cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5291/ill-data.7-03-143
title: Dynamics of Iron Phthalocyanines inside Single Walled Carbon Nanotubes
DOI: 10.6084/m9.figshare.13365650
title: Occurrence of pathogenic viruses in Africanized honey bees in Brazil.seq

description: DWV, ABPV, IAPV and BQCV virus sequences analyzed from <i>Apis mellifera</i> honeybee from apiaries located in Brazil based on sanger analysis
DOI: 10.6084/m9.figshare.13365650.v1
title: Occurrence of pathogenic viruses in Africanized honey bees in Brazil.seq

description: DWV, ABPV, IAPV and BQCV virus sequences analyzed from <i>Apis mellifera</i> honeybee from apiaries located in Brazil based on sanger analysis
DOI: 10.25345/c50r4m
title: MassIVE MSV000086634 - A central role for STAT3 in inflammatory breast cancer and therapeutic resistance
title: Analysis of histone post-translational modifications
DOI: 10.5066/f78p5xkp
title: Historical methyl mercury in San Francisco Bay

description: San Francisco Bay, California is considered a mercury-impaired watershed. Elevated concentrations of mercury are found in water and sediment as well as fish and estuarine birds. Sources of mercury to the watershed since 1845 include sediment-associated mercury from mercury mining, mercury losses from gold amalgamation activities in mines of the Sierra Nevada, aerial deposition of mercury from global and regional emissions to air, and the direct discharge of mercury to Bay waters associated with the urbanization and industrialization of the estuary. We assessed historical trends in mercury bioaccumulation by measuring mercury concentrations in feathers of the endangered California Ridgways rail (formerly California Clapper Rail) using museum specimens. We developed a structural equation model to attribute variation in historical mercury bioaccumulation in rails to sources of mercury, and estimated the toxicological consequences of extreme mercury exposure to rails from known correlations between feather and blood mercury concentrations.
DOI: 10.5066/f7h13050
title: Data for projected impacts of climate, urbanization, water management, and wetland restoration on waterbird habitat in California's Central Valley

description: The Central Valley of California is one of the most important regions for wintering waterbirds in North America despite extensive anthropogenic landscape modification and decline of historical wetlands there. Like many other mediterranean-climate ecosystems across the globe, the Central Valley has been subject to a burgeoning human population and expansion and intensification of agricultural and urban development that have impacted wildlife habitats. Future effects of urban development, changes in water supply management, and precipitation and air temperature related to global climate change on area of waterbird habitat in the Central Valley are uncertain, yet potentially substantial. Therefore, we modeled area of waterbird habitats for 17 climate, urbanization, water supply management, and wetland restoration scenarios for years 2006-2099 using a water resources and scenario modeling framework. Planned wetland restoration largely compensated for adverse effects of climate, urbanization, and water supply management changes on habitat areas through 2065, but fell short thereafter for all except one scenario. Projected habitat reductions due to climate models were more frequent and greater than under the recent historical climate and their magnitude increased through time. After 2065, area of waterbird habitat in all scenarios that included severe warmer, drier climate was projected to be greater than 15 percent less than in the "existing" landscape most years. The greatest reduction in waterbird habitat occurred in scenarios that combined warmer, drier climate and plausible water supply management options affecting priority and delivery of water available for waterbird habitats. This scenario modeling addresses the complexity and uncertainties in the Central Valley landscape, use and management of related water supplies, and climate to inform waterbird habitat conservation and other resource management planning. Results indicate that increased wetland restoration and additional conservation and climate change adaptation strategies may be warranted to maintain habitat adequate to support waterbirds in the Central Valley. These data are used to support the following publication: Matchett EL, Fleskes JP (2017) Projected Impacts of Climate, Urbanization, Water Management, and Wetland Restoration on Waterbird Habitat in California's Central Valley. PLoS ONE 12(1): e0169780. doi:10.1371/journal.pone.0169780
DOI: 10.5066/f7h41ph1
title: Summertime Fog & Coastal Low Cloud Decadal Dataset for North and Central Coastal California for 1999-2009

description: This dataset is made up of three digital files: a raster grid of decadal summertime frequencies, as hours per day, of fog and low cloud cover (FLCC) along the Northern and Central Coastal California; a contour vector file generated from the raster data; and print-ready map of the contour vector file overlaid on a digital elevation hillshade map. The digital values were calculated from an archive of 72,000 geostationary operational environmental satellite observations during June, July, August, and September, 1999-2009. http://onlinelibrary.wiley.com/doi/10.1002/2015EA000119/pdf
DOI: 10.5066/p91v2s8c
title: Local ecological niche models, genotype associations and environmental data for desert tortoises.

description: These data include environmental covariates used to develop species distribution models for Gopherus agassizii and Gopherus morafkai, along with PCA-reduced environmental covariates used to explore local species-environment relationships within a subregion of the ectone between the two species. We also provide the genotype association used to test the mapped clusters of multiscale geographically weighted regression coefficients against models of (i) a geographically-based taxonomic designation these two sister species, and (ii) an environmental ecoregion designation. These data support the following publication: Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. https://doi.org/10.1111/ddi.12927
DOI: 10.5066/p93zftzi
title: The timing of dabbling duckling hatch, nest departure and depredation in Suisun Marsh, California from 2015-2017

description: These data include a row for each duck nest equipped with a small video camera. We present the location of the nest, the timing of the start of hatch, the timing of the end of hatch, and the timing of the departure of the hen and ducklings from the nest. Additionally, this table includes any predators that were observed at the nest depredating eggs prior to hatch and any predators that were observed at the nest between hatch and departure from the nest. These data support the following publication: "Sitting ducklings: Timing of hatch, nest departure, and predation risk for dabbling duck broods" by Sarah H. Peterson, Joshua T. Ackerman, Mark P. Herzog, C. Alex Hartman, Rebecca Croston, Cliff L. Feldheim, and Michael L. Casazza in Ecology and Evolution.
DOI: 10.5066/p94j36gc
title: Demographic data and location information for the endangered Gailum buxifolium from 2005 to 2014 on Santa Cruz and San Miguel Islands, California

description: The sea-cliff bedstraw (Galium buxifolium) data set contains two types of information, formatted as CSVs: 1) Spatial coordinates (WSG 84) of known population occurrences on Santa Cruz and San Miguel Islands, California, as of 2006 (sensitive locality information); 2) Demographic data measured at four populations growing on Santa Cruz Island. Three of the populations were measured in 2005 and 2006, the fourth was measured in 2005, 2006, 2008, 2010, 2011, and 2014. These data support the following publication: McEachern, K., Chess, K.A., Flagg, K., and Niessen, K.G., 2019, Sea-cliff bedstraw (Galium buxifolium) patterns and trends, 2005-14, on Santa Cruz and San Miguel Islands, Channel Islands National Park, California: U.S. Geological Survey Open-File Report 2019-1054, 23 p., https://doi.org/10.3133/ofr20191054.
DOI: 10.5066/p960w8md
title: Additional mapping tools for Great Basin wildfire and conifer management to increase operational resilience: integrating sagebrush ecosystem and sage-grouse response

description: Conservation planning efforts for sagebrush ecosystems of western North America increasingly focus on enhancing operational resilience though decision-support tools that link spatially explicit variation in soil and plant processes to outcomes of biotic and abiotic disturbances spanning large spatial extents. However, failure to consider higher trophic-level fauna (e.g. wildlife) in these tools can hinder efforts to operationalize resilience owing to spatiotemporal lags between slower reorganization of plant and soil processes following disturbance, and faster behavioral and demographic responses of fauna to disturbance. These spatial products provide additional examples for managers of sagebrush ecosystems and greater sage-grouse (Centrocercus urophasianus) populations in the Great Basin to aid with decisions regarding: 1) wildfire prevention, suppression, and management; and 2) removal of encroaching conifers. These products integrate models of ecological resilience mapped to variation in soil moisture and temperature regimes, wildlife risk and recovery processes, and potential ecological traps with measures of sage-grouse habitat selection and abundance. Please refer to Ricca and Coates (2019) and examples within for further details on methodology.
DOI: 10.5066/p96q5d2t
title: Small mammal surveys from northern San Francisco Bay: 1998-2014

description: This datasets summarizes small mammal trapping efforts that USGS San Francisco Bay Estuary Field Station has led, co-led, or supervised, to detect and monitor the endangered salt marsh harvest mouse (Reithrodontomys raviventris) in the northern reaches of San Francisco Bay from 1998-2014. As the salt marsh harvest mouse is listed as endangered under the Endangered Species Act, sensitive location information can be made available upon request by contacting the dataset point of contact. These data support the following publication: Marcot, B.G., Woo, I., Thorne, K.M., Freeman, C.M., and Guntenspergen, G.R., 2020. Habitat of the endangered salt marsh harvest mouse (Reithrodontomys raviventris) in San Francisco Bay. Ecology and Evolution, https://doi.org/10.1002/ece3.5860.
DOI: 10.6084/m9.figshare.4620883.v1
title: Experiment 4AB: RData

description: Number Attraction; Sound Plurals, Markedness
DOI: 10.5066/p97nbutc
title: Tree mortality in blue oak woodlands in Sequoia National Park during the extreme drought

description: These data were collected to quantify oak mortality during the 2014-2017 drought. 30 tenth hectare drought plots were established throughout the blue oak woodland of Sequoia National Park. Information on species, size, and mortality were collected for all standing trees in each plot. In addition, two long-term 2.25 ha plots were established, in which the same data were collected.
DOI: 10.6084/m9.figshare.4620877.v1
title: Experiment 3: RData

description: Number Attraction and Broken Plurals
DOI: 10.6084/m9.figshare.4620874.v1
title: Experiment 2AB: RData

description: Gender Attraction and Markedness
DOI: 10.6084/m9.figshare.4620802.v1
title: Experiment 1: RData

description: Gender Attraction<br>
DOI: 10.5281/zenodo.4390084
title: High-resolution inundation dataset for coastal India and Bangladesh

description: This collection of gridded data layers provides the extent of inundation in May 2020 resulting from the cyclone Amphan in 39 coastal districts in India and Bangladesh. <strong>Input data:</strong> These geospatial data layers are derived from Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) data for pre-Amphan (May 5-18, 2020) and post-Amphan (May 22-30, 2020) periods. We accessed ready-to-use SAR data on Google Earth Engine (GEE). These input data were preprocessed using Ground Range Detected (GRD) border-noise removal, thermal noise removal, radiometric calibration, and terrain correction, to derive backscatter coefficients (σ°) in decibels (dB). We used VH polarisation instead of VV, since the latter is known to be affected by windy conditions as compared to VH. <strong>Methods:</strong> We developed a binary water/non-water classification scheme for the pre- and post-Amphan images using the automated Otsu thresholding approach that finds optimum threshold values based on clusters found in the histograms of pixel values. This analysis resulted in eight images: four each for pre-Amphan and post-Amphan periods (one each for coastal districts of Odisha and West Bengal and two for Bangladesh for each period). The pixels in these images have two values: 0 for non-water and 1 for water. We then used a decision rule to identify areas that changed from ‘non-water’ to ‘water’ after the cyclone. The decision rule generated the ‘inundation layer’ with the permanent water bodies such as river, lakes, oceans and aquaculture masked out. This analysis resulted in four images, each with pixels with a value of 1 for inundated regions. <strong>Data set format:</strong> The spatial resolution of all the derived datasets is 10m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis. <strong>Data set for download:</strong> A. Three data layers for Odisha, India: OD_pre_binary.tif OD_post_binary.tif OD_inundation.tif These data layers cover 10 districts: Baleshwar, Bhadrak, Cuttack, Jagatsinghpur, Jajpur, Kendrapara, Keonjhar, Khordha, Mayurbhanj and Puri. B. Three data layers for West Bengal, India: WB_pre_binary.tif WB_post_binary.tif WB_inundation.tif These data layers cover 9 districts: Barddhaman, East Midnapore, Haora, Hugli, Kolkata, Nadia, North 24 Parganas, South 24 Parganas, and West Midnapore. C. Six data layers for Bangladesh – three each for lower (L) region and upper (U) region. BNG_L_pre_binary.tif BNG_L_post_binary.tif BNG_L_inundation.tif BNG_U_pre_binary.tif BNG_U_post_binary.tif BNG_U_inundation.tif The data layers for the lower region cover 11 districts: Bagerhat, Barguna, Barisal, