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Fig. 2 in Temporal variation in the behavior of Apis mellifera (Hymenoptera: Apidae) and Lycastrirhyncha nitens (Diptera: Syrphidae) on Pontederia sagittata (Commelinales: Pontederiaceae) inflorescences in relation to nectar availability
Fig. 2. Total activity time (± 95 % CI) of Apis mellifera and Lycastrirhyncha nites on inflorescences of L (black circle), M (gray circle) and S (white circle) morphs of Pontederia sagittata during daily periods of video-recording.
Fig. 1 in Temporal variation in the behavior of Apis mellifera (Hymenoptera: Apidae) and Lycastrirhyncha nitens (Diptera: Syrphidae) on Pontederia sagittata (Commelinales: Pontederiaceae) inflorescences in relation to nectar availability
Fig. 1. Position of styles and stamens and differences in pollen size in the three floral morphs of Pontederia sp. a) long-styled [L], b) mid-styled [M] and c) shortstyled [S] (Zomlefer 1994). Legitimate pollinations are indicated by arrows.
OLS, OntoPortal, Skosmos - API mapping specifications for API Gateway and Terminology Service Suite
<p>A set of initial API endpoints have been defined for the central API Gateway together with the initial set of service wrappers for each of the covered technologies (OLS, OntoPortal and Skosmos). This involves detailing the call URL structures, HTTP methods, service parameters, authentication methods, and expected response formats for each type of endpoint. <span>This document outlines the various backend APIs for the API Gateway and provides guidance on how to harmonise and map them. The API Gateway is used to provide information in a format that can be utilised by the widgets within the Terminology Service Suite (TSS). Each table in this document is related to one widget of the TSS.</span></p>
Mathematics Stack Exchange API Q&A Data
<p>This dataset was compiled as part of the ESPRC project "Example-driven machine-human collaboration in mathematics", for the purpose of doing text-based analysis of mathematical discourse and for the construction of a conversational mathematics bot.</p> <p>It consists of approximately 1 million mathematics questions and their respective answers, as well as markers of interaction quality (such as user-provided scoring of question and answer quality) and social dynamics (reputation scores, badges, etc).</p> <p>The data was obtained from the <a href="https://stackexchange.com/">StackExchange</a> website, by querying the <a href="https://api.stackexchange.com/">Stack Exchange API</a> according to its documentation.</p> <p> </p> <p> </p>
Dataset of "What Should Developers Be Aware Of? An Empirical Study on the Directives of API Documentation"
<p>Dataset of <em>What Should Developers Be Aware Of? An Empirical Study on the Directives of API Documentation</em> (Martin Monperrus, Michael Eichberg, Elif Tekes, Mira Mezini), In Empirical Software Engineering, Springer, 2011.</p> <p><br> * dataset-src.tar.bz2 contains the source code of the Java libraries used as raw data.<br> * dataset.xml.bz2 contains the API documentation extracted from source code.<br> * directives.xml.bz2 contains the API directives found during the exploratory case study.<br> * directive-appendix.pdf is a human-readable PDF version of directives.xml.bz2.<br> <br> All datasets are published under the Creative Commons Attribution License: if you use them, please cite:<br> </p>
API Database of Python frameworks & Labeled Issues
<p>PyLibAPIs.7z : contains public API data (MongoDB dump) for these frameworks:</p> <ul> <li>TensorFlow</li> <li>Keras</li> <li>Scikit-learn</li> <li>Pandas</li> <li>Flask</li> <li>Django</li> </ul> <p>Label.xlsx: contains issues and their labels</p> <p>Breaking Changes for All Frameworks.pdf: contains the breaking change distributions of all six frameworks</p>
Figures 1-4 in Ultrastructural detection of lipids in the cephalic salivary glands of Apis mellifera and Scaptotrigona postica (Hymenoptera: Apidae) workers
Figures 1-4. Lipids, detected using imidazole-osmium, in cephalic salivary gland (CSG) cells of Apis mellifera workers. (1-2) Small droplets of lipid (arrows) dispersed in the cellular cytoplasm of a newly emerged worker (NE), mitochondria (m) of medium electron density and narrow alveolar lumens (l) containing scarce IO-positive secretion (s). (3-4) Gland cells from workers working in the brood comb area (CA). Note lipid droplets dispersed in the cytoplasm (arrows) (1C), heterogeneous granules (gr) (1D) and large amounts of lipid secretion (s) in the alveolar lumen (l). (c) Cuticle, (n) nuclei. Scale bars: 1, 2, 4 = 1 µm, 3 = 3 µm.
