Find research datasets worth reusing

BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 1. Frequencies of strengths and weaknesses through a survey of literature from 2000 to 2012

We identified 192 specific studies which analyze, directly or indirectly, the subject of the strengths and weaknesses of e-learning educational services, respectively those containing the idea of some of their strengths and weaknesses ambivalence. The frequencies of strengths and weaknesses reported to intervals corresponding to the years when they were published are shown in Figure1.

BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 3.Frequency of principal week indices related to the 101 'week articles'

Reduced social interaction is the most important for weaknesses, representing 9.4% of the total of studied bibliography and an occurrence frequency of 5.5% (Figure3). A significant frequency difference of the weaknesses indices compared to the strengths indices was observed. If the top 5 strengths have frequencies ranging between 13.6% and 7%, none of the weaknesses has an occurrence frequency over 6%, all barely ranging between 5.5% and 3.4%, relative to the middle of strengths frequency range. For the practice of e-learning educational services, this may show either a still insufficient detection or theoretical analysis of weaknesses, or, indeed, the superiority of these services.

BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 5. Frequency of common indices related to 102 'common articles'

An additional and very interesting perspective is provided by the analysis of common indices (ambivalent), based on the 102 works-common articles type, and their frequencies (Figure 5).  The data reading indicates 13 ambivalent indices as percentages in descending order: 1. flexibility, 19%; 2. interactivity, 15.4%; 3. cost, 13%; 4. accessibility, 12%; 5. time, 7%; 6. anxiety/reduce social impact, 7%; 7. usability, 6%; 8. connection, 4%; 9. develop skills, 4%; 10. responsibility, 4%; 11. quality, 4%; 12. diversity, 3%; 13. delivery, 1% (Figure 5).

BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 4. Frequency of strong/weak indices related to the 102 'common articles'

Based on the data presented in Figure 2 and Figure 3 and carrying out a comparative and cumulative analysis to identify ambivalent strengths and weaknesses, the 102 ambivalent strong/weak indices related to the 'common articles' in Figure 4 were highlighted.  Their analysis shows that: 1. There are at least 13 ambivalent indicators identified in the studied literature (Table 1); 2. The strengths weight is 62% while the weight of weaknesses is 38.9%, resulting in a pretty big difference in favor of underlining and supporting the strengths, 23.1% more than in favor of weaknesses. These data indicate a significantly higher perception and approach in favor of appreciating the strengths of e-learning educational services, even in the case of their ambivalence. 3. In this context, the data illustrate the following three cases: 3.1. a huge gap between the perception and the interpretation of an index as strength and as weakness (e.g., flexibility is regarded 7.5 times more a strength rather than a weakness). It can be seen that this category of indices definitely belongs to strengths, acknowledged and validated by a large number of studies. In relation to these, efforts will be made for the development, improvement, elevation and obtaining superior parameters. 3.2. a relative correspondence between the perception and the interpretation of an index as strength and as weakness (e.g., the time required to design and implement educational services is considered a strength at a rate of 3.2% and a weakness at a rate of 3.3%). It results that this category of indices has to be studied thoroughly and watched in experimental studies, to replace the uncertainty area in their analysis, to determine which their area of predominance is, to what extent their identified limits and shortcomings have been reduced to allow their conversion into strengths or not; 3.3. a very large gap between the perception and interpretation of an index as a weakness and as a strength (e.g., lack of instructional delivery is considered as being a weakness 20 times more than a strength).This shows that this category of indices comes into focus as weaknesses which need to be analyzed, studied and experimented in order to reduce their negative impact.

opencc-by-4.0Mar 2018View details →

Dataset for: Developing research data management services and support for researchers: a mixed methods study

