materials to study offline (Fig.1b). Also, the user can visit the discussions forums to see what is new (Fig.1e), query the calendar (Fig.1c), to display upcoming exams or homework deadlines (Fig.1d), and also look up for any homework on a specific screen.</p>
Raw data for the SciPipe machine learning in drug discovery case study
<p>Accompanying raw data for the machine learning in drug discoverycase studies for SciPipe [1] available at https://github.com/pharmbio/scipipe-demo </p> <p>[1] http://scipipe.org</p>
Ecosystem-Level Determinants of Sustained Activity in Open-Source Projects: A Case Study of the PyPI Ecosystem
<pre><em>Replication pack, FSE2018 submission #164: </em><em>------------------------------------------ </em></pre> <pre><strong>**</strong>Working title:<strong>** </strong>Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem <strong>**</strong>Note:<strong>** </strong>link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. <em>Content description </em><em>=================== </em> <strong>- **</strong>ghd-0.1.0.zip<strong>** </strong>- the code archive. This code produces the dataset files described below <strong>- **</strong>settings.py<strong>** </strong>- settings template for the code archive. <strong>- **</strong>dataset_minimal_Jan_2018.zip<strong>** </strong>- the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) <strong>- **</strong>dataset_full_Jan_2018.tgz<strong>** </strong>- full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. <strong>- **</strong>build_model.r, helpers.r<strong>** </strong>- R files to process the survival data (`survival_data.csv` in <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>**</strong>, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in <strong>**</strong>dataset_full_Jan_2018.tgz<strong>**</strong>) <strong>- **</strong>Interview protocol.pdf<strong>** </strong>- approximate protocol used for semistructured interviews. <strong>- </strong>LICENSE - text of GPL v3, under which this dataset is published <strong>- </strong>INSTALL.md - replication guide (~2 pages)</pre> <pre><em>Replication guide </em><em>================= </em> <em>Step 0 - prerequisites </em><em>---------------------- </em> <strong>- </strong>Unix-compatible OS (Linux or OS X) <strong>- </strong>Python interpreter (2.7 was used; Python 3 compatibility is highly likely) <strong>- </strong>R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): <strong>- </strong>up to 2Tb of disk space (see Step 2 detalization levels) <strong>- </strong>at least 16Gb of RAM (64 preferable) <strong>- </strong>few hours to few month of processing time <em>Step 1 - software </em><em>---------------- </em> <strong>- </strong>unpack <strong>**</strong>ghd-0.1.0.zip<strong>**</strong>, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. <strong>- </strong>copy `settings.py` into the extracted folder. Edit the file: <strong> * </strong>set `DATASET_PATH` to some newly created folder path <strong> * </strong>add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` <strong>- </strong>install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` <strong>- </strong>install libarchive and headers: `sudo apt-get install libarchive-dev` <strong>- </strong>(optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. <strong>- </strong>install Python libraries: `pip install --user -r requirements.txt` . <strong>- </strong>disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. <em>Step 2 - obtaining the dataset </em><em>----------------------------- </em> The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: <em>####Option 2.a, difficulty level: easiest </em> Just use the precomputed data. Step 1 is not necessary under this scenario. <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>** </strong><strong>- </strong>get `survival_data.csv`, go to the next step <em>####Option 2.b, difficulty level: easy </em> Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. <strong>- </strong>create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018<strong>** </strong>to the newly created folder <strong>- </strong>rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. <em>####Option 2.c, difficulty level: medium </em> Use pre-downloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. <strong>- </strong>Download <strong>**</strong>dataset_full_Jan_2018.tgz<strong>** </strong>(5.4Gb compressed, 34Gb unpacked). <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` <strong>- </strong>extract the content of this archive into `<DATASET_PATH>`. <strong>- </strong>clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. <em>####Option 2.d, difficulty level: hard </em> Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). <strong>- </strong>ensure the `<DATASET_PATH>` is empty <strong>- </strong>add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. <em>Step 3 - run the regression </em><em>--------------------------- </em> install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>
Supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation
<p>These are supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation</p> <p>The folowing files are available:</p> <ul> <li>Supplement 1. Map of Mayotte with the sampling sites and the rivers.</li> <li>Supplement 2. <em>rbcL</em> primers, reaction mixture, and conditions used for the PCR of the 312-bp <em>rbcL</em> fragment. The information provided is for a single reaction with a final volume of 25µL.</li> <li>Supplement 3. The 20 fastq files containing the demultiplexed DNA reads.</li> <li>Supplement 4. Number of sequence reads for each sample before and after the trimming procedure.</li> <li>Supplement 5. The 20 OTU lists, corresponding to the 20 SSTs, including the number of DNA reads within the 90 samples and their assigned taxonomy.</li> <li>Supplement 6. Sampling site description with sample codes, names of rivers, year, number of raw DNA reads and GPS coordinates.</li> <li>Supplement 7. Values and summary statistics for the environmental variables.</li> <li>Supplement 8. The script used in Mothur for the bioinformatic analysis from trimming to the used OTU lists.</li> </ul> <p> </p>
W2Share Case Study: Workflow Research Object (WRO)
<p>Case Study - Molecular Dynamics</p> <p>Our case study is based on a molecular dynamics simulation defined in the following article:</p> <p>Silveira, R.L. and Skaf, M. S. Molecular Dynamics Simulations of Family 7 Cellobiohydrolase Mutants Aimed at Reducing Product Inhibition. J. Phys. Chem. B 119, 9295-9303 (2015). DOI: <a href="https://doi.org/10.1021/jp509911m">https://doi.org/10.1021/jp509911m</a></p>
Raw data sets from Jones et al. 2018 QSR publication: A multi-proxy approach to understanding complex responses of saltlake catchments to climate variability and human pressure: A Late Quaternary case study from south-eastern, Spain
<p>Attached are the raw data sets containing the pollen data, DXR, Grain size and C14 ages from the recent publication: Jones et al. 2018 QSR publication: A multi-proxy approach to understanding complex responses of saltlake catchments to climate variability and human pressure: A Late Quaternary case study from south-eastern, Spain.</p> <p>Note that these data sets do contain hiatuses and a major age-reversal due to erosian which have likely been caused by increased seasonal wetness at the onset of the Holocene. A full explanation is provided in our 2018 publication. If you do wish to use the data, it is essential that you read the publication inorder to interpret the results correctly. We also require that when using this data that you correctly cite it (Bibliographic reference and the doi number of the data set). There were some problems uploading the XRF (geochemical) data sets, so I haven't included these yet, but hopefully will do eventually. </p> <p>Below I have also included the abstract from our publication, which provides an overview of the purpose of our work and a brief summary of the main findings.</p> <p>Abstract of Jones et al. 2018:</p> <p>The article focuses on a former salt lake in the upper Vinalopo Valley in south-eastern Spain. The study spans the Late Pleistocene through to the Late Holocene, although with particular focus on the period between 11 ka cal BP and 3000 ka cal BP (which spans the Mesolithic and part of the Bronze Age). High resolution multi-proxy analysis (including pollen, non pollen palynomorphs, grain size, X-ray fluorescence, and X-ray diffraction) was undertaken on the lake sediments. The results show strong sensitivity to<br> both long term and small changes in the evaporation/precipitation ratio, affecting the surrounding vegetation composition, lake-biota and sediment geochemistry. To summarise the key findings the main general trends identified include: 1) Hyper-saline conditions<br> and low lake levels at the end of the Late Glacial 2) Increasing wetness and temperatures which witnessed an expansion of mesophilic woodland taxa, lake infilling and the establishment of a more perennial lake system at the onset of the Holocene 3) An increase in solar insolation after 9 ka cal BP which saw the re-establishment of pine forests 4) A continued trend towards increasing dryness (climatic optimum) at 7 ka cal BP but with continued freshwater input 5) An increase in sclerophyllous open woody vegetation (anthropogenic?), and increasing wetness (climatic?) is represented in the lake record between 5.9 and 3 ka cal BP 6) The Holocene was also punctuated by several aridity pulses, the most prominent corresponding to the 8.2 ka cal BP event. These events, despite a paucity of well dated archaeological sites in the surrounding area, likely altered the carrying capacity of this area both regionally and locally, particularly during the Mesolithic-Neolithic transition, in terms of fresh water supply for human/animal consumption, wild plant food reserves and suitable land for crop growth.</p>
Supplementary Material: Comparing Formal Tools for System Design: a Case Study from the Railway Domain
<p>The package includes a set of models for a railway moving-block system:</p> <p>(a) a PDF document named Moving-block Model and Requirements.pdf, which includes a UML model of a moving-block system together with a set of requirements for the system;</p> <p>(b) a set of 10 folders, each one associated to a formal or semi-formal development tool. Each folder contains one or more model of the moving-block system from (a), developed by means of the tool.</p>
Data set supporting journal article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019
