Dataset Search Results

This dataset contains five files. (i) open_citations_jan2024_pub_ids.csv.gz, open_citations_jan2024_iid_el.csv.gz, open_citations_jan2024_el.csv.gz, and open_citation_jan2024_pubs.csv.gz represent a conversion of Open Citations to an edge list using integer ids assigned by us. The integer ids can be mapped to omids, pmids, and dois using the open_citation_jan2024_pubs.csv and open_citations_jan2024_pub_ids.scv files. The network consists of 121,052,490 nodes and 1,962,840,983 edges. Code for generating these data can be found https://github.com/chackoge/ERNIE_Plus/tree/master/OpenCitations. (ii) The fifth file, baseline2024.csv.gz, provides information about the metadata of PubMed papers. A 2024 version of PubMed was downloaded using Entrez and parsed into a table restricted to records that contain a pmid, a doi, and has a title and an abstract. A value of 1 in columns indicates that the information exists in metadata and a zero indicates otherwise. Code for generating this data: https://github.com/illinois-or-research-analytics/pubmed_etl. If you use these data or code in your work, please cite https://doi.org/10.13012/B2IDB-5216575_V1.

This dataset contains the training results (model parameters, outputs), datasets for generalization testing, and 2-D implementation used in the article "Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields." The article will be submitted to Artificial Intelligence for Earth Systems. The datasets are saved as CSV for 1-D time-series data and *netCDF for 2-D time series dataset. The model parameters are saved in every training epoch tested in the study.

Impact assessment is an evolving area of research that aims at measuring and predicting the potential effects of projects or programs. Measuring the impact of scientific research is a vibrant subdomain, closely intertwined with impact assessment. A recurring obstacle pertains to the absence of an efficient framework which can facilitate the analysis of lengthy reports and text labeling. To address this issue, we propose a framework for automatically assessing the impact of scientific research projects by identifying pertinent sections in project reports that indicate the potential impacts. We leverage a mixed-method approach, combining manual annotations with supervised machine learning, to extract these passages from project reports. This is a repository to save datasets and codes related to this project. Please read and cite the following paper if you would like to use the data: Becker M., Han K., Werthmann A., Rezapour R., Lee H., Diesner J., and Witt A. (2024). Detecting Impact Relevant Sections in Scientific Research. The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING). This folder contains the following files: evaluation_20220927.ods: Annotated German passages (Artificial Intelligence, Linguistics, and Music) - training data annotated_data.big_set.corrected.txt: Annotated German passages (Mobility) - training data incl_translation_all.csv: Annotated English passages (Artificial Intelligence, Linguistics, and Music) - training data incl_translation_mobility.csv: Annotated German passages (Mobility) - training data ttparagraph_addmob.txt: German corpus (unannotated passages) model_result_extraction.csv: Extracted impact-relevant passages from the German corpus based on the model we trained rf_model.joblib: The random forest model we trained to extract impact-relevant passages Data processing codes can be found at: https://github.com/khan1792/texttransfer

Data: Variation in pesticide toxicity in the western honey bee (Apis mellifera) associated with consuming phytochemically different monofloral honeys Includes: Identification and quantification of phenolic components of honeys: Raw_data_JOCE.xlsx – sheet: “HoneyPhytochemicals” Effects of honey phytochemicals on acute pesticide toxicity: Raw_data_JOCE.xlsx – sheet: “raw_LD50 Raw_data_JOCE.xlsx – sheet: “raw_LD50_hive_based”

This dataset includes a total of 300 images of 45 extant species of Podocarpus (Podocarpaceae) and nine images of fossil specimens of the morphogenus Podocarpidites. The goal of this dataset is to capture the diversity of morphology within the genus and create an image database for training machine learning models. The images were taken using Airyscan confocal superresolution microscopy at 630x magnification (63x/NA 1.4 oil DIC). The images are in the CZI file format. They can be opened using Zeiss propriety software (Zen, Zen lite) or open microscopy software, such as ImageJ. More information on how to open CZI files can be found here: [https://www.zeiss.com/microscopy/us/products/microscope-software/zen/czi.html#microscope---image-data]

This repository includes scripts and datasets for the paper, "FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models." Note: The results from estimating species trees with ASTRID-multi (included in this repository) are *not* included in the FastMulRFS paper. We estimated species trees with ASTRID-multi in the fall of 2019, but ASTRID-multi had an important bug fix in January 2020. Therefore, the ASTRID-multi species trees in this repository should be ignored.

