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26 results for “Library Studies”
Dataset for: Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manusript:<br> Perrier L, Blondal E, MacDonald H. Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies. 2018; 40(3-4): 173-183. doi: 10.1016/j.lisr.2018.08.002</p> <p>Full-text available at: <a href="https://doi.org/10.1016/j.lisr.2018.08.002">https://doi.org/10.1016/j.lisr.2018.08.002</a> </p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset:</p> <ol> <li>Data Dictionary: RDMMetaEthnography_DataDictionary_v1.pdf</li> <li>Data Abstraction Sheet: RDMMetaEthnography_StudyCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_ParticipantCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_Outcomes.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_COREQ,csv</li> </ol> <p>Contact: Laure Perrier: <a href="https://orcid.org/0000-0001-9941-7129">orcid.org/0000-0001-9941-7129</a></p>
Comparative Analysis of Anthraquinone and Chalcone Derivatives-Based Virtual Combinatorial Library. A Cheminformatics "Proof-of-Concept" Study
<p>This computational “proof-of-concept” study illustrated the combinatorial approach used to explain how the selected natural products' structures undergo molecular diversity analysis. A virtual combinatorial library (1.6M) based on 20 anthraquinones and 24 chalcones were enumerated. The resulting compounds were optimized to the near drug-likeness properties and the physicochemical descriptors were calculated for all datasets including FDA, Non-FDA, and natural products (NPs) datasets from ZINC 15. UMAP and principal component analysis (PCA) were applied to compare and represent the chemical space coverage of each dataset. Subsequently, the Laplacian score, and Gini coefficient, were applied to delineate feature selection, and selectivity among properties respectively. Finally, we demonstrated the diversity between the datasets by employing Murcko’s, and central scaffolds systems, calculated three fingerprint descriptors, and analyzed their diversity by PCA and self-organizing maps (SOM). The optimized enumeration resulted in 1,610,268 compounds with NP-Likeness, and synthetic feasibility mean scores close to FDA, Non-FDA, and NPs datasets. The overlap between the chemical space of 1.6M was more prominent with NPs. Laplacian score has prioritized NP-likeness and hydrogen bond acceptor properties (1.0 and 0.923) respectively, while the Gini coefficient showed that all properties have selective effects on datasets (0.81 to 0.93). Scaffold and fingerprint diversity indicated that the descending order for the tested datasets was FDA, Non-FDA, NPs, 1.6M. Virtual combinatorial libraries based on NPs can be considered as a source of the combinatorial compound with NP-likeness properties. Furthermore, measuring molecular diversity is supposed to be performed by different methods to allow for comparison and better judgment. </p> <p>This link provides an illustration of the whole virtual combinatorial library using the TMAP algorithm in addition to the complete dataset. TMAP is a recent algorithm applied to visualize ultra-large high-dimensional chemical libraries for structures and physicochemical properties (Probst & Reymond, 2020). This approach creates and distributes intuitive tree representations of big data sets with arbitrary dimensionality in the order of 10<sup>7</sup>.</p> <p><strong>To visualize the whole library of compounds, download the "index(2).rar", then extract the index.html that pop-up in the WinRAR application.</strong></p>
A Study of Undefined Behavior Across Foreign Function Boundaries in Rust Libraries
<p>Developers rely on the static safety guarantees of the Rust programming language to write secure and performant applications. However, Rust is frequently used to interoperate with other languages which allow design patterns that conflict with Rust’s evolving aliasing models. Miri is currently the only dynamic analysis tool that can validate applications against these models, but it does not support finding bugs in foreign functions, indicating that there may be a critical correctness gap across the Rust ecosystem. We conducted a large-scale evaluation of Rust libraries that call foreign functions to determine whether Miri’s dynamic analyses remain useful in this context. We used Miri and an LLVM interpreter to jointly execute applications that call foreign functions, where we found 46 instances of undefined or undesired behavior in 37 libraries. Three bugs were found in libraries that had more than 10,000 daily downloads on average during our observation period, and one was found in a library maintained by the Rust Project. Many of these bugs were violations of Rust’s aliasing models, but the latest Tree Borrows model was significantly more permissive than the earlier Stacked Borrows model. The Rust community must invest in new, production-ready tooling for multi-language applications to ensure that developers can detect these errors.</p>
Research Artefact: An Empirical Study of React-Library Related Issues via Stack Overflow
<p>This research artifact accompanies the paper titled "An Empirical Study of React-Library Related Issues via Stack Overflow." It is a comprehensive repository that includes the collected dataset containing 447,542 React-related Stack Overflow question posts, as well as 384 representative samples obtained randomly. The primary objective of this artifact is to facilitate the replication of our dataset for researchers and allow them to utilize it for further investigations and research purposes.</p>
Understanding API Usage and Testing: An Empricial Study of C Libraries (Artifact)
<h1>LibProbe Artifact</h1> <p>For the sake of the evaluation we preprocessed the CCScanner data to identify all clients of the libraries used in our evaluation and included those in our <code>MongoDB</code> database. This artifact will start by importing this pre-processed dependency information into a docker image which is then used for the evaluation.</p> <h2>Running the docker image</h2> <ul> <li> <p>First load the docker image by running</p> <ul> <li> <div> <div><code>gunzip -c libprobe_v2.2.tar.gz | sudo docker import - libprobe:latest </code></div> </div> </li> </ul> <p>this will load the image in your local docker images.</p> </li> <li> <p>Run a container from the image: <code>sudo docker run -it --device /dev/snd --privileged libprobe:latest /bin/bash</code> This will run a docker container which maps the pulseaudio and alsa configurations from your local host to the docker image. This is necessary to get some clients for some target libraries to build correctly. You will need an ubuntu host machine which has pulseaudio and alsa installed.</p> </li> <li>Run <code>mongod --fork --logpath /var/log/mongodb/mongod.log</code> followed by <code>mongorestore --drop --db apiusage /tmp/libprobe/database/apiusage</code> to import the results saved in the artifact. </li> </ul> <h2>Validating analysis results</h2> <ul> <li>The docker image provided does not contain any clients due to size limitations on sharing. The Mongo database contains all the results of running this evaluation.</li> <li>We provide a script <code>clone_clients.py</code> with a <code>client_repos.json </code>file in <code>/tmp/libprobe/extra</code> which can be used to clone the clients in the clients directory. </li> <li>To get the results it's possible to run <code>python3 libprobe.py analyse all -n</code> from <code>/tmp/libprobe</code>. This will overwrite the JSON files in the <code>json_files</code> directory and overwrite the graphs in the <code>graphs</code> directory.</li> </ul> <h2>Running the evaluation for one library (vorbis)</h2> <ul> <li>Download clients: Go to <code>/tmp/libprobe</code> and run <code>python3 libprobe.py download vorbis</code></li> <li>Prepare the library: <ul> <li>In /tmp/data/libraries/xiph@@vorbis run make clean then make and make check and make install.</li> <li>Copy all C files from /tmp/data/libraries/xiph@@vorbis/lib to /tmp/data/libraries/xiph@@vorbis/lib/.libs to collect accurate API coverage information.