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# WikiCSSH If you are using WikiCSSH please cite the following: > Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020. “WikiCSSH: Extracting Computer Science Subject Headings from Wikipedia.” In Workshop on Scientific Knowledge Graphs (SKG 2020). https://skg.kmi.open.ac.uk/SKG2020/papers/HAN_et_al_SKG_2020.pdf > Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020. "WikiCSSH - Computer Science Subject Headings from Wikipedia". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0424970_V1 Download the WikiCSSH files from: https://doi.org/10.13012/B2IDB-0424970_V1 More details about the WikiCSSH project can be found at: https://github.com/uiuc-ischool-scanr/WikiCSSH This folder contains the following files: WikiCSSH_categories.csv - Categories in WikiCSSH WikiCSSH_category_links.csv - Links between categories in WikiCSSH Wikicssh_core_categories.csv - Core categories as mentioned in the paper WikiCSSH_category_links_all.csv - Links between categories in WikiCSSH (includes a dummy category called <ROOT> which is parent of isolates and top level categories) WikiCSSH_category2page.csv - Links between Wikipedia pages and Wikipedia Categories in WikiCSSH WikiCSSH_page2redirect.csv - Links between Wikipedia pages and Wikipedia page redirects in WikiCSSH This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a> or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Raw arthroscopic scores, histologic scores, cytokine measurements, and performance data for the study cohort described in the accompanying publication.

This dataset was extracted from a set of metadata files harvested from the DataCite metadata store (https://search.datacite.org/ui) during December 2015. Metadata records for items with a resourceType of dataset were collected. 1,647,949 total records were collected. This dataset contains three files: 1) readme.txt: A readme file. 2) version-results.csv: A CSV file containing three columns: DOI, DOI prefix, and version text contents 3) version-counts.csv: A CSV file containing counts for unique version text content values.

Diversity - PubMed dataset Contact: Apratim Mishra (Oct, 2024) This dataset presents article-level (pmid) and author-level (auid) diversity data for PubMed articles. The chosen selection includes articles retrieved from Authority 2018 [1], 907 024 papers, and 1 316 838 authors, and is an expanded dataset of V1. The sample of articles consists of the top 40 journals in the dataset, limited to 2-12 authors published between 1991 – 2014, which are article type "journal type" written in English. Files are 'gzip' compressed and separated by tab space, and V3 includes the correct author count for the included papers (pmids) and updated results with no NaNs. ################################################ File1: auids_plos_3.csv.gz (Important columns defined, 5 in total) • AUID: a unique ID for each author • Genni: gender prediction • Ethnea: ethnicity prediction ################################################# File2: pmids_plos_3.csv.gz (Important columns defined) • pmid: unique paper • auid: all unique auids (author-name unique identification) • year: Year of paper publication • no_authors: Author count • journal: Journal name • years: first year of publication for every author • Country-temporal: Country of affiliation for every author • h_index: Journal h-index • TimeNovelty: Paper Time novelty [2] • nih_funded: Binary variable indicating funding for any author • prior_cit_mean: Mean of all authors’ prior citation rate • Insti_impact: All unique institutions’ citation rate • mesh_vals: Top MeSH values for every author of that paper • relative_citation_ratio: RCR The ‘Readme’ includes a description for all columns. [1] Torvik, Vetle; Smalheiser, Neil (2021): Author-ity 2018 - PubMed author name disambiguated dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2273402_V1 [2] Mishra, Shubhanshu; Torvik, Vetle I. (2018): Conceptual novelty scores for PubMed articles. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5060298_V1

This dataset includes image stacks, annotated counts, and ground-truth masks from two high-resolution sediment cores extracted from Laguna Pallcacocha, in El Cajas National Park, Ecuadorian Andes by Moy et al. (2002) and Hagemans et al. (2021). The first core (PAL 1999, from Moy et al. (2002)) extends through the Holocene (11,600 cal. yr. BP - present). There are a total of 900 annotated image stacks and masks in the PAL 1999 domain. The second core (PAL IV, from Hagemans et al. (2021)) captures the 20th century. There are 2986 annotated image stacks and masks in the PAL IV domain. Different microscopes and annotations tools were used to image and annotate each core and there are corresponding differences in naming conventions and file formats. Thus, we organized our data separately for the PAL 1999 and the PAL IV domains. The three letter codes used to label our pollen annotations are in the file: “Pollen_Identification_Codes.xlsx”. Both domain directories contain: • Image stacks organized by subdirectory • Annotations within each image stack directory, containing specimen identifications using a three letter code and coordinates defining bounding boxes or circles • Ground-truth distance-transform masks for each image stack The zip file "bestValModel_encoder.paramOnly.zip" is the trained pollen detection model produced from the images and annotations in this dataset. Please cite this dataset as: Feng, Jennifer T.; van den Berg, Thya; Donders, Timme H.; Kong, Shu; Puthanveetil Satheesan, Sandeep; Punyasena, Surangi W. (2025): Slide scans, annotated pollen counts, and trained pollen detection models for fossil pollen samples from Laguna Pallcacocha, El Cajas National Park, Ecuador . University of Illinois Urbana-Champaign. https://doi.org/10.13012/B2IDB-4207757_V1 Please also include citations of the original publications from which these data are taken: Feng, Jennifer T., Sandeep Puthanveetil Satheesan, Shu Kong, Timme H. Donders, and Surangi W. Punyasena. “Addressing the ‘Open World’: Detecting and Segmenting Pollen on Palynological Slides with Deep Learning.” bioRxiv, January 1, 2025. https://doi.org/10.1101/2025.01.05.631390. Feng, Jennifer T., Sandeep Puthanveetil Satheesan, Shu Kong, Timme H. Donders, and Surangi W. Punyasena. “Addressing the ‘Open World’: Detecting and Segmenting Pollen on Palynological Slides with Deep Learning.” Paleobiology, 2025 [in press]. Feng, J. T. (2023). Open-world deep learning applied to pollen detection (MS thesis, University of Illinois at Urbana-Champaign). https://hdl.handle.net/2142/120168