Figures 5-8 in Ultrastructural detection of lipids in the cephalic salivary glands of Apis mellifera and Scaptotrigona postica (Hymenoptera: Apidae) workers
Figures 5-8. Imidazole-osmium preparations for lipid detection in gland cells from Apis mellifera workers. (5-6) Gland cells from worker working in the brood comb area (CA) (5) and forager (FO) (6) showing lipid droplets dispersed in the apical region (arrow), infolds (i) of the apical membrane forming channels flanked by mitochondria (m). (6) Note the presence of lipid droplets in the apical channels (arrows). (7-8) Osmium-imidazole-positive dots spread in the cytoplasm (arrows) and vesicles (ve) of forager cellular glands. (c) Cuticle, (s) secretion. Scale bars: 5 = 3 µm, 6 = 2 µm, 7-8 = 1 µm.
Figure 1 in Comparison of two morphometric methods for discriminating honey bee (Apis mellifera L.) populations in Turkey
Figure 1. Sampling locations in Turkey (Thrace: 1–2; Aegean: 3; Central Anatolia/ Mediterranean: 4–11; Southeastern Anatolia: 12–13; Northeastern Anatolia: 14–15).
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.
Fig. 1 in Effect of entomopathogens on Africanized Apis mellifera L. (Hymenoptera: Apidae)
Fig. 1. Survival curve (hours) of Africanized Apis mellifera workers. (A) Entomopathogens sprayed on A. mellifera, (B) entomopathogens on a smooth surface, (C) entomopathogens on soy leaves and (D) entomopathogens mixed with candy paste. Kaplan–Meier vs Weibull survival test adjusted for time (h). Temperature 27 ± 2 ◦ C, 12-h photophase and relative humidity of 60 ± 10%. Sterilized distilled water.
Fig. 1 in Behavioral repertoires and interactions between Apis mellifera (Hymenoptera: Apidae) and the native bee Lithurgus littoralis (Hymenoptera: Megachilidae) in flowers of Opuntia huajuapensis (Cactaceae) in the Tehuacán desert
Fig. 1. Behavior accumulation curves of bees in 150 flowers of Opuntia huajuapensis. A: Apis mellifera (1) and Lithurgus littoralis (2). B: L. littoralis females (3) and L. littoralis males (4). Dotted lines indicate the 95% confidence intervals.
Fig. 2 in Behavioral repertoires and interactions between Apis mellifera (Hymenoptera: Apidae) and the native bee Lithurgus littoralis (Hymenoptera: Megachilidae) in flowers of Opuntia huajuapensis (Cactaceae) in the Tehuacán desert
Fig. 2. Time spent (A) and mean feeding duration (B) in flowers of Opuntia huajuapensis by Apis mellifera females and Lithurgus littoralis females and males. No A. mellifera males were recorded at any time during the experiment. Vertical bars indicate 95% confidence intervals.
Collection of images and raw coordinates of honey bee (Apis mellifera) wings from the central highlands of Ecuador.
<p>Images and raw coordinates of honey bee (Apis mellifera) wings from the central highlands of Ecuador</p>
Fig. 1 in Current status of Acarapis woodi mite infestation in Africanized honey bee Apis mellifera in Brazil
Fig. 1. States evaluated for the presence of Acarapis woodi in Brazil. The blank area represents the states not surveyed; dark grey areas show the states surveyed in this study which presented negative results (circles with negative sign); light grey areas show previous studies from the 1970s.
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>
Conformance Assessment of Architectural Design Decisions on API Endpoint Designs Derived from Domain Models: Dataset and Code
<p>This is the dataset and related code artifact for the article "Conformance Assessment of Architectural Design Decisions on API Endpoint Designs Derived from Domain Models".</p> <p><strong>Abstract of the article:</strong></p> <p>Domain-driven design (DDD) is commonly used, especially in enterprise systems, to design microservices. A crucial aspects of microservice design is API design, which includes the design of API endpoints. In particular, we studied link mapping, API operation design, and resource segregation as API endpoint design issues that are linked to domain model design. Based on Architectural Design Decisions (ADD) studied in a prior empirical study on the interrelation of DDD and APIs, we suggest a new approach for the automated assessment of conformance to ADD options. This approach aims to support the continuous analysis of API endpoint designs. The approach suggests automated detectors to detect ADD options selected in a given API endpoint design, as well as an assessment scoring scheme based on our empirical results. For evaluation of our results, we first manually created a ground truth for 12 cases in a multi-case study, and then compared the results of our automated detectors to the ground truth for each of those cases.</p>
BioStars Posts API Output
<p>A dataset is extracted from BioStars (https://www.biostars.org/). Biostars is a question and answer forum that focuses on bioinformatics, computational genomics, and biological data analysis. </p> <p>This dataset contains output from the BioStars API (https://www.biostars.org/info/api/) through post ID (UID) 9557161. </p> <p>This dataset is licensed with the same license as BioStars content: https://www.biostars.org/info/about/ </p> <p>The author of this Zenodo dataset has no affiliation with the BioStars team. </p> <p>NOTE: No content was available for IDs from 9463943 to 494831. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.