Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

On-the-Fly Syntax Highlighting Using Neural Networks - Replication Package (Data)

This dataset includes the data to replicate the study for the paper On-the-Fly Syntax Highlighting Using Neural Networks. It can be reused for future research in the field. We also include the detailed results obtained by executing our approach. HLNN-Resources.zip includes the input data already formatted to be directly used with the shared source code. The paper is published in the proceeding of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE).

opencc-by-4.0Feb 2019View details →

Replication data for: Reconciliation k-median: Clustering with non-polarized representatives

# Description These files contain the data employed in the experiments described in Bruno Ordozgoiti and Aristides Gionis. 2019. Reconciliation k-median: Clustering with Non-Polarized Representatives. In Proceedings of the 2019 World Wide Web Conference (WWW’19), May 13–17, 2019, San Francisco, CA, USA. Twitter ID's have been anonymized. # Contents domain_mentions.txt: Each line contains a domain name, a user ID and the number of times this user has mentioned this domain name in a tweet. format: domain_name <TAB> user_id <TAB> mention_count domains_ideology_score.txt: Domain names and their ideology score, estimated as described in (Lahoti et al. WSDM 2018). Note: missing scores can be retrieved from supplementary data in https://doi.org/10.1093/poq/nfw006 format: domain_name <TAB> ideology_score follow_graph.txt: The Twitter follower graph. Each line contains a user id and the user id of one of its followers. format: user_id <TAB> follower_user_id representatives.txt: US Congress representatives, each with Twitter handle and polarity score computed using Barbera's method (Barbera, 2015). format: rep_name <TAB> website_url <TAB> district <TAB> twitter_handle <TAB> party <TAB> barbera_polarity_score user_polarity.txt: User ID's and polarity score computed using Barbera's method (Barbera, 2015). format: user_id <TAB> barbera_polarity_score

opencc-zeroApr 2019View details →

Replication Data for: Replication for: How Much Do Startups Impact Employment Growth in the U.S.?

These are data files to support the replication of the blog post "How Much Do Startups Impact Employment Growth in the U.S.?" The replication is not a complete replication attempt. Files were downloaded from the U.S. Census Bureau at https://www.census.gov/ces/dataproducts/bds/data_firm.html on 2019-04-23. A codebook, as provided by the U.S. Census Bureau on the same date, is provided.

opencc-by-4.0Jun 2021View details →

Replication Data for: "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis"

This data package contains all the data relevant to reproduce the results presented in the publication "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis".

opencc-by-4.0Feb 2024View details →

# Replication code and data for: Tracking green space along streets of world cities

# Replication code and data for: Tracking green space along streets of world cities Falchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4  The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains output data, reporting sampling-point level data on the yearly (2016-2023) values of the  Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units.    ____ To replicate the analysis, the results, and the figures of the paper: <ul> <li>Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking</li> <li>*Optional data extraction steps* (processed output data are already available in the Zenodo repository): <ul> <li>Adjust your working directory</li> <li>Run [lines 4-11] of workflow/sourcer.R</li> <li>Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com)  and complete the export to Drive tasks to generate the output .csv files</li> </ul> </li> <li>Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)</li> </ul> <div> <div> <div> </div> <div> <div> <div> </div> <div>     </div> </div> </div> </div> </div> <div> <div> <div> </div> <div> <div> <div> </div> <div>     </div> </div> </div> </div> </div>

opencc-by-4.0Feb 2022View details →

Replication data for: "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?"

The dataset accompanies the scientific article "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?" and can be used to reproduce the findings presented in this article. This dataset consists of two components, namely (i) the corpus data involving the Dutch semi-copular verb "raken" and (ii) an R analysis script to reproduce the computational steps.

opencc-by-4.0Mar 2022View details →

Replication data for: 'A First-Order Statistical Exploration of the Mathematical Limits of Micromagnetic Tomography'

This repository contains the random data generated for obtaining results described in "A first-order statistical exploration of the mathematical limits of Micromagnetic Tomography". All tested parameters are systematically divided over different folders and subfolders. This dataset contains only .npy files, generated with python version 3.8.8 and numpy version 1.21.5. Each file can be opened with numpy.load(filename) The resulting figures are constructed with data of at least 15 iterations; each iteration is stored in a separate folder 'test_' followed by the iteration number. The README file inside provides a detailled overview of the files included.