Raw data associated with PMID: 38925247

This is a research data for a manuscript - A Framework of Simulating Structural Sediment Perimeter Barriers using VFSMOD.

Objectives: This study follows-up on previous work that began examining data deposited in an institutional repository. The work here extends the earlier study by answering the following lines of research questions: (1) what is the file composition of datasets ingested into the University of Illinois at Urbana-Champaign campus repository? Are datasets more likely to be single file or multiple file items? (2) what is the usage data associated with these datasets? Which items are most popular? Methods: The dataset records collected in this study were identified by filtering item types categorized as "data" or "dataset" using the advanced search function in IDEALS. Returned search results were collected in an Excel spreadsheet to include data such as the Handle identifier, date ingested, file formats, composition code, and the download count from the item's statistics report. The Handle identifier represents the dataset record's persistent identifier. Composition represents codes that categorize items as single or multiple file deposits. Date available represents the date the dataset record was published in the campus repository. Download statistics were collected via a website link for each dataset record and indicates the number of times the dataset record has been downloaded. Once the data was collected, it was used to evaluate datasets deposited into IDEALS. Results: A total of 522 datasets were identified for analysis covering the period between January 2007 and August 2016. This study revealed two influxes occurring during the period of 2008-2009 and in 2014. During the first time frame a large number of PDFs were deposited by the Illinois Department of Agriculture. Whereas, Microsoft Excel files were deposited in 2014 by the Rare Books and Manuscript Library. Single file datasets clearly dominate the deposits in the campus repository. The total download count for all datasets was 139,663 and the average downloads per month per file across all datasets averaged 3.2. Conclusion: Academic librarians, repository managers, and research data services staff can use the results presented here to anticipate the nature of research data that may be deposited within institutional repositories. With increased awareness, content recruitment, and improvements, IRs can provide a viable cyberinfrastructure for researchers to deposit data, but much can be learned from the data already deposited. Awareness of trends can help librarians facilitate discussions with researchers about research data deposits as well as better tailor their services to address short-term and long-term research needs.

This dataset contain information for the paper "Changes in neuropeptide prohormone genes among Cetartio-dactyla livestock and wild species associated with evolution and domestication" Veterinary Sciences, MDPI. Protein sequences were predicted using GeneWise for 98 neuropeptide prohormone genes from publicly available genomes of 118 Cetartiodactyla species. All predictions (CetartiodactylaSequences2022.zip) were manually verified. Sequences were aligned within each prohormone using MAFFT (MDPImultalign2022.zip includes multiple sequence alignment of all species available for each prohormone). Phylogenetic gene trees were constructed using PhyML and the species tree was constructed using ASTRAL (MDPItree2022.zip). The data is released under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.

Table S2. Raw pollen counts and climatic data for each seasonal sampling period. Climatic data reflects the average daily conditions observed over the duration samples were collected (˚C/day, mm/day, MJ/m2/day). Lycopodium counts and counts for each pollen taxon reflect the aggregated pollen sum from four sampling heights.

This page contains the data for the publication "Regulation of growth and cell fate during tissue regeneration by the two SWI/SNF chromatin-remodeling complexes of Drosophila" published in Genetics, 2020

Video recorded by Louise Barker using a Cannon Powershot camera documents late-season combat behavior in Agkistrodon contortrix. Recorded in Beaufort County, North Carolina, 11.1 km SE of downtown Washington on 21 October 2020.