</li> </ul> </li> <li>Process the library to get the APIs and the coverage information : <code>python3 libprobe.py processlib vorbis</code></li> <li>Prepare clients for excluding sub directories that might contain vorbis library code: <code>python3 libprobe.py prepclients vorbis</code></li> <li>Get client usages: <code>python3 libprobe.py fetchusages vorbis</code></li> <li>Analyse: <code>python3 libprobe.py analyse vorbis -n</code></li> <li>(optional) Measure differential coverage for improved coverage libs: <code>python3 libprobe.py coverage vorbis</code></li> </ul> <h2>Running the evaluation for all libraries (this requires at least 300GB of disk space)</h2> <ul> <li>Download clients: Go to <code>\tmp\libprobe</code> and run <code>python3 libprobe.py download all</code></li> <li>Process the libraries: <code>python3 libprobe.py processlib all</code></li> <li>Prepare clients: <code>python3 libprobe.py prepclients all</code></li> <li>Get usages: <code>python3 libprobe.py fetchusages all</code></li> <li>Analyse: <code>python3 libprobe.py analyse all -n</code></li> <li>(optional) Measure differential coverage for improved coverage libs: <code>python3 libprobe.py coverage <library></code></li> </ul> <h2>Getting baseline coverage for libraries</h2> <p>All libraries are cloned in <code>/tmp/data/libraries</code> and clients are cloned in <code>/tmp/data/clients</code>.</p> <div> </div> <ul> <li>MBedtls: Copy the script <code>coverage_mbedtls.sh</code> from <code>/tmp/libprobe/extra</code> into the the build directory of Mbedtls <code>/tmp/data/libraries/Mbed-TLS@@mbedtls/build</code> and run <code>./coverage_mbedtls.sh</code> followed by <code>genhtml baseline.info --output-directory</code> out this will calculate the baseline coverage for mbedtls.</li> <li>FFTW: Copy the script coverage.sh from /tmp/libprobe/extra into the the root dir of FFTW and run ./coverage.sh baseline this will calculate the baseline coverage for fftw</li> <li>HDF5: Copy the script coverage_hdf.sh from /tmp/libprobe/extra into the the root dir of HDF and run <code>./coverage_hdf.sh baseline</code> this will calculate the baseline coverage for HDF.</li> <li>LMDB: Copy the script coverage_lmdb.sh from /tmp/libprobe/extra into the <code>/tmp/data/libraries/LMDB@@lmdb/libraries/liblmdb</code> and run <code>./coverage_lmdb.sh baseline</code> this will calculate the baseline coverage for LMDB.</li> <li>Zip: Copy the script coverage_zip.sh from /tmp/libprobe/extra into <code>/tmp/data/libraries/kuba--@@zip/build/CMakeFiles/zip.dir/sr</code>c and run <code>./coverage_zip.sh baseline</code> this will calculate the baseline coverage for zip.</li> <li>Vorbis: Copy the script cal_cov.py from /tmp/libprobe/extra to <code>/tmp/data/libraries/xiph@@vorbis/lib/.libs</code> and then copy all source files in the <code>.libs</code> folder by running <code>cp ../*.c .</code> from the <code>.libs</code> folder. Finally run <code>python3 cal_cov.py .</code>.</li> <li>XXhash: Copy the script coverage.sh from /tmp/libprobe/extra into /tmp/data/libraries/Cyan4973@@xxHash and run ./coverage.sh baseline this will calculate the baseline coverage for xxhash</li> </ul> <h2>Reproducing increased coverage using clients</h2> <ul> <li> <p>LMDB: The client we will use is Knot DNS.</p> <ul> <li>Change directory to <code>/tmp/data/clients/CZ-NIC@@knot</code> and run <code>autogen.sh</code>.</li> <li>Run <code>./configure --with-lmdb=/usr/local</code>.</li> <li>Then <code>make && make check</code>.</li> </ul> <p>Now go back to the LMDB directory and run</p> <ul> <li> <p>Run <code>./coverage_lmdb.sh after_knot</code>.</p> </li> <li> <p>Now go /tmp/libprobe and run <code>python3 libprobe.py coverage lmdb</code></p> </li> </ul> </li> <li> <p>VORBIS: The client we will use in SFML.</p> <p>Go to the vorbis library dir <code>/tmp/data/libraries/xiph@@vorbis</code> and run <code>make clean</code>.</p> <ul> <li>Run <code>make && make check && make install</code>.</li> <li>Go to the <code>.libs</code> folder and copy all c files there by doing <code>cp ../*.c .</code>.</li> <li>Copy <code>/tmp/libprobe/extra/cal_cov.py</code> into the <code>.libs</code> folder and run <code>python3 cal_cov.py .</code>. This will show the baseline coverage.</li> </ul> <p>Go to the SFML directory <code>/tmp/data/clients/SFML@@SFML</code>.</p> <ul> <li>Create build directory <code>mkdir build && cd build</code>.</li> <li>Run <code>cmake -DSFML_BUILD_TEST_SUITE=TRUE -GNinja ..</code>.</li> <li>Run <code>ninja</code>.</li> <li>Run <code>ctest</code>. You will see some failing tests. Thats normal as we are only interested in the Audio tests for vorbis. All Audio tests should pass.