opencc-by-sa-4.0Dec 2017View details →

Replication Materials for Disclosure Limitation and Confidentality Protection in Linked Data

These are the data and derived figures as used in the chapter by Abowd, Schmutte, and Vilhuber, "Disclosure Limitation and Confidentiality Protection in Linked Data"

opencc-by-4.0Mar 2024View details →

Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study

This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented. For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables.

opencc-by-4.0May 2021View details →

Replication data for "Climate change may induce connectivity loss and mountaintop extinction in Central American forests"

Model code and predictor data underlying the publication "Climate change may induce connectivity loss and mountaintop extinction in Central American forests".

opencc-by-4.0Apr 2023View details →

Replication Data for: "Ocean acidification increases susceptibility to sub-zero air temperatures in ecosystem engineers and limit poleward range shifts"

These datasets contain all the raw data needed to replicate the results from our paper Ocean acidification increases susceptibility to sub-zero air temperatures in ecosystem engineers and limit poleward range shifts published in eLife - <a href="https://doi.org/10.7554/eLife.81080">https://doi.org/10.7554/eLife.81080</a>

opencc-by-4.0Jun 2023View details →

Input data to replicate "The social cost of tropical cyclones"

Input data for the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a> that replicate the results of <a href="https://doi.org/10.1038/s41467-023-43114-4">Krichene et al. 2023</a>. To run the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>, the following files from this repository need to be placed in the <code>./data/input/</code> subdirectory of the project folder containing the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>: <ul> <li><code>GMT.nc</code>: Global mean temperature time series as used by the <a href="https://gitlab.pik-potsdam.de/tovogt/tc_emulator">tropical cyclone emulator</a>.</li> <li><code>GrowthClimateDataset.dta</code>: The <a href="https://purl.stanford.edu/wb587wt4560">input data</a> of <a href="https://dx.doi.org/10.1038/nature15725">Burke et al. 2015</a>.</li> <li><code>IHME_GLOBAL_GDP_ESTIMATES_1950_2015.csv</code>: Historical GDP per capita data from <a href="https://doi.org/10.1186/1478-7954-10-12">James et al. 2012</a> (downloaded from <a href="https://ghdx.healthdata.org/record/ihme-data/gross-domestic-product-gdp-estimates-country-1950-2015">IHME</a>).</li> <li><code>mean_temperature_gswp3-w5e5.csv</code>: Population-weighted average national temperature time series for the historical period.</li> <li><code>pulse_response_ricke_caldeira_2014.csv</code>: The global mean temperature response of an additional emission pulse according to <a href="https://dx.doi.org/10.1088/1748-9326/9/12/124002">Ricke & Caldeira 2014</a>.</li> <li><code>tcdata/TCE-DAT_historic-exposure_1950-2015.csv</code> and <code>tcdata/TotalPopulation.csv</code>: Historical (national) numbers of people affected by tropical cyclones according to <a href="https://doi.org/10.5880/pik.2017.011">TCE-DAT</a> with the corresponding total population counts.</li> <li><code>tcdata/emulator/</code>: Projected (national) shares of people affected by tropical cyclones according to the <a href="https://gitlab.pik-potsdam.de/tc_cost/tc_emulator">tropical cyclone emulator</a> as computed by the scripts in the <a href="https://gitlab.pik-potsdam.de/tc_cost/tc_people_affected">corresponding repository</a>.</li> <li><code>wid_all_data.zip</code>: A bulk data set from the <a href="https://wid.world/bulk_download/wid_all_data.zip">World Inequality Database</a>.</li> </ul> For more information, see <a href="https://doi.org/10.1038/s41467-023-43114-4">Krichene et al. 2023</a> and the README file provided with the <a href="https://dx.doi.org/10.5281/zenodo.8056520">scripts</a>.

opencc-by-4.0May 2020View details →

Benchmark and training data for replicating financial and insurance examples

This dataset contains training, validation and out-of-sample test data for two European calls and two examples of portfolio of variable annuity guarantees.

opencc-by-4.0May 2022View details →

Replication Data & Code - Large-scale land acquisitions exacerbate local land inequalities in Tanzania

