Find research datasets worth reusing

opencc-by-4.0Dec 2024View details →

Mari4_YARD - Cost Effective Projection - Dataset

<div> <div>Welcome to the Pan/Tilt Projection Mechanism Demonstration Results Dataset, a unique collection of data showcasing the projection of information using a two degree of freedon mechanism based projection system. This dataset is designed to facilitate research in robotics and perception, particularly in the areas of Augmented Reality.This dataset is generated during the demonstration of the projection technology at Brodosplit and AIMEN facilities during technology demonstrations.</div> </div>

opencc-by-4.0Sep 2024View details →

Supplementary material: The FEniCS Project on AWS Graviton3

This archive contains the manuscript source, raw data and plotting code for the paper "The FEniCS Project on AWS Graviton3". The associated repository is located at https://github.com/michalhabera/aws-graviton-benchmark

opencc-by-4.0Feb 2024View details →

FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution

<h2>Overview</h2> This meteorological data set consists of climatologies (climate indicators) on 30-year average basis for Europe and covers two historical periods as well as two periods for three selected climate scenarios with a high spatial resolution of less than 1 km. The two 30-year periods provided for the observations allow the analysis of the climate change that has already happened.  Resolution: 30x30 arcsec Projection: EPSG 4326 Extent for historical data: 10.67°W – 47.67°E, 33.68°N – 71.33°N Extent for scenario data: 10.67°W – 39.33°E, 33.68°N – 71.33°N Periods for historical data: 1961-1990 and 1991-2020 Periods for scenario data: 2036-2065 and 2071-2100 Format: GeoTIFF List of climatologies (climate indicators)   <table> <tbody> <tr> <td> # </td> <td> Short name </td> <td> Name </td> <td> Description </td> <td> Unit </td> <td> Yearly (Y) or monthly (M) </td> </tr> <tr> <td> 1 </td> <td> tasmin </td> <td> Average daily minimum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 2 </td> <td> tasmax </td> <td> Average daily maximum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 3 </td> <td> tas </td> <td> Average temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 4 </td> <td> tas_warmest_month </td> <td> Average temperature mean in the warmest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 5 </td> <td> tas_coldest_month </td> <td> Average temperature mean in the coldest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 6 </td> <td> tasmin_coldest_month </td> <td> Average temperature minimum in the coldest month </td> <td> Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 7 </td> <td> tasmax_warmest_month </td> <td> Average temperature maximum in the warmest month </td> <td> Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 10 </td> <td> GSL </td> <td> Average length of the growing season </td> <td> The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 12 </td> <td> GDD </td> <td> Average Growing Degree Days per year above 5°C </td> <td> Σ(Tmean – 5°C) per year.  </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 13 </td> <td> FD_first </td> <td> Average date of the first frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 14 </td> <td> FD_last </td> <td> Average date of the last frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 20 </td> <td> GLO_hori </td> <td> Average sum of global radiation </td> <td>   </td> <td> kWh </td> <td> Y, M </td> </tr> <tr> <td> 33 </td> <td> pr </td> <td> Average precipitation sum </td> <td>   </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 39 </td> <td> ET0 </td> <td> Average annual potential evapotranspiration </td> <td> Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 40 </td> <td> WBAL </td> <td> Average climatic water balance </td> <td> Precipitation minus potential evapotranspiration </td> <td> mm </td> <td> Y, M </td> </tr> </tbody> </table> <h2>Data sources</h2> The raw historical data is a combination or extension of daily CHELSA (Climatologies at high resolution for the earth’s land surface areas) with ERA5-Land to fully cover 1961-2020.  <ul> <li>CHELSA-W5E5 v1.0 (https://doi.org/10.5194/essd-15-2445-2023) for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), maximum temperature (tasmax) and minimum temperature (tasmin) for the period 1979-2016</li> <li>CHELSA V2.1 for climatological average monthly wind speed (sfcWind_01, ..., sfcWind_12) and for climatological mean vapor pressure deficit (vpd_01, ..., vpd_12)</li> <li>ERA5-Land for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), dew point (tds), and wind speed (sfcWind) for the period 1961-2020</li> <li>v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.</li> </ul> Climate models from EURO-CORDEX (doi.org/10.1007/s10113-013-0499-2): <ul> <li>MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17</li> <li>MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17</li> <li>ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4</li> </ul>