List of Chinese Students Receiving a Ph.D. in Chemistry between 1905 and 1964. Based on two books compiling doctoral dissertations by Chinese students in the United States. Includes disciplines; university; advisor; year degree awarded, birth and/or death date, dissertation title. Accompanies Chapter 5 : History of the Modern Chemistry Doctoral Program in Mainland China by Vera V. Mainz published in "Igniting the Chemical Ring of Fire : Historical Evolution of the Chemical Communities in the Countries of the Pacific Rim", Seth Rasmussen, Editor. Published by World Scientific. Expected publication 2017.

Table S3. Mean slope response for each predictive model used in the ecoinformatic analysis. Mean responses are provided for each seasonal and annual pollen data set analyzed from BCI and PNSL and are summarized by life form. Calculated p-values are provided for each model.

This file contains the supplemental appendix for the article "Farmer Preferences for Agricultural Soil Carbon Sequestration Schemes" published in Applied Economic Policy and Perspectives (accepted 2017).

Prepared by Vetle Torvik 2018-04-15 The dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed. &bull; How was the dataset created? First and last names of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including Ethnea+Genni as described in: <i>Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA. http://hdl.handle.net/2142/88927</i> <i>Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720</i> EthnicSeer: http://singularity.ist.psu.edu/ethnicity <i>Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada</i> SexMachine 0.1.1: <a href="https://pypi.python.org/pypi/SexMachine/">https://pypi.org/project/SexMachine</a> First names, for some Author-ity records lacking them, were harvested from outside bibliographic databases. &bull; The code and back-end data is periodically updated and made available for query at <a href ="http://abel.ischool.illinois.edu">Torvik Research Group</a> &bull; What is the format of the dataset? The dataset contains 9,300,182 rows and 10 columns 1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition) 2. name: full name used as input to EthnicSeer) 3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX 4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction 5. lastname: used as input for Ethnea+Genni 6. firstname: used as input for Ethnea+Genni 7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short) 8. Genni: predicted gender; 'F', 'M', or '-' 9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male) 10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'

Spreadsheet with data about whether or not the indicated institutional repository website provides metadata documentation. See readme file for more information.

The dataset consists of results and various input data that are used in the GAMS model for the publication "Repeal of the Clean Power Plan: Social Cost and Distributional Implications". All the data are either excel files or in the .inc format which can be read within GAMS or Notepad. Main data sources include: agriculture, transportation and electricity data. Model details can be found in the paper and the GAMS model package.

This dataset contains MRI data and Imaris modeling analysis of CLARITY-cleared, immunostained tissue associated with a study that assessed the effects of lipid blends containing various levels of a hydrolyzed fat system on myelin development in healthy neonatal piglets. Data are from thirty-two piglets of mixed sexes across four diet treatment groups and includes a sow-fed reference group. MRI data (presented in Figure 2 of the associated article) consists of volumetric data from Voxel-Based Morphometry analysis in brain grey matter and white matter, as well as mean fractional anisotropy and mean orientation dispersion index data from Tract-Based Spatial Statistics analysis. Imaris data (presented in Figure 3 of the associated article) consists of twenty-one select output measures from 3D modeling analysis of PLP-stained prefrontal cortex tissue. All methods used for collection/generation/processing of data are described in the associated article: Louie AY, Rund LA, Komiyama-Kasai KA, Weisenberger KE, Stanke KL, Larsen RJ, Leyshon BJ, Kuchan MJ, Das T, Steelman AJ. A hydrolyzed lipid blend diet promotes myelination in neonatal piglets in a region and concentration-dependent manner. J Neurosci Res. 2023.

Sugarcane, a tropical C4 grass in the genus Saccharum (Poaceae), accounts for nearly 80% of sugar produced worldwide and is also an important feedstock for biofuel production. Generating transgenic sugarcane with predictable and stable transgene expression is critical for crop improvement. In this study, we generated a highly expressed single copy locus as landing pad for transgene stacking. Transgenic sugarcane lines with stable integration of a single copy nptII expression cassette flanked by insulators supported higher transgene expression along with reduced line to line variation when compared to single copy events without insulators by NPTII ELISA analysis. Subsequently, the nptII selectable marker gene was efficiently excised from the sugarcane genome by the FLPe/FRT site-specific recombination system to create selectable marker free plants. This study provides valuable resources for future gene stacking using site-specific recombination or genome editing tools.

This dataset enumerates the number of geocoded tweets captured in geographic rectangular bounding boxes around the metropolitan statistical areas (MSAs) defined for 49 American cities, during a four-week period in 2012 (between April and June), through the Twitter Streaming API. More information on MSA definitions: https://www.census.gov/population/metro/

Datasets used in the study, "Optimal completion of incomplete gene trees in polynomial time using OCTAL," presented at WABI 2017.

Raw MD simulation trajectory, input and configuration files, SEM current data, and experimental raw data accompanying the publication, "Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore". README.md contains a description of all associated files.