Overview This dataset contains the raw data for the manuscript:  Perrier L, Barnes L. Developing research data management services and support for researchers: a mixed methods study. Partnership. 2018;13(1). doi: doi.org/10.21083/partnership.v13i1.4115. Full-text available at: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202</a> Data and Documentation Files Five files make up the dataset:  <ol> <li>Coding Scheme: RDMServicesSupport_Codes.txt</li> <li>Transcript, Focus Group 01 (anonymized): RDMServicesSupport_FocusGroup01.pdf</li> <li>Transcript, Focus Group 02 (anonymized): RDMServicesSupport_FocusGroup02.pdf</li> <li>Transcript, Focus Group 03 (anonymized): RDMServicesSupport_FocusGroup03.pdf</li> <li>Transcript, Focus Group 04 (anonymized): RDMServicesSupport_FocusGroup04.pdf</li> </ol> Contact: Laure Perrier: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">orcid.org/0000-0001-9941-7129</a>

opencc-by-4.0Jan 2018View details →

A survey of current practice of data search services

Relevancy ranking is an important component of making a data repository's search system responsive to data seekers’ needs. The <a href="https://www.rd-alliance.org/groups/data-discovery-paradigms-ig">Research Data Alliance (RDA) Data Discovery Paradigms Interest Group</a> is a collaborative activity within our data community which aims to improve data searchability. This survey is intended to gather information about the current practices and lessons learnt by data repositories in implementing relevancy ranking in search systems. We expect that analysis of the survey results will: <ul> <li>Help data repositories choose appropriate technologies when implementing or improving their search functionality;</li> <li>Provide a means for sharing experiences in improving relevancy ranking;</li> <li>Capture the aspirations, successes and challenges encountered from research data repository managers;</li> <li>Help the Data Discovery Paradigms Interest group align future activities on data search improvement with the interests of data search service providers.</li> </ul> For the above the purpose, we designed a survey instrument to answer the following topics (the numbers in brackets indicate the number of questions asked per topic): <ol> <li>What are characteristics of each repositories (5)?</li> <li>What are system configurations (e.g., ranking model, index methods, query methods) (7)?</li> <li>Evaluation methods and benchmark (10) <ul> <li>What has been evaluated?</li> <li>What evaluation methods have been applied?</li> <li>How was the evaluation collection built?</li> <li>What is approximate  performance range of search systems with certain configuration?</li> </ul> </li> <li>What methods have been used to boost searchability to web search engines (e.g., Google, Bing) (2)</li> <li>What other technologies or system configurations have been employed (5)?</li> <li>Wish list for future activities for the RDA relevance task force (2)?</li> </ol> Notes: Survey instruments from Version 1 and Version 2 have same questions, but order questions slightly different.  Version 2 has the one as instrumented to participants.

opencc-by-4.0May 2018View details →

Accompanying dataset for: A Monte Carlo Method for Metamorphic Testing of Machine Translation Services

This is the original dataset, where white spaces are used to separate words of all languages. This is however not the best method of analyzing some Asian languages. Please refer to the following new version for enhanced, character-based analysis results: Zhi Quan Zhou. (2018). Accompanying dataset for: A Monte Carlo Method for Metamorphic Testing of Machine Translation Services (Version 2.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1230139

Stable Modeling on Resource Usage Parameters of MapReduce Application-Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary

In Figure 5, the positive dependency of different strength between each resource usage parameter and the corresponding previous usage parameter is exhibited for all MapReduce applications. It indicates that all current resource usage parameters are positively dependent on the previous values to some extent degree. Except for these common dependencies, there exist some special dependencies for different applications. On the top-left panel of Figure 5, CPU usage of Pi application shows the strongest positive dependency to lagged CPU usage, the Teragen application had the weakest positive dependency, and others exhibit the moderate positive dependency.

opencc-by-4.0May 2018View details →

Survey and Interview Data from Mixed-Method Survey of Serverless Computing and Function-as-a-Service Software Development in Industrial Practice

This dataset contains the almost-raw data resulting from two out of the three methods chosen by the researchers for their namesake study «A Mixed-Method Empirical Study of Function-as-a-Service Software Development in Industrial Practice».  Among the files are web survey questions, anonymised survey results, and interview guidelines. We encourage other researchers to perform open coding and other analysis techniques on the data to verify our claims and to generate new insights.

opencc-by-sa-4.0Jul 2016View details →

Data Set for Article "Verification-Aided Debugging: An Interactive Web-Service for Exploring Error Witnesses", Proc. CAV'16

This is the description of the supplementary archive of example interactive reports for the approach described in the article "Verification-Aided Debugging: An Interactive Web-Service for Exploring Error Witnesses", Proc. CAV'16. This archive contains a static snapshot of our system that allows the reader to a) experience the features of our web-service without relying on its online availability and b) reproduce the bug reports displayed in this static snapshot by validating the provided witnesses against the source code and the corresponding specifications using CPAchecker. The witness database is available at:   static/index.html The supplied verification tasks can be found at:   static/programs/ The supplied error witnesses are grouped by their corresponding verification tasks and can be found at:   static/witnesses/ The software verifier CPAchecker is placed at:   CPAchecker/ To browse the witness database and explore the supplied error reports, we recommend using the Firefox web browser, because not all features of our bug reports are guaranteed to be available in other browsers. Like the supplementary archive originally provided to the reviewers, this witness database contains only a small selection of the witnesses harvested from the "Competition on Software Verification 2016", because we do not want to burden the reader with an enormous amount of data that likely is not relevant for understanding the concepts. Also, error witnesses produced by some competition candidates that were not even syntactically correct were removed, because they do not add any value to the evaluation. However, the full data is still available online via our web service, for example, the list of witnesses for a verification task can be requested by computing the SHA-1 hash of the verification task's source code and submitting the following query:   http://vcloud.sosy-lab.org/webclient/master/witness?inputFile=<program-hash> The resulting JSON data contains all hashes of witnesses stored for the given program. A witness stored in the database can be requested via its SHA-1 hash by submitting the following query:   https://vcloud.sosy-lab.org/webclient/files/<hash> All verification tasks are available at the SV-COMP repository:   https://github.com/dbeyer/sv-benchmarks If you use verification tasks from the repository and are interested in validating witnesses produced for SV-COMP '16, please use the 'svcomp16' tag, because the tasks and their hashes might have changed since then. You can use CPAchecker to validate a witness for a verification task and generate an error report. First, navigate to the CPAchecker directory:   cd CPAchecker/ Now, perform the validation by providing the verification task (consisting of specification and program source code) and a witness:   scripts/cpa.sh -generateReport -witness-validation \     -spec <specification> \     <source-code> \     -spec <witness> For example:  scripts/cpa.sh -generateReport -witness-validation \     -spec ../static/programs/loop-acceleration/ALL.prp \     ../static/programs/loop-acceleration/array_false-unreach-call3.i \     -spec ../static/witnesses/loop-acceleration/array_false-unreach-call3.i/a4572a0c1b505b1d1170b7347e48a2a93cb3f4c1 The report will be generated in the subdirectory   output/report/

opencc-by-4.0Jan 2019View details →

Jamendo content analysed with the Audio Commons Analysis Service

This dataset contains partial output of running the final release of the <a href="https://github.com/AudioCommons/faas-ac-analysis">Audio Commons Analysis Service</a> on 99960 music pieces of the <a href="https://licensing.jamendo.com">Jamendo Licensing</a> catalogue. The service is described in <a href="https://www.audiocommons.org/assets/files/AC-WP4-QMUL-D4.13%20Release%20of%20tool%20for%20the%20automatic%20semantic%20description%20of%20music%20pieces.pdf">Deliverable D4.13</a> of the Audio Commons project. The analysis results are comprised of chord output including confidence for all pieces (using the software described in the deliverable) and of the output of the Essentia music extractor (v2.1_beta4-447-gc5ea2738) for 77190 of the pieces (of which tempo, beats, tuning and global-key are exposed through the API of the analysis service). The dataset is formatted as a single JSON file containing an array of documents. Each document in the array has two or three top-level keys. The first key is "_id" with values of the form "jamendo-tracks:<jamendo-id>". This id can be used to request metadata and audio through the <a href="https://developer.jamendo.com">Jamendo API</a>. The second key of the document is "chords", containing a subdocument with the output of the chord extraction algorithm. The third key is "essentia-music", which contains a subdocument with the output of the Essentia music extractor. <div> </div>

opencc-by-4.0Feb 2019View details →

Digital Scholarship services and supports - an overview from Irish Research and National Libraries - Data with Comments