<h3>Reference</h3>Sullivan J.A., Samii, C., Brown, D., Moyo, F., Agrawal, A. 2023. Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania. Proceedings of the National Academy of Sciences 120, e2207398120. <a href="https://doi.org/10.1073/pnas.2207398120">https://doi.org/10.1073/pnas.2207398120</a> <h3>Abstract</h3>Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet, rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100 million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (P = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty, and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities.<h3>Replication Data</h3>We include anonymized household survey data from our analysis to support open and reproducible science. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. For the 2018 surveys, several anonymized extracts are provided including an imputed (n=10) dataset to fill in missing data that was used for the main analysis. This data can be found in the hh_data folder and includes:<ul><li>hh_imputed10_2018: anonymized household dataset for 2018 with variables used for the main analysis where missing data was imputed 10 times</li><li>hh_compensation_2018: anonymized household extract for 2018 representing household benefits and compensation directly received from LSLAs</li><li>hh_migration_2018: anonymized household extract for 2018 representing household migration behavior following LSLAs</li><li>hh_rsdata_2018: extracted remote sensing data at the household geo-location for 2018</li><li>hh_land_2019:  anonymized household extract for 2019 of land variables</li></ul>Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in lsms_data folder). We've provide sub-modules from the LSMS dataset relevant to our analysis but the full datasets can be access through the World Bank's Microdata Library (https://microdata.worldbank.org/index.php/home). Across several analyses we use the LSLA boundaries for our four selected sites. We provide a shapefile for the LSLA boundaries in the gis_data folder.Finally, our data replication includes several model outputs (found in mod_outputs), particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our main_analysis.Rmd script. <h3>Replication Code</h3>We provide replication code in the form of R Markdown (.Rmd) or R (.R) files. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article. Scripts include:<ul><li>main_analysis.Rmd: main analysis supporting the finding, graphs, and tables reported in our main manuscript</li><li>compensation.R: analysis of benefits and compensation received directly by households from LSLAs</li><li>landvalue.R: analysis of household land values as a function of distance from LSLAs</li><li>migration.R: analysis of migration behavior following LSLAs</li><li>selection_bias.R: analysis of LSLA selection bias between control and treatment enumeration areas</li></ul>

opencc-by-4.0Dec 2023View details →

Replication Data for Lead-Free Semiconductors, Phase-Evolution and Superior Stability of Multinary Tin Chalcohalides

Tin-based semiconductors are highly desirable materials for energy applications due to their low toxicity and biocompatibility relative to analogous lead-based semiconductors. In particular, tin-based chalcohalides possess optoelectronic properties that are ideal for photovoltaic and photocatalytic applications. In addition, they are believed to benefit from increased stability compared with halide perovskites. However, to fully realize their potential, it is first necessary to better understand and predict the synthesis and phase evolution of these complex materials. Here, we describe a versatile solution-phase method for the preparation of the multinary tin chalcohalide semiconductors Sn2SbS2I3, Sn2BiS2I3, Sn2BiSI5, and Sn2SI2. We demonstrate how certain thiocyanate precursors are selective toward the synthesis of chalcohalides, thus preventing the formation of binary and other lower order impurities rather than the preferred multinary compositions. Critically, we utilized 119Sn ssNMR spectroscopy to further assess the phase purity of these materials. Further, we validate that the tin chalcohalides exhibit excellent water stability under ambient conditions, as well as remarkable resistance to heat over time compared to halide perovskites. Together, this work enables the isolation of lead-free, stable, direct band gap chalcohalide compositions that will help engineer more stable and biocompatible semiconductors and devices.

opencc-by-4.0May 2019View details →

Replication data for "The uncertain future of protected lands and waters" - protected area base layer for Amazonia

