cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6394065
title: Dataset of "Atomic-level description of thermal fluctuations in inorganic lead halide perovskites" publication

description: The "Zenodo_22-02-2022.zip" file contains a "files" folder and a jupyter notebook to plot the figures reported in the publication. The "files" folder contains several subfolders with the txt files required to plot the figures. The "XAS_simulations.zip" file contains a README.txt and few subfolders with input and output files needed to reproduce the XAS simulations. The README.txt explains the structure of the archive and the content of each subfolders.
description: {"references": ["Cannelli, Oliviero, et al. \"Atomic-level description of thermal fluctuations in inorganic lead halide perovskites.\" The journal of physical chemistry letters 13.15 (2022): 3382-3391."]}
DOI: 10.5281/zenodo.4928983
title: Dataset of "Atomic-level description of thermal fluctuations in inorganic lead halide perovskites" publication

description: The "Zenodo_22-02-2022.zip" file contains a "files" folder and a jupyter notebook to plot the figures reported in the publication. The "files" folder contains several subfolders with the txt files required to plot the figures. The "XAS_simulations.zip" file contains a README.txt and few subfolders with input and output files needed to reproduce the XAS simulations. The README.txt explains the structure of the archive and the content of each subfolders.
description: {"references": ["Cannelli, Oliviero, et al. \"Atomic-level description of thermal fluctuations in inorganic lead halide perovskites.\" The journal of physical chemistry letters 13.15 (2022): 3382-3391."]}
DOI: 10.5281/zenodo.4564629
title: Dataset of "Quantifying Photoinduced Polaronic Distortions in Inorganic Lead Halide Perovskites Nanocrystals" publication

description: The zip file contains a ReadMe.txt file and several subfolders with: - input raw data from the synchrotron experiments, - the python scripts used in the data analysis, - the output of the python scripts (processed txt files) discussed in the publication related to this data set. - the Figures folder, which contains all the output files and related python scripts to generate analogous figures to those presented in the publication.
DOI: 10.5281/zenodo.4564628
title: Dataset of "Quantifying Photoinduced Polaronic Distortions in Inorganic Lead Halide Perovskites Nanocrystals" publication

description: The zip file contains a ReadMe.txt file and several subfolders with: - input raw data from the synchrotron experiments, - the python scripts used in the data analysis, - the output of the python scripts (processed txt files) discussed in the publication related to this data set. - the Figures folder, which contains all the output files and related python scripts to generate analogous figures to those presented in the publication.
DOI: 10.5281/zenodo.6946369
title: De novo assembly and annotation of parasitic trematode genomes

description: Contained in this release are 19 genome assemblies and annotations of parasitic trematodes, encompassing 13 species. This included representatives of the <em>Schistosoma</em> (<em>n</em> = 13 assemblies), <em>Trichobilharzia</em> (<em>n</em> = 2 assemblies), <em>Heterobilharzia americana</em> (<em>n</em> = 2 assemblies) and <em>Dicrocoelium dendriticum </em>(<em>n </em>= 1 assembly). The <em>Schistosoma curassoni</em> assembly has been released previously (10.5281/zenodo.6594833) but a new annotation is included with the original assembly here. These genomes were assembled from a variety of sources including stored parasites from museum collections, established laboratory strains and wild-caught isolates sampled from natural hosts in endemic regions. Using a combination of DNA sequencing approaches, all genomes were assembled into chromosomal-scale scaffolds. This was followed by genome annotation based on short-read RNA sequencing (RNA-seq) and long-read isoform sequencing (Iso-seq) transcriptomic data. Included here are the primary assemblies (representing a non-redundant haploid genome) for each species (*.primary.fa), alternate loci (alternate representations of loci found in a largely haploid assembly; *.haplotypes.fa) and annotations (*.gff3). Metadata for each assembly can be found in the included spreadsheets (metadata.xlsx). This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see https://www.sanger.ac.uk/about/research-policies/open-access-science/. This repository will be updated with a complete list of collaborators/authors prior to publication. Please contact Duncan Berger (db22@sanger.ac.uk) with questions regarding pre-publication use of this dataset.
DOI: 10.5281/zenodo.6946368
title: De novo assembly and annotation of parasitic trematode genomes