</li> </ul> <p>Go back to the <code>.libs</code> folder in vorbis and re-run the <code>cal_cov.py</code> script.</p> <ul> <li>Now go /tmp/libprobe and run <code>python3 libprobe.py coverage vorbis</code></li> </ul> </li> </ul> <ul> <li>SDL: The client we will use in UFOAI.<br> <ul> <li> <p>Go to the SDL library dir <code>/tmp/data/libraries/libsdl-org\@\@SDL</code> and then the <code>build2</code> directory where the built library is. .</p> </li> <li>Run <code>make clean && make && make install && make test</code>.</li> <li>Copy <code>/tmp/libprobe/extra/coverage_sdl.sh</code> into the <code>build2</code> folder and run <code>./coverage_sdl.sh baseline</code>. Run <code>genhtml baseline.info --output-directory out</code></li> </ul> </li> </ul> <p> Go to the UFOAI directory <code>/tmp/data/clients/ufoaiorg\@\@ufoai</code></p> <ul> <li> <ul> <li>Run <code>./configure --target-os=linux --disable-uforadiant</code> && <code>make</code>.</li> <li>Run .<code>/testall</code></li> </ul> </li> </ul> <p> Now go /tmp/libprobe and run <code>python3 libprobe.py coverage sdl</code></p> <p> </p> <ul> <li>FFTW: The client we will use in CAVA.</li> </ul> <p> Go to the FFTW3 library dir <code>/tmp/data/libraries/FFTW@@fftw3</code> and run <code>reset_cov.sh</code> then <code>make clean</code></p> <ul> <li> <ul> <li>Run <code>make && make install && make check</code>.</li> <li>Copy <code>/tmp/libprobe/extra/coverage.sh</code> into root directory of the library and run the <code>.libs</code> folder and run <code>./coverage.sh baseline</code> This will show the baseline coverage.</li> </ul> </li> </ul> <p> Go to the CAVA directory <code>/tmp/data/clients/karlstav@@cava</code>.</p> <ul> <li> <ul> <li>Run <code>./autogen.sh</code> followed by <code>./configure</code> then <code>make</code></li> <li>Run the script <code>./run_all_tests.sh</code> . </li> </ul> </li> </ul> <p> Go back to the FFTW3 library and run <code>./coverage.sh after_cava</code></p> <ul> <li> <ul> <li>Now go /tmp/libprobe and run <code>python3 libprobe.py coverage fftw3</code></li> </ul> </li> </ul>
An Empirical Study on the Usage and Availability of Machine Learning Libraries in Open-Source Python Projects - Dataset
<p>This repository contains the dataset of the manuscript:</p> <p>"An Empirical Study on the Usage and Availability of Machine Learning Libraries in Open-Source Python Projects"</p>
Data from: How "simple" methodological decisions affect interpretation of population structure based on reduced representation library DNA sequencing: a case study using the lake whitefish
Reduced representation (RRL) sequencing approaches (e.g., RADSeq, genotyping by sequencing) require decisions about how much to invest in genome coverage and sequencing depth (library quality), as well as choices of values for adjustable bioinformatics parameters. To empirically explore the importance of these "simple" decisions, we generated two independent sequencing libraries for the same 142 individual lake whitefish (Coregonus clupeaformis) using a nextRAD RRL approach: (1) A small number of loci and low sequencing depth (library A); and (2) more loci and higher sequencing depth (library B). The fish were selected from populations with different levels of expected genetic subdivision. Each library was analyzed using the STACKS pipeline followed by three types of population structure assessment (FST, DAPC and ADMIXTURE) with iterative increases in the stringency of sequencing depth and missing data requirements, as well as more specific a priori population maps. Library B was always able to resolve strong population differentiation in all three types of assessment regardless of the selected parameters. In contrast, library A produced more variable results; increasing the minimum sequencing depth threshold (-m) resulted in a reduced number of retained loci, and therefore lost resolution at high -m values for FST and ADMIXTURE, but not DAPC. FST and DAPC were robust to varying the population map and increasing the stringency of missing data requirements. In contrast, ADMIXTURE was unable to resolve strong population differentiation when increasing these same parameters in library A. Similarly, when examining fine scale population subdivision, library