opencc-by-4.0Nov 2024View details →

E2EGit: A Dataset of End-to-End Web Tests in Open Source Projects

ABSTRACT  End-to-end (E2E) testing is a software validation approach that simulates realistic user scenarios throughout the entire workflow of an application. In the context of web applications, E2E testing involves two activities: Graphic User Interface (GUI) testing, which simulates user interactions with the web app’s GUI through web browsers, and performance testing, which evaluates system workload handling. Despite its recognized importance in delivering high-quality web applications, the availability of large-scale datasets featuring real-world E2E web tests remains limited, hindering research in the field. To address this gap, we present E2EGit, a comprehensive dataset of non-trivial open-source web projects collected on GitHub that adopt E2E testing. By analyzing over 5,000 web repositories across popular programming languages (JAVA, JAVASCRIPT, TYPESCRIPT, and PYTHON), we identified 472 repositories implementing 43,670 automated Web GUI tests with popular browser automation frameworks (SELENIUM, PLAYWRIGHT, CYPRESS, PUPPETEER), and 84 repositories that featured 271 automated performance tests implemented leveraging the most popular open-source tools (JMETER, LOCUST). Among these, 13 repositories implemented both types of testing for a total of 786 Web GUI tests and 61 performance tests. DATASET DESCRIPTION  The dataset is provided as an SQLite database, whose structure is illustrated in Figure 3 (in the paper), which consists of five tables, each serving a specific purpose. The repository table contains information on 1.5 million repositories collected using the SEART tool on May 4. It includes 34 fields detailing repository characteristics. The non_trivial_repository table is a subset of the previous one, listing repositories that passed the two filtering stages described in the pipeline. For each repository, it specifies whether it is a web repository using JAVA, JAVASCRIPT, TYPESCRIPT, or PYTHON frameworks. A repository may use multiple frameworks, with corresponding fields (e.g., is web java) set to true, and the field web dependencies listing the detected web frameworks. For Web GUI testing, the dataset includes two additional tables; gui_testing_test _details, where each row represents a test file, providing the file path, the browser automation framework used, the test engine employed, and the number of tests implemented in the file. gui_testing_repo_details, aggregating data from the previous table at the repository level. Each of the 472 repositories has a row summarizing the number of test files using frameworks like SELENIUM or PLAYWRIGHT, test engines like JUNIT, and the total number of tests identified. For performance testing, the performance_testing_test_details table contains 410 rows, one for each test identified. Each row includes the file path, whether the test uses JMETER or LOCUST, and extracted details such as the number of thread groups, concurrent users, and requests. Notably, some fields may be absent—for instance, if external files (e.g., CSVs defining workloads) were unavailable, or in the case of Locust tests, where parameters like duration and concurrent users are specified via the command line. To cite this article refer to this citation: @inproceedings{di2025e2egit,   title={E2EGit: A Dataset of End-to-End Web Tests in Open Source Projects},   author={Di Meglio, Sergio and Starace, Luigi Libero Lucio and Pontillo, Valeria and Opdebeeck, Ruben and De Roover, Coen and Di Martino, Sergio},   booktitle={2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (MSR)},   pages={10--15},   year={2025},   organization={IEEE/ACM} } This work has been partially supported by the Italian PNRR MUR project PE0000013-FAIR.

opencc-by-4.0Oct 2024View details →

Boulez_dataset_PoD: Dataset containing results from the research project Points of Discontinuity concerning Pierre Boulez, Le Marteau sans Maître pour voix d'alto et 6 instruments (1953–57), no. 7: après "l'artisanat furieux". Rapide

The complete datasets resulting from the research project Points of Discontinuity contain 23 datasets for the musical works or excerpts that were part of the online listening experiment, with each dataset containing seven or eight files (all audio files are stored in a dataset with restricted access), as well as a dataset (PoD_general_dataset) with five additional files. This dataset Boulez_dataset_PoD contains eight files: <ul> <li>Boulez_01_ReadMe.pdf</li> <li>Boulez_02_data.xlsx (processed data for this work)</li> <li>Boulez_03_individual_data.xlsx (raw data for each participant obtained from the experiment)</li> <li>Boulez_04_audio.mp3 [non-public] (audio recording used in the experiment) [stored in the restricted dataset <a href="https://doi.org/10.5281/zenodo.13981214" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981214</a>]</li> <li>Boulez_05_model_results.sv (graphical representation of results and the model in Sonic Visualiser) [requires audio file Berg_04_audio.mp3 to display correctly]</li> <li>Boulez_06_SV-data_model+results.zip (text files with the marker locations for all layers in Sonic Visualiser)</li> <li>Boulez_07_model+results_SV-screenshot.pdf (a screenshot of the full-screen display of the SV-file)</li> <li>Boulez_08_annotated_score.pdf (model analysis annotated in the score)</li> </ul>

opencc-by-4.0Mar 2024View details →

SINGER-inferred targets for exceptional population differentiation in coalescence times in African populations in 1000 Genomes Project