<p>This data set contains evapotranspiration data obtained by a conventional eddy covariance set-up and a low-cost eddy covariance set-up as described in the research article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019.</p> <p>The data set contains all necessary data needed to replicate figures and analysis presented in the research article. The data sets are sorted and named according to the figure the data were used for. </p>
PERCEIVE Education, Research Development and Innovation in the South-East Development Region A Case Study 20191006
<p>The data set include secondary data extracted from EUROSTAT database. The data set referring to the education and research infrastructure on Sud-Est NUTS II region from Romania for the period 2007-2013. </p>
PERCEIVE. WP4_ T4-5_Citizens vs Practitioners views of the EU Cohesion Policy in 9 PERCEIVE case-study regions
<p>This dataset contains data related to the papers: Tudor MM, Florian V, Rosu, SE, Kruzslicika M (2019) "Citizens’ vs practitioners’ perception on EU regional approach", published by Rural areas and Development & Tudor MM, Florian V (2019) "Percepția cetățenilor vs. percepția practicienilor cu privire la politica de coeziune a UE", published in proceedings of "Piețele Agricole și Spațiul Rural în Contextul Modernizării și Simplificării Politicii Agricole Comune". The dataset was built collecting data from different sources: the dataset “<em>PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Survey at citizen level and data relative to regional performance of the Cohesion Policy and institutional quality</em>” (<a href="https://doi.org/10.5281/zenodo.3346047">https://doi.org/10.5281/zenodo.3346047</a>); the dataset “<em>PERCEIVE. WP1. Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries. Task1.2. Focus group with Cohesion Policy practitioners</em>” (focus groups and other interviews) (<a href="http://amsacta.unibo.it/6215/">http://amsacta.unibo.it/6215/</a>).</p>
PERCEIVE. WP3_ T3-1_ Communication strategies effectiveness in the context of EU Cohesion Policy - Romanian case study – Sud Est NUTS II region
<p>This dataset contains data related to the papers: Tudor MM, Florian V, (2018) "Impactul și eficacitatea strategiilor de comunicare în contextul politicii de coeziune a Uniunii Europene. Studiu de caz - România", published in proceedings of "Economia agroalimentară și dezvoltarea rurală din perspectiva integrării europene". The dataset was built collecting data from the dataset “<em>PERCEIVE. WP1. Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries. Task1.2. Focus group with Cohesion Policy practitioners</em>” (focus groups and other interviews) (<a href="http://amsacta.unibo.it/6215/">http://amsacta.unibo.it/6215/</a>). Data used in the above-mentioned paper regarding the Section II from the Focus Group protocol and is related with Romanian PERCEIVE case-study region – Sud EST (RO22).</p>
РИС. 4. ROC-кривые и плоЩадь под ROC-кривой (AUC) для раЗных моделей. А. РеЗультаты для Xeropicta derbentina. B. РеЗультаты для Brephulopsis cylindrica. Красная кривая иллюстрирует покаЗатели модели для обучаюЩего набора, бледно-голубые линии — для тестовых наборов и отдельных моделей, толстая синяя линия — обЩие покаЗатели всех моделей. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 4. ROC-кривые и плоЩадь под ROC-кривой (AUC) для раЗных моделей. А. РеЗультаты для Xeropicta derbentina. B. РеЗультаты для Brephulopsis cylindrica. Красная кривая иллюстрирует покаЗатели модели для обучаюЩего набора, бледно-голубые линии — для тестовых наборов и отдельных моделей, толстая синяя линия — обЩие покаЗатели всех моделей.
FIG. 3 in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
FIG. 3. Principal component analysis (PCA). The dimensionality reduction technique represents multivariate data on the 2D plane thus showing their structure. Datapoint colors are the same as in Fig. 2.
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017]. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017].
Figure 4 in Abrupt boundaries between mountain meadows and forests separate ground-dwelling invertebrate communities: a case study from South Tyrol, Italy
Figure 4. Abundance-based accumulation curves for ground-dwelling macro-invertebrates from montane meadows and mixed forests in South Tyrol, Italy. The data come from pitfall traps along a linear transect from extensively managed hay meadows ('MM' and 'M', squares) across an abrupt ecotone ('E', diamonds) towards mixed forest stands ('F' and 'FF', circles). The curves show from left to right: the sampling coverage, taxa and species richness (Hill number q = 0), Shannon (q = 1) and Simpson diversity (q = 2). Plot [A] shows the total faunal community data at the highest taxonomic resolution, plot [B] the Araneae data at species level.
Figure 3 in Abrupt boundaries between mountain meadows and forests separate ground-dwelling invertebrate communities: a case study from South Tyrol, Italy
Figure 3. Boxplots with jitter of ground-dwelling macro-invertebrate activity densities from montane meadows and mixed forests in South Tyrol, Italy. Each data point represents a pitfall trap along a linear transect starting from extensively managed hay meadows ('MM' and 'M') across an abrupt ecotone ('E') towards mixed forest stands ('F' and 'FF'). Significant differences between the habitat plots (i.e. on the X-axes) according to GLM and Tukey's HSD post-hoc tests are indicated with different letters at top of each plot.
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.