description: Contained in this release are 19 genome assemblies and annotations of parasitic trematodes, encompassing 13 species. This included representatives of the <em>Schistosoma</em> (<em>n</em> = 13 assemblies), <em>Trichobilharzia</em> (<em>n</em> = 2 assemblies), <em>Heterobilharzia americana</em> (<em>n</em> = 2 assemblies) and <em>Dicrocoelium dendriticum </em>(<em>n </em>= 1 assembly). The <em>Schistosoma curassoni</em> assembly has been released previously (10.5281/zenodo.6594833) but a new annotation is included with the original assembly here. These genomes were assembled from a variety of sources including stored parasites from museum collections, established laboratory strains and wild-caught isolates sampled from natural hosts in endemic regions. Using a combination of DNA sequencing approaches, all genomes were assembled into chromosomal-scale scaffolds. This was followed by genome annotation based on short-read RNA sequencing (RNA-seq) and long-read isoform sequencing (Iso-seq) transcriptomic data. Included here are the primary assemblies (representing a non-redundant haploid genome) for each species (*.primary.fa), alternate loci (alternate representations of loci found in a largely haploid assembly; *.haplotypes.fa) and annotations (*.gff3). Metadata for each assembly can be found in the included spreadsheets (metadata.xlsx). This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see https://www.sanger.ac.uk/about/research-policies/open-access-science/. This repository will be updated with a complete list of collaborators/authors prior to publication. Please contact Duncan Berger (db22@sanger.ac.uk) with questions regarding pre-publication use of this dataset.
DOI: 10.6073/pasta/aa5ef46443438cd77da2b110e9c58509
title: Plant succession and biomass dynamics following logging and burning in Watersheds 1 and 3, Andrews Experimental Forest, 1962 to Present

description: Watersheds 1 and 3 in the HJ Andrews Experimental Forest have a long history of hydrologic, geomorphic, and ecological study. Long-term successional studies in the two watersheds are unprecedented in their scope and duration (1962 to present), spanning more than 50 years of post-logging measurements. To date, studies have focused on understory responses to logging and burning, early stand developmental processes (tree growth and mortality), and understory responses to canopy closure. Understory sampling was initiated in 1962, prior to harvest, and includes approximately 190, 2 x 2 m permanent understory plots. Measurements include estimates of ground-surface conditions and abundance (cover and biomass) of herbaceous, shrub, and tree species. After broadcast burning (1963 in WS3, 1966 in WS1) plots were sampled annually through 1972/1973, but less frequently thereafter (every 2-6 years). Studies of early stand development were initiated in 1979/1980, with sample plots collocated with the understory plots. In each of approximately 190, 250 m2 tree plots, all conifer and hardwood stems greater than or equal to 1.4 m tall are tagged, measured for diameter, and assessed for status (live or dead, including the cause of mortality). Both watersheds experienced recent snow-related windthrow (2019, 2020) and parts of WS1 burned in 2020 during the Holiday Farm Fire, adding complexity to the structure and composition of the understory and overstory communities.
DOI: 10.6084/m9.figshare.20416074
title: Sample Two Market Orientation and TOC Original Dataset 110

description: Data from a sample of 110 US manufacturing managers related to market orientation, Theory of Constraints, and performance.
DOI: 10.6084/m9.figshare.20416074.v1
title: Sample Two Market Orientation and TOC Original Dataset 110

description: Data from a sample of 110 US manufacturing managers related to market orientation, Theory of Constraints, and performance.
DOI: 10.6084/m9.figshare.20416086
title: The Impact of COVID-19 on Library Technical Services Units [Data Set]

description: This dataverse contains the raw and cleaned data for Elizabeth Szkirpan's research project into the measurable impact of COVID-19 on United States library technical services units. Files used for data analysis, as well as the Python scripts for data investigation, are all available within this repository.
DOI: 10.6084/m9.figshare.20416086.v1
title: The Impact of COVID-19 on Library Technical Services Units [Data Set]

description: This dataverse contains the raw and cleaned data for Elizabeth Szkirpan's research project into the measurable impact of COVID-19 on United States library technical services units. Files used for data analysis, as well as the Python scripts for data investigation, are all available within this repository.
DOI: 10.6084/m9.figshare.20416092
title: Python Codes for Data Analysis of The Impact of COVID-19 on Technical Services Units Survey Results

description: Copies of Anaconda 3 Jupyter Notebooks and Python script for holistic and clustered analysis of "The Impact of COVID-19 on Technical Services Units" survey results. Data was analyzed holistically using cleaned and standardized survey results and by library type clusters. To streamline data analysis in certain locations, an off-shoot CSV file was created so data could be standardized without compromising the integrity of the parent clean file. Three Jupyter Notebooks/Python scripts are available in relation to this project: COVID_Impact_TechnicalServices_HolisticAnalysis (a holistic analysis of all survey data) and COVID_Impact_TechnicalServices_LibraryTypeAnalysis (a clustered analysis of impact by library type, clustered files available as part of the Dataverse for this project).
DOI: 10.6084/m9.figshare.20416098
title: Data Clustered By Library Type for The Impact of COVID-19 on Technical Services Units Survey Results

description: These datasets, clustered by library type, contain cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. These specific iterations of data reflect the cleaning and standardization conducted on the raw dataset retrieved from survey responses, and then cluster the data into specific library type files. All files reflect the removal of data auto-generated by Qualtrics (such as survey start/stop times), blank rows, survey responses not completed after question four (the first section of survey questions), and non-United States responses. Survey respondents were asked to identify their library type (Academic, Public, K-12 School, Special Collections and/or Archives, Other, and Blank responses). There is some duplication between files, as respondents were allowed to select more than one library type to represent the sometimes complicated governing structure within libraries. Note that these files also contain an additional cleaning steps to standardize numbers within the "How many full and part-time staff members (not student workers) were on your Technical Services team prior to the COVID-19 pandemic?" and "How many full and part-time staff members (not student workers) are on your technical services team now?" questions (Q6 and Q7). String text was removed from these fields, as well as incomplete responses (e.g. Indicating a before number but not an after number).
DOI: 10.6084/m9.figshare.20416098.v1
title: Data Clustered By Library Type for The Impact of COVID-19 on Technical Services Units Survey Results

description: These datasets, clustered by library type, contain cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. These specific iterations of data reflect the cleaning and standardization conducted on the raw dataset retrieved from survey responses, and then cluster the data into specific library type files. All files reflect the removal of data auto-generated by Qualtrics (such as survey start/stop times), blank rows, survey responses not completed after question four (the first section of survey questions), and non-United States responses. Survey respondents were asked to identify their library type (Academic, Public, K-12 School, Special Collections and/or Archives, Other, and Blank responses). There is some duplication between files, as respondents were allowed to select more than one library type to represent the sometimes complicated governing structure within libraries. Note that these files also contain an additional cleaning steps to standardize numbers within the "How many full and part-time staff members (not student workers) were on your Technical Services team prior to the COVID-19 pandemic?" and "How many full and part-time staff members (not student workers) are on your technical services team now?" questions (Q6 and Q7). String text was removed from these fields, as well as incomplete responses (e.g. Indicating a before number but not an after number).
DOI: 10.6084/m9.figshare.20416104
title: Analyzed Data for The Impact of COVID-19 on Technical Services Units Survey Results

description: These datasets contain cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. These specific iterations of data reflect cleaning and standardization so that data can be analyzed using Python. Ultimately, the three files reflect the removal of survey begin/end times, other data auto-recorded by Qualtrics, blank rows, blank responses after question four (the first section of the survey), and non-United States responses. Note that State names for "What state is your library located in?" (Q36) were also standardized beginning in Impact_of_COVID_on_Tech_Services_Clean_3.csv to aid in data analysis. In this step, state abbreviations were spelled out and spelling errors were corrected.
DOI: 10.6084/m9.figshare.20416104.v1
title: Analyzed Data for The Impact of COVID-19 on Technical Services Units Survey Results

description: These datasets contain cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. These specific iterations of data reflect cleaning and standardization so that data can be analyzed using Python. Ultimately, the three files reflect the removal of survey begin/end times, other data auto-recorded by Qualtrics, blank rows, blank responses after question four (the first section of the survey), and non-United States responses. Note that State names for "What state is your library located in?" (Q36) were also standardized beginning in Impact_of_COVID_on_Tech_Services_Clean_3.csv to aid in data analysis. In this step, state abbreviations were spelled out and spelling errors were corrected.
DOI: 10.6084/m9.figshare.20416110
title: Cleaned Data for The Impact of COVID-19 on Technical Services Units Survey Results

description: This dataset contains the cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. The specific iteration of this data reflects the removal of survey begin/end times, as well as other data auto-recorded by Qualtrics. It does not reflect removed blank rows, surveys containing blank data after question four (the first survey section), and non-United States responses.
DOI: 10.6084/m9.figshare.20416113
title: Raw Data for The Impact of COVID-19 on Technical Services Units Survey Results

description: This dataset contains the raw data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. The specific iteration of this data does not reflect the removal of survey begin/end times, or other data auto-recorded by Qualtrics, nor has it removed blank rows or non-United States responses.
DOI: 10.6084/m9.figshare.20416113.v1
title: Raw Data for The Impact of COVID-19 on Technical Services Units Survey Results

description: This dataset contains the raw data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. The specific iteration of this data does not reflect the removal of survey begin/end times, or other data auto-recorded by Qualtrics, nor has it removed blank rows or non-United States responses.
DOI: 10.34740/kaggle/dsv/4017544
title: Tales from the Pizza Guy
DOI: 10.5281/zenodo.6950531
title: Ixodes Scapularis monitoring data compiled from 7 studies

description: This dataset lists 289 blacklegged tick population datasets from 7 studies that record abundance. These datasets were found by inputing keywords <em>Ixodes Scapularis</em> and <em>tick </em>in data repositories including Long Term Ecological Research data portal, National Ecological Observatory Network data portal, Google Datasets, Data Dryad, and Data One. The types of tick data recorded from these studies include density (number per square meter for example), proportion of ticks, count of ticks found on people. The locations of the datasets range from New York, New Jersey, Iowa, Massachusetts, and Connecticut, and range from 9 to 24 years in length. These datasets vary in that some record different life stages, geographic scope (county/town/plot), sampling technique (dragging/surveying), and different study length. The impact of these study factors on study results is analyzed in our research. Funding: RMC is supported by the National Institute of General Medical Sciences of the National Institutes of the Health under Award Number R25GM122672. CAB, JP, and KSW are supported by the Office of Advanced Cyberinfrastructure in the National Science Foundation under Award Number #1838807. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation.
description: {"references": ["Ellison A. 2017. Incidence of Ticks and Tick Bites at Harvard Forest since 2006. Environmental Data Initiative. https://doi.org/10.6073/pasta/71f12a4ffb7658e71a010866d1805a84. Dataset accessed 6/25/2019", "New York State Department of Health Office of Public Health. 2019. Deer Tick Surveillance: Adults (Oct to Dec) excluding Powassan virus: Beginning 2008. https://health.data.ny.gov/Health/Deer-Tick-Surveillance-Nymphs-May-to-Sept-excludin/kibp-u2ip", "New York State Department of Health Office of Public Health. 2019. Access Nymph Deer Tick Collection Data by County (Excluding Powassan Virus). https://health.data.ny.gov/Health/Deer-Tick-Surveillance-Nymphs-May-to-Sept-excludin/kibp-u2ip", "Ostfeld RS, Levi T, Keesing F, Oggenfuss K, Canham CD (2018) Data from: Tick-borne disease risk in a forest food web. Dryad Digital Repository. https://doi.org/10.5061/dryad.d1c8046", "Oliver JD, Bennett SW, Beati L, Bartholomay LC (2017) Range Expansion and Increasing Borrelia burgdorferi Infection of the Tick Ixodes scapularis (Acari: Ixodidae) in Iowa, 1990\u20132013. Journal of Medical Entomology 54(6): 1727-1734. https://doi.org/10.1093/jme/tjx121", "The Connecticut Agricultural Experiment Station. (n.d.). Summaries of tick testing. CT.gov. Retrieved May 12, 2022, from https://portal.ct.gov/CAES/Fact-Sheets/Tick-Summary/Summaries-of-Tick-Testing", "Jordan, R. A., & Egizi, A. (2019). The growing importance of lone star ticks in a Lyme disease endemic county: Passive tick surveillance in Monmouth County, NJ, 2006 - 2016. PloS one, 14(2), e0211778. https://doi.org/10.1371/journal.pone.0211778"]}