This repo saves the gene targets which shows the signal of population differentiation in coalescence times in African populations in 1000 Genomes Project.  Here are the detailed explanations for the header of the files: chrom               The chromosome where the genomic window is located (e.g., chr1, chrX). window_start        The 0-based start position of the genomic window being analyzed. window_end          The 1-based end position of the genomic window (exclusive). score               Ratio between the overall diversity and the population specific diversity, in the genomic window. transcript_chrom    The chromosome where the transcript is located. transcript_start    The 0-based start position of the transcript. transcript_end      The 1-based end position of the transcript (exclusive). transcript_id       The unique identifier for the transcript (e.g., Ensembl or RefSeq ID). strand              The strand of the transcript: "+" for forward, "-" for reverse. coding_start        The start position of the coding region of the transcript (if applicable). coding_end          The end position of the coding region of the transcript (if applicable). RGB                 The RGB color used for visualization in genome browsers (format: R,G,B). block_count         The number of exons in the transcript. block_sizes         Comma-separated list of exon lengths (in base pairs). block_starts        Comma-separated list of exon start positions relative to transcript_start.

opencc-by-4.0Oct 2021View details →

Evaluation of different Open Science projects based on Vienna principles.

Evaluation of different Open Science projects based on Vienna principles.

openother-openNov 2021View details →

globalbioticinteractions/AEC-DBCNet: Collaborative databasing of North American bee collections within a global informatics network project archive

Data in this archive are from the Collaborative databasing of North American bee collections within a global informatics network project. Data was originally captured using Arthropod Easy Capture software developed at the American Museum of Natural History (AMNH), New York. Project lead investigators are John Ascher (Principal Investigator) and Jerome Rozen (Co-Principal Investigator) at the AMNH, and Douglas Yanega (Principal Investigator), University of California Riverside. Please use this citation for this archive: John Ascher, Digital Bee Collections Network data archive from the Collaborative databasing of North American bee collections within a global informatics network project. Version: 08 Mar 2016. https://doi.org/10.5281/zenodo.1436853 This project was supported by the National Science Foundation grant <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956388">DBI 0956388</a> and <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956340">DBI 0956340</a> ABSTRACT Natural history collections contain millions of bee specimens documenting the geographic ranges, temporal occurrence patterns, and floral associations of the 20,000 described bee species. This project will digitize and consolidate specimen records from 10 bee collections across the United States. The investigators will make or verify species identifications, capture full label data, georeference and error-check localities, and upload this information to publicly accessible databases. Web-based tools will be used to capture data across collections efficiently, validate bee and plant names through automated comparison with taxonomic authority files, and synthesize data on species pages with images, digitized literature records, and other information about bees and their host plants. Data will be uploaded to the Global Biodiversity Information Facility and to Discover Life (www.discoverlife.org), a website that features customizable global maps for all global bee species and dynamic identification keys for North American species. To obtain information needed to conserve and manage pollinators, the investigators will work with ecologists to model geographic and temporal trends in bee populations in relation to environmental variables. Bees are the most important pollinators of the approximately 1/3 of crops that require animal pollination. Recent declines in honey bee populations highlight the need to understand better the roles of native bees in agricultural and natural systems. This project will help predict risks to bees and their pollination services from climate change, habitat loss, and other factors. The outreach program Bee Hunt (www.discoverlife.org/bee) will educate the public, including students in underserved communities, about bee diversity and the importance of pollination services. Using digital photography and rigorous research protocols, Bee Hunt will empower people at biological field stations, nature centers, parks, schools, and other sites to collect high-quality data to augment information from specimen records.

opencc-by-4.0Dec 2022View details →

Bacteriophage genomes in the Japanese 4D microbiome project

4,709 bacteriophage genomes constructed from the metagenomic dataset in the Japanese 4D microbiome project

opencc-by-nc-4.0Oct 2011View details →

tci20111028 - Put-project stimulus task

This dataset contains files recorded from Nakre Ruth Abia. Her descriptions are prompted by a set of videos from the <a href="http://fieldmanuals.mpi.nl/volumes/2004/put-project/">put-project</a>, developed at the Max Planck Institute for Psycholinguistics, Nijmegen. The dataset includes: <ul> <li>audio files (tci20111028-01.wav, tci20111028-02.wav, tci20111028-03.wav)</li> <li>transcription file (tci20111028a-01.eaf)</li> </ul> Note that the transcription file in this record is not up to date! For an updated version of this file, please download the most recent version of the Komnzo text corpus as a zip-file under <a href="https://doi.org/10.5281/zenodo.1306246">10.5281/zenodo.1306246</a> The material was recorded by Christian Döhler as part of a language documentation project for his PhD. The project was located at the <a href="http://chl.anu.edu.au/">School of Culture, History and Language</a> at the <a href="http://anu.edu.au">Australian National University, Canberra</a>. For the most part it was funded by the <a href="http://dobes.mpi.nl/">DOBES project</a> of the <a href="https://www.volkswagenstiftung.de/en/foundation">Volkswagen Foundation</a>. Reference: Bowerman, M., Gullberg, M., Majid, A., & Narasimhan, B. (2004). Put project: The cross-linguistic encoding of placement events. In A. Majid (Ed.), Field Manual Volume 9 (pp. 10-24). Nijmegen: Max Planck Institute for Psycholinguistics. doi:<a href="https://doi.org/10.17617/2.492916">10.17617/2.492916</a>.

opencc-by-2.0Nov 2021View details →

Personal Online Dosimetry Using Computational Methods: The PODIUM Project and the Future of Active Dosimetry

Individual monitoring of workers exposed to external ionizing radiation is essential to allow application of the ALARA principle and follow up of the official dose limits. However, large uncertainties still exist in personal dosimetry. Also, many practical problems exist for personal dosimetry, with many dosemeters getting lost and the reluctance of many workers to wear one or more dosemeters. Most legal dosimetry is done with passive dosemeters, which are analyzed after the wearing period in an accredited lab. Active dosemeters are also widely used, although mostly only for ALARA purposes or for specific exposure situations. Although the active dosemeters are dosimetrically and technically at least equivalent to passive dosemeters, their higher cost limits their use as only legal dosemeters. In an attempt of reinventing dosimetry by using the modern evolutions in simulations, artificial intelligence and computer vision, the PODIUM project was set up. PODIUM was a short feasibility project, funded by the EC CONCERT programme. The objective of the PODIUM project was to improve personal dosimetry by an innovative approach: the development of an online dosimetry application based on computer simulations without the use of physical dosemeters. Operational quantities, protection quantities and radiosensitive organ doses (e.g. eye lens, brain, heart, extremities) can be calculated based on the use of modern technology such as personal tracking devices, flexible individualized phantoms and scanning of geometry set-up. When combined with fast simulation codes, the aim was to perform personal dosimetry in real-time. We applied and validated the methodology for two situations where improvements in dosimetry are urgently needed: neutron workplaces and interventional radiology. Several validation and test measurements were done in different hospitals, and in 2 workplace fields with significant neutron exposure. Personal doses could be calculated within acceptable simulation times, just based on captured movements of the workers and information of the radiation fields. These doses agreed with the results from physical dosemeters within the standard uncertainties that are accepted in personal dosimetry. This PODIUM dosimetry method can also be used to visualize the radiation in near real time. This will increase awareness of radiation protection among workers and will improve the application of the ALARA principle, and it can also be used in training modules. The use of neural networks and big data will help in further reducing simulation time, making real time simulations and dosimetry without physical dosemeters possible in the near future.

Aper project dataset

Empirical study Google Play apps and mappings + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/appbrain_ranking-top32_100.txt?versionId=286db783-c0ce-479f-b183-93653a516030">appbrain_ranking-top32_100.txt </a> + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/selected-google-play-apps.txt?versionId=fa76fec8-4dec-43bd-85d4-525871d9dffa">selected-google-play-apps.txt </a> + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/aper-mapping.7z?versionId=d14f499a-fed6-43d6-9ed1-ad9fd40d8502">aper-mapping.7z </a>   Benchmark and tool comparisons Including all results in RQ4. Also with how we collected the issues. + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/Aper_Performance_Type1.csv?versionId=075e6292-30c4-452a-a8c5-2c424507b097">Aper_Performance_Type1.csv </a> + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/Aper_Performance_Type2.csv?versionId=1c759cf9-3f1f-4cf3-8aaf-4020bc31acf5">Aper_Performance_Type2.csv </a> + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/Lint_Performance_Type1.csv?versionId=ff8fe2b4-6a81-4ab3-816f-803c620ab127">Lint_Performance_Type1.csv </a> + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/Lint_Performance_Type2.csv?versionId=cd054773-71b2-4f1b-811e-0e3b77c11617">Lint_Performance_Type2.csv </a> + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/ARPDroid_Performance_Type1.csv?versionId=8802e9c5-8abd-4483-85b3-ebd839ee9bbf">ARPDroid_Performance_Type1.csv </a> + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/RevDroid_Performance_Type1.csv?versionId=bf173ab8-4cf7-4423-914c-63f4be0f9e2c">RevDroid_Performance_Type1.csv </a> + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/arpfix-benchmark.7z?versionId=748b8b32-983e-487d-8aa7-da9c7f3e4e57">arpfix-benchmark.7z </a> Real bugs and videos + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/214-fd-apps.txt?versionId=7a9f2c75-a567-453f-bbdd-d68ac845d2c2">214-fd-apps.txt </a> + <a href="https://zenodo.org/api/files/40fc2fd2-ef6e-47c8-a859-98c4bc6a5e79/replication-videos.7z?versionId=f1ba35fc-4e48-4e0a-8a89-a48f0fffaddb">replication-videos.7z </a>

DReSA: Project team reflections

This presentation provides thoughts and reflections from the Digital Research Skills Australaisa (DReSA) project team on DReSA. Team members highlight their perspectives on value propositions and benefits for their respective institutiosn/organisations and nationally, as well as individual reflections on collaboration and working together on the project so far. You can watch the video on YouTube here: https://youtu.be/qqH92itI8SI

Data for Development of LOS Analysis Procedures and Performance Measurement Systems for Parking (UTEP Year 4 Project)

These Excel files consists of the data in the tables and figures in the final report. They appear in Chapters 3 to 6 in the final report. Each file is for one chapter. Each table or figure is listed as an Excel worksheet. The name of the worksheet is the figure or table number in the final report.

Variables of importance in the projection (VIP) list with values > 1 from OPLS-DA analysis of Citrus samples inoculated with Xanthomonas citri subs. citri

Features filtered containing Variables of importance in the projection (VIP) values > 1 from OPLS-DA analysis (SIMCA) of Citrus sinensis samples inoculated with Xanthomonas citri subs. citri after 24 and 48 hours post inoculation.

Data for CS447 Final Project

Datasets for our CS447 final project's sequence classification experiment. The .fa.gz files are the genomes we used; .fastq.gz files are the reads we used.

Fig. 9 in The "Spaghetti Project": the final identification guide to European Terebellidae (sensu lato) (Annelida, Terebelliformia)

Fig. 9. Type localities of European species for which a neotype is required.

opencc-zeroDec 2021View details →

dryad36/100

Fourier projection algorithm for calculating phonon dispersion relations of crystals

We propose a Fourier projection algorithm and demonstrate its superiority for calculation of the phonon dispersion relations of 2D and 3D crystals based on the atomic coordinate trajectories from supercell molecular dynamics/first-principle molecular dynamics simulations. The lattice vibration states are described in a six-dimensional phase space composed of lattice and wave vectors. Phonon dispersion information in the first Brillouin zone sampled with a k-mesh grid of N×N×N is generated by Fourier projection of the lattice vibration frequencies in the supercell by N×N×N primitive cells, the dispersion relations along high-symmetry paths are retrieved therefrom. Our algorithm is physically intuitive, computationally convenient, and universally applicable. As examples of application, the successful dispersion relation calculations of 2D graphene, 3D BCC-Fe and FCC-Cu are presented. This algorithm shows the correspondence between real and reciprocal lattices, that is, the spatial information in the states of the lattice vibration were preserved. The high-symmetry paths of the first Brillouin zone in the reciprocal lattice primitive cells of the hexagonal, BCC, and FCC lattice were also given in detail. In combination with first-principles molecular dynamics, as long as the interatomic force is accurately known, reliable and accurate dispersion relation can be achieved by this Fourier projection algorithm.

An Empirical Study on the Usage and Availability of Machine Learning Libraries in Open-Source Python Projects - Dataset

This repository contains the dataset of the manuscript: "An Empirical Study on the Usage and Availability of Machine Learning Libraries in Open-Source Python Projects"

Data for Mh landscape project

Tree ring width, tree ring oxygen and carbon isotopes measurements, climate data for 1900-2015 for seven sugar maple stands (Ontario, CA).