B was robust to changing parameters but library A lost resolution depending on the parameter set. We used library B to examine actual subdivision in our study populations. All three types of analysis found complete subdivision among populations in Lake Huron, ON and Dore Lake, SK, Canada using 10,640 SNP loci. Weak population subdivision was detected in Lake Huron with fish from sites in the north-west, Search Bay, North Point and Hammond Bay, showing slight differentiation. Overall, we show that apparently simple decisions about library quality and bioinformatics parameters can have potentially important impacts on the interpretation of population subdivision. Although costly, the early investment in a high-quality library and more conservative stringency settings on STACKS parameters lead to a final dataset that was more consistent and robust when examining both weak and strong population differentiation.
Evaluating the Impact of Third-Party Library Reuse in Java Projects: An Empirical Study
<p>Update, we have added the following:<br> - `dataset.ipynb`: Computes all the dataset used for this study. The user needs to use a call graph generator like OPAL/WALA.</p> <p>- `loc.ipynb`: Computes a dataset for LOC information given a call graph dataset</p> <p>- `called-reuse.ipynb`: Computing a dataset for all called functions and LOC in third-party libraries</p> <p>- `megamorphic.ipynb`: Empirically decide the megamorphic call sites. <br> <br> This part of the submission related to the paper "Evaluating the Impact of Third-Party Library Reuse in Java Projects: An Empirical Study". Here is a table of contents:</p> <ul> <li>`census.csv`: extracted data from the Census-II</li> <li>`releases_all.csv`: all releases available from Maven Coordinates in Census-II</li> <li>`releases_jar.csv`: dataset of all releases with a POM and JAR file</li> <li>`releases_jar_6_months.csv`: releases selected when studiying the 6-month interval</li> <li>`releases_jar_6_months.csv`: releases selected when comparing consecutive versions per artefactid and groupid</li> <li>`loc_composition.csv`: data on the ratio of insourced and outsourced code in project releases</li> <li>`loc_desc_without_lambdas.csv`: mined data on LOC information and class information.</li> <li>`loc_reuse_lower_bound.csv`: data on the ratio of reused LOC and functions of outsourced code, lower-bound</li> <li>`loc_reuse_upper_bound.csv`: data on the ratio of reused LOC and functions of outsourced code, upper-bound</li> </ul>
Data from: How “simple” methodological decisions affect interpretation of population structure based on reduced representation library DNA sequencing: a case study using the lake whitefish
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Data from: Substantial differences in bias between single-digest and double-digest RAD-seq libraries: a case study
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Gładysz, K., Wrochna M., & Popek, R. (2024) Tracking Particulate Matter Accumulation on Green Roofs: A Comparative Study at Warsaw University Library - DATA
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Characterizing Support for a Third-party Library: A Study of External Pull Requests for npm packages
<p>Third-party libraries play a key role in building contemporary software applications. Despite this, most libraries are open source that often rely on volunteer (usually unpaid and overworked) contributions for their sustainability. Our motivation is to understand the extent to which third-party libraries are supported by contributions in the form of Pull Requests (PR) from outside the project team (i.e., External PR). Concretely, we analyze 1,076,123 PRs to investigate the External PR prevalence, bots, and the PR characteristics. Our results show that external contributions are prevalent, with packages receiving a high rate of (median of 73.45%) External PR . Furthermore, contributors are also submitting a high proportion of External PR (median of 87.62%). Results indicate a statistical difference in the acceptance of PRs submitted by bots compared to abandoned or open PRs. Furthermore, comparing external and internal PR, we find that Internal PR are more likely to be accepted. Statistically, we find that submitted patches (i.e., commit and code metrics) submitted by Internal PR are higher than patches submitted by External PR. We find that the External PR and Internal PR both have the same content (i.e., introducing new features and fixing bugs). Differently, External PR have more PRs that relate to documentation content, while Internal PR relates to refactoring-related changes</p>
Supplementary materials for "The values of public libraries: a systematic review of empirical studies of stakeholder perceptions"
<p>Supplementary materials for "The values of public libraries: a systematic review of empirical studies of stakeholder perceptions"</p> <p>https://www.emerald.com/insight/0022-0418.htm</p>
Is There an Academic Elegancy in the Field of Library Science and Information Science? Are the Higher Impact Studies Citing the Studies Published in the Higher Impact Journals?
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Replication Package for "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"
<p># Replication Package for the Paper: "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"</p> <p>This replication package includes the raw data, questionnaire answers, and a Python notebook to reproduce the results detailed in the paper "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"</p> <p>## Repository Structure<br>1. **Results:** Contains Excel files with the responses from the 2 human experts and the 5 models and the review of the 3 human reviewers.<br>2. **Tables:** Contains the full Wilcoxon Test Results for H01 and H02 as well as the Anderson Darling test for normality and the Spearmans' Rho.</p> <p>## Replication Process<br>To replicate the results of our study, open the provided Python Notebook in Google Colab and follow the instructions to reproduce the results seamlessly.</p> <p># Instructions for Use<br>To utilize this replicability package, refer to the steps outlined in the notebook file.</p> <p># Remarks<br>If you encounter any issues or have any questions, please get in touch with the paper's authors. We will be glad to assist you!</p>
Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries in Software Package Ecosystems
<p>Replication package for the paper "Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries in Software Package Ecosystems "</p>
Supplementary material 1 from: Bourret A, Nozères C, Parent E, Parent GJ (2023) Maximizing the reliability and the number of species assignments in metabarcoding studies using a curated regional library and a public repository. Metabarcoding and Metagenomics 7: e98539. https://doi.org/10.3897/mbmg.7.98539
Creation of Gulf of St. Lawrence regional library (GSL-rl) and creation of an eDNA metabarcoding dataset
A study of Mobile Technology Capabilities and Challenges in Library Service Delivery
<p>Mobile Technology has provided faster access to and delivery of information and other related services; it is also challenging the libraries and information centers to change round, modify and upgrade their services approach by implementing the new technological changes. Nowadays all mobile devices are gradually becoming an integral part of all and sundry thereby redirecting connectivity and interactions globally with dynamic feature compare to the static state of the computer technology. From the study, Mobile Technology capabilities will greatly support all forms of information access, storage, transmission etc. and provide help to researchers and users of research content to the strengthening of their relationship and providing enhanced user oriented services to existing users. The challenges facing mobile technology were also identified with a view of recommending to developers needed areas in satisfying local users. The study discusses on the capabilities and challenges of mobile technology in general services delivery</p>
THE RESEARCHES IN UZBEKISTAN ON THE STUDY OF THE TEMURIDS PERIOD OF HERITAGE STORED IN LARGE MUSEUMS AND LIBRARIES OF TURKEY
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IDSL.CSA annotation coverage for ST000929 and ST001000 study by DDA and CSA libraries
<p>IDSL.CSA annotation coverage for ST000929 and ST001000 study by DDA and CSA libraries</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.