This dataset contains survey data with comments (cleaned, direct references to institutions removed) and broken down by survey question sections. Some of the data has been converted to counts where it was used to generate the charts. The data is the output of a 2018 CONUL (Ireland’s consortium of research and national libraries) survey focusing on Digital Scholarship services and supports from a Irish Research and National Library perspective.

opencc-by-sa-4.0Sep 2018View details →

MAJA look-up tables for Sentinel-2 A&B sensors, for Copernicus Atmosphere Monitoring Service aerosol types

The archive contains the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors. These look-up tables correspond to the aerosol types used by Copernicus Atmosphere Monitoring Service (CAMS). However, the default continental model is also provided. Version 1.1 has new LUT for water vapour estimates, which corrects for a bias observed for large water vapour contents (above 2.5 g/cm2) Version 1.2 just changed the Folder name for a better integration with Start_maja. Version 1.3 added the Header files

opencc-by-sa-4.0Apr 2019View details →

National Weather Service Coded Surface Bulletins, 2003- (JSON format)

This dataset contains the Coded Surface Bulletin dataset reformatted as JSON files. The Coded Surface Bulletin dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003. Each JSON file contains one top-level object corresponding to one bulletin. The top-level object is composed of name/value pairs with the names bulletinType, createDate, validDate, Highs, Lows, ColdFronts, WarmFronts, OccludedFronts, StationaryFronts, and Troughs. The name/value pairs for bulletinType, createDate, and validDate are always present. The other name/value pairs are only present if there is corresponding data. The value for bulletinType is either "LR" or "HR", for low-resolution or high-resolution, respectively. The values for createDate and validDate are UTC timestamp strings. If the bulletinType value is "LR", the longitudes and latitudes have 1° precision. If the bulletinType value is "HR", the longitudes and latitudes have 0.1° precision. The value associated with the name High in the top-level object is itself an object composed of three name value pairs that describe the geographic locations and surface air pressure levels for one or more high pressure centers. The names of the object elements are lats, lons, and pressures. The values for these are all arrays. For a given object, the arrays will all have the same size. The arrays contain latitudes in degrees, longitudes in degrees, and pressures in millibars. If the arrays contain N elements apiece, the object is describing N pressure centers. The object associated with the name Low in the top-level object is structured in the same way. It describes the geographic locations and surface air pressure levels for one or more low pressure centers. The ColdFronts, WarmFronts, StationaryFronts, OccludedFronts, and Troughs names in the top-level object, when present, have values that are arrays. In each case, the array is composed of one or more objects. Each object represents a front or trough of the given type. Each object is composed of three name/value pairs with the names lats, lons, and strength. The value for the name strength is a string that is one of "weak", "moderate", "strong", or "unstated". The values associated with the names lats and lons are arrays. This pair of arrays represent the vertices of a polyline describing the location of a frontal boundary or trough. The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the <a href="https://mesonet.agron.iastate.edu/">Iowa Environmental Mesonet</a>.

opencc-by-sa-4.0Apr 2019View details →

National Weather Service Coded Surface Bulletins, 2003- (netCDF format)

This dataset contains the Coded Surface Bulletin (CSB) dataset reformatted as <a href="https://www.unidata.ucar.edu/software/netcdf/docs/">netCDF-4</a> files. The CSB dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003. Each netCDF file contains one year of CSB fronts data represented as spatial map data grids. The times and geospatial locations for the data grid cells are also included. The front data is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded. The front polylines from the CSB dataset are rasterized into the appropriate data grids. Each file conforms to the <a href="http://cfconventions.org/">Climate and Forecast Metadata Conventions</a>. There are two large groupings of the CSB netCDF files. One group uses a data grid based on the <a href="https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr">North American Regional Reanalysis</a> (NARR) <a href="https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID221">grid</a>, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side. The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid. There are a number of files within each group. The files all follow the naming convention codsus_[masked]_<grid>_<subproduct>.nc, where [masked] indicates that the presence of the word masked is optional and <grid> is either merra2-1deg or narr-96km. The <subproduct> element is either the word mask or the sequence <n>wide_<year>, where <n> is the front width and <year> is the year for the data stored in the file. The codsus_<grid>_mask.nc file is a file containing a single data grid that delineates the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year. The WPC meteorologists don't attempt to provide equal levels of attention to every grid cell displayed on their workstations. The files of the form codsus_masked_<grid>_<n>wide_<year>.nc have all had the mask described above applied to exclude parts of fronts that extend past the envelope. The files of the form codsus_<grid>_<n>wide_<year>.nc have no masking applied. The <n>wide portion of the file names takes two forms—1wide and 3wide. The fronts in the1wide files were rasterized by drawing the front polylines with a width of one grid cell. The fronts in the 3wide files were rasterized by drawing the front polylines with a width of 3 grid cells. Within each grid group, there are five subsets of files: <ul> <li>codsus_masked_<grid>_1wide_<year>.nc</li> <li>codsus_masked_<grid>_3wide_<year>.nc</li> <li>codsus_<grid>_1wide_<year>.nc</li> <li>codsus_<grid>_3wide_<year>.nc</li> <li>codsus_<grid>_mask.nc</li> </ul> The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the <a href="https://mesonet.agron.iastate.edu/">Iowa Environmental Mesonet</a>.

opencc-by-4.0Mar 2018View details →

Summary for policymakers of the assessment report on land degradation and restoration of the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services: Figure SPM.1

The purpose of Figure  SPM.1 is to support the statement that land degradation occurs just about everywhere in the world (i.e. it is ‘pervasive’), takes many forms, and that examples of successful restoration are also widespread. The figure consists of a backdrop map of the world from a multiple land degradation perspective, showing the level of uncertainty between studies, overlaid with dots representing all the places specifically mentioned in the eight chapters of the main Assessment Report on Land Degradation and Restoration, including case studies of both degradation and restoration. Around the map are brief notes regarding the main forms of degradation encountered.

opencc-byNov 2019View details →

Identifying and Implementing Relevant Research Data Management Services for the Library at the University of Dodoma, Tanzania

This data set presents the results of research conducted at the University of Dodoma, Tanzania. The purpose of the research was to identify and report on relevant RDM services that need to be implemented so that researchers and university management could collaborate and make our research data accessible to the international community. The data set was used to support both the mini-dissertation as well as a paper published in the Data Science Journal. The journal paper presents findings on important issues for consideration when planning to develop and implement RDM services at a developing country, academic institution. The paper also mentions the requirements for the sustainability of these initiatives.

Fig. 3. 2 in Spores of Paenibacillus larvae, Ascosphaera apis, Nosema ceranae and Nosema apis in bee products supervised by the Brazilian Federal Inspection Service

Fig. 3. 2% agarose gel stained with SYBR Safe of the multiplex PCR products from royal jelly samples (1–10) obtained from markets of the state of São Paulo, Brazil. M, ® molecular marker 100 pb (Invitrogen); C+, positive control for N. ceranae (218 pb), N. apis (321 pb), A. apis (485 pb) and P. larvae (700 pb); C−, negative control.

Fig. 4. 2 in Spores of Paenibacillus larvae, Ascosphaera apis, Nosema ceranae and Nosema apis in bee products supervised by the Brazilian Federal Inspection Service

Fig. 4. 2% agarose gel stained with SYBR Safe of the multiplex PCR products from honey samples (1–17) obtained from markets of the state of São Paulo, Brazil. M, ® molecular marker 100 pb (Invitrogen); C+, positive control for N. ceranae (218 pb), N. apis (321 pb), A. apis (485 pb) and P. larvae (700 pb); C−, negative control.

Fig. 5. 2 in Spores of Paenibacillus larvae, Ascosphaera apis, Nosema ceranae and Nosema apis in bee products supervised by the Brazilian Federal Inspection Service

Fig. 5. 2% agarose gel stained with SYBR Safe of the multiplex PCR products from pollen samples (1–10) obtained from markets of the state of São Paulo, Brazil. M, ® molecular marker 100 pb (Invitrogen); C+, positive control for N. ceranae (218 pb), N. apis (321 pb), A. apis (485 pb) and P. larvae (700 pb); C−, negative control.