We created a database of terrestrial and coastal protected areas (PAs) for all nine Amazonian countries following the IUCN definition for PAs and including only state-designated and state-managed PAs. We used the best available sources of archival data, including original legal documents, to confirm information about PAs. We included PAs that currently exist, as well as those that existed previously but have been degazetted. We note that this database differs from the World Database of Protected Areas (WDPA) for several reasons: • we focus on nationally-designated PAs and omit international or local designations • we include previously protected areas • we exclude other area-based conservation interventions other than state-designated and state-managed PAs (such as indigenous lands, privately protected areas, recreational sites, and community based natural resource management areas) which are included in the WDPA in certain countries • We use the establishment date as provided in each PA’s gazettement legal document, rather than the Status Year field in the WDPA, which lists the year that the PA’s current designation was established (46) • We use the spatial extent as provided in each PA’s gazettement legal document, rather than the spatial extent provided in the WDPA. The spatial extent in the WDPA (Rep_Area) is reported by nations and may represent the area as measured in GIS or paper maps, rather than the legally gazetted area. See Table S16 for detailed information by country describing the sources of PA data used for the nine Amazonian countries.  Citation of original paper: Golden Kroner, R. E., Qin, S., Cook, C. N., Krithivasan, R., Pack, S. M., Bonilla, O. D., Cort-Kansinally, K. A., Coutinho, B., Feng, M., Martínez Garcia, M. I., He, Y., Kennedy, C. J., Lebreton, C., Ledezma, J. C., Lovejoy, T. E., Luther, D. A., Parmanand, Y., Ruíz-Agudelo, C. A., Yerena, E., … Mascia, M. B. (2019). The uncertain future of protected lands and waters. Science, 364(6443), 881–886. <a href="https://doi.org/10.1126/science.aau5525">https://doi.org/10.1126/science.aau5525</a>

opencc-by-4.0May 2022View details →

Replication data for: Energy flow analysis of an industrial ammonia refrigeration system

This dataset includes energy data acquired from a pelagic fish processing plant, including data from an industrial ammonia refrigeration system that provides cooling and freezing. In addition, production data is included. Data from the system was analysed within the KSP project PCM-STORE (308847) supported by the Research Council of Norway and industry partners. PCM-STORE aims at building knowledge on novel PCM technologies for low-temperature thermal energy storage. Collecting and analysing data is an important part of evaluating the potential for reduction of CO2 emissions and increasing energy efficiency. Many processing plants measure and log data, but it is not often published. This dataset includes specific energy demand, peak power demand, power demand for different sections of the plant, ambient temperatures, and production volumes. The data was collected in 2021. The included graphics show the refrigeration system and some resulting tables and graphs. Production follows a seasonal cycle throughout the year, with no (or very low) production in the spring (Mar-May), and peak production in the autumn (Sep-Nov). The cycle is linked to the seasonal availability of fish. Annual SEC numbers (200-247 kWh/tonnes) were found to be in line with other Norwegian pelagic plants. A strong dependency between SEC and volume throughput were also found, where months of low production resulted in high SEC values and vice versa. Knowledge about the processes indicates that a fillet production is more energy intensive compared to round production, due to more energy demand from the fillet sections, higher mass (fish and brine) in each box and higher requirement of hot water for cleaning. This dataset is related to the conference paper "Energy flow analysis of an industrial ammonia refrigeration system and potential for a cold thermal energy storage" presented at the 15th IIR Gustav Lorentzen Conference on Natural Refrigerants, Trondheim, Norway 13-15 June 2022.

opencc-by-4.0May 2022View details →

Data for "Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments"

Flow Cytometry and plate reader data from "High Throughput Experimentation to replicate Yeast Gates Experiment," accompanied with jupyter noteboooks to replicate the analyses.  Sequencing data is <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA784977">available separately</a>. Files named <code>flow_cytometryax</code> should be concatenated: they are individual slices of a gzipped tar file. Concatenate them and then extract with <code>tar xzf filename</code>. The paper is available on <a href="https://biorxiv.org/cgi/content/short/2022.05.31.493627">Biorxiv</a>. The Jupyter notebooks used to analyze this data for the paper are <a href="https://github.com/rpgoldman/replication-paper-data-analysis">available on GitHub</a>.

opencc-by-4.0Oct 2021View details →

Replication Data for: Geometric Transformers for Protein Interface Contact Prediction

This dataset contains replication data for the paper titled "Geometric Transformers for Protein Interface Contact Prediction". The dataset consists of pickled Python dictionaries containing pairs of DGLGraphs that can be used to train and validate protein interface contact prediction models. It also contains our best model checkpoints saved as PyTorch LightningModules. Our GitHub repository, DeepInteract, linked in the "Additional notes" metadata section below provides more details on how we use these files as examples for cross-validation.