This dataset contains BEPAM model code and input data to the replicate the results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land." The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output_0213-2020_Complete_daycent-agversion-[rental payment level]%_[biomass price].gms. (BEPAM-CRP model-Sep2020.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.

Datasets that accompany Beilke, Blakey, and O'Keefe 2021 publication (Title: Bats partition activity in space and time in a large, heterogeneous landscape; Journal: Ecology and Evolution).

This dataset contains all the code, notebooks, datasets used in the study conducted for the research publication titled "Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19 Data". Specifically, this package include the artifacts used to conduct spatial-temporal analysis with space time kernel density estimation (STKDE) using COVID-19 data, which should help readers to reproduce some of the analysis and learn about the methods that were conducted in the associated book chapter. ## What’s inside - A quick explanation of the components of the zip file * Multi-scale CyberGIS Analytics for Detecting Spatiotemporal Patterns of COVID-19.ipynb is a jupyter notebook for this project. It contains codes for preprocessing, space time kernel density estimation, postprocessing, and visualization. * data is a folder containing all data needed for the notebook * data/county.txt: US counties information and fip code from Natural Resources Conservation Service. * data/us-counties.txt: County-level COVID-19 data collected from New York Times COVID-19 github repository on August 9th, 2020. * data/covid_death.txt: COVID-19 death information derived after preprocessing step, preparing the input data for STKDE. Each record is if the following format (fips, spatial_x, spatial_y, date, number of death ). * data/stkdefinal.txt: result obtained by conducting STKDE. * wolfram_mathmatica is a folder for 3D visulization code. * wolfram_mathmatica/Visualization.nb: code for visulization of STKDE result via weolfram mathmatica. * img is a folder for figures. * img/above.png: result of 3-D visulization result, above view. * img/side.png: result of 3-D visulization, side view.

Data files and R code to replicate the econometric analysis in the journal article: B Chen, BM Gramig and SD Yun. “Conservation Tillage Mitigates Drought Induced Soybean Yield Losses in the US Corn Belt.” Q Open. https://doi.org/10.1093/qopen/qoab007

This data set contains all the map data used for "Quantifying transportation energy vulnerability and its spatial patterns in the United States". The multiple dimensions (i.e., exposure, sensitivity, adaptive capacity) of transportation energy vulnerability (TEV) at the census tract level in the United States, the changes in TEV with electric vehicles adoption, and the detailed data for Chicago, Los Angeles, and New York are in the dataset.

This dataset includes five files developed using the procedures described in the article 'Developing County-level Data of Nitrogen Fertilizer and Manure Inputs for Corn Production in the United States' and Supplemental Information published in the Journal of Cleaner Production in 2021. Citation: Xia, Yushu, Hoyoung Kwon, and Michelle Wander. "Developing county-level data of nitrogen fertilizer and manure inputs for corn production in the United States." Journal of Cleaner Production 309 (2021): e126957. Brief method: The fertilizer and manure inputs for corn were generated with a top-down approach by assigning county-level total N inputs reported by USGS to different crops using state- and county-level survey data. The corn N needs were estimated using empirical extension-based equations coupled with soil and environmental covariates. The estimates of fertilizer N inputs were further refined for corn grain and silage production at the county level and gap-filling (using state-level averages) was carried out to generate final files for U.S. county-level N inputs. The dataset is provided in an alternative format in Google Earth Engine: https://code.earthengine.google.com/13a0078e7ee727bc001e045ad0e8c6fc

12CO and 13CO emission maps of the 30 Doradus molecular cloud in the Large Magellanic Cloud, obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) during Cycle 7. See the associated article in the Astrophysical Journal, and README file, for details. Please cite the article if you use these data.

Survey questions and data collected from Illinois land managers on practices and knowledge relating to impacts to wildlife. 0s indicated non-selection, 1s indicate selection of answer.

The data provided in this submission are the gene annotations for the Illinois EBP pilot project samples, as well as the predicted proteins for each sample in FASTA format.

This repository contains the training dataset associated with the 2023 Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics (DGM-Image Challenge), hosted by the American Association of Physicists in Medicine. This dataset contains more than 100,000 8-bit images of size 512x512. These images emulate coronal slices from anthropomorphic breast phantoms adapted from the VICTRE toolchain [1], with assigned X-ray attenuation coefficients relevant for breast computed tomography. Also included are the labels indicating the breast type. The challenge has now concluded. More information about the challenge can be found here: <a href="https://www.aapm.org/GrandChallenge/DGM-Image/">https://www.aapm.org/GrandChallenge/DGM-Image/</a>. * New in V3: we added a CSV file containing the image breast type labels and example images (PNG).

This lexicon is the expanded/enhanced version of the Moral Foundation Dictionary created by Graham and colleagues (Graham et al., 2013). Our Enhanced Morality Lexicon (EML) contains a list of 4,636 morality related words. This lexicon was used in the following paper - please cite this paper if you use this resource in your work. Rezapour, R., Shah, S., & Diesner, J. (2019). Enhancing the measurement of social effects by capturing morality. Proceedings of the 10th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA). Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Minneapolis, MN. In addition, please consider citing the original MFD paper: <a href="https://doi.org/10.1016/B978-0-12-407236-7.00002-4">Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S. P., & Ditto, P. H. (2013). Moral foundations theory: The pragmatic validity of moral pluralism. In Advances in experimental social psychology (Vol. 47, pp. 55-130)</a>.

Photographs and video of two Lesser Chameleons (Furcifer minor) nesting together at the same time near Itremo, Madagascar.

This dataset builds on an existing dataset which captures artists’ demographics who are represented by top tier galleries in the 2016–2017 New York art season (Case-Leal, 2017, https://web.archive.org/web/20170617002654/http://www.havenforthedispossessed.org/) with a census of reviews and catalogs about those exhibitions to assess proportionality of media coverage across race and gender. The readme file explains variables, collection, relationship between the datasets, and an example of how the Case-Leal dataset was transformed. The ArticleDataset.csv provides all articles with citation information as well as artist, artistic identity characteristic, and gallery. The ExhibitionCatalog.csv provides exhibition catalog citation information for each identified artist.

Read me file for the data repository ******************************************************************************* This repository has raw data for the publication "Enhancing Carrier Mobility In Monolayer MoS2 Transistors With Process Induced Strain". We arrange the data following the figure in which it first appeared. For all electrical transfer measurement, we provide the up-sweep and down-sweep data, with voltage units in V and conductance unit in S. All Raman modes have unit of cm^-1. ******************************************************************************* How to use this dataset All data in this dataset is stored in binary Numpy array format as .npy file. To read a .npy file: use the Numpy module of the python language, and use np.load() command. Example: suppose the filename is example_data.npy. To load it into a python program, open a Jupyter notebook, or in the python program, run: import numpy as np data = np.load("example_data.npy") Then the example file is stored in the data object. *******************************************************************************

Photographs and video of the snake Compsophis infralineatus predating upon the chameleons Calumma crypticum and Calumma gastrotaenia near Mandraka, Madagascar.

These data were used to make a predictive model of when ornate box turtles (Terrapene ornata) are likely to be above ground and at risk from fire. The data were generated using shell temperatures, soil temperatures at 0.35 m deep from known overwintering sites, and the spring and fall soil temperature inversion dates during 2019–2022 to infer if 26 individual radio-tracked turtles were above or below ground at three sites in Illinois.

This data set includes the cochlear implant (CI) electrodograms recorded in 2 different acoustic conditions using acoustic head KEMAR. It is a part of a study intended to explore the effect of interaural asymmetry on interaural coherence after CI processing.

This dataset contains: field study design parameters, plant performance metrics, and nitrogen cycling rates associated with a field experiment that compared nitrification rates between maize lines with and without nitrification inhibition loci nitrogen fixation rates with with and without a nitrogen fixing inoculant product. The overarching goal was to evaluate nitrogen fixation by a diazotroph inoculant and retention of nitrogen in the rhizosphere via a novel nitrification inhibition phenotype of maize.

These data are the result of analyses of the metagenome of North American bats, including 18s and 16s barcode genes designed to target microorganisms of the gut. These files are Phyloseq import files created by the DADA2 program. Each barcode gene is uploaded separately as the four files required to build a phyloseq object. For each barcode gene, the files include amplicon sequence variant (ASV) sequences, sequence tables (seqtab) which connect individual samples to the ASVs, tax tables (taxtab) which identify the taxa present as determined by a Bayesian RDP classifier, and rooted phylogenetic trees for the ASVs. Additionally, we have included a "sample_data" file which is necessary for sorting of samples across all four sequence analysis data sets by study and species. Some sample information which could identify the location of endangered species has been restricted. Multiple studies are represented in the data which can be accessed using standard methods in the Phyloseq program (e.g. For a study of bats, parasites, and gut microbiome dysregulation by Bennett, Suski, and OKeefe 2024 [in prep March 2024], study specific data can be accessed using the Study variable "DYSBIOMICS." File names include reference to the primer set used to generate them (18s primer sets: G3, G4, G6; 16s primer set: 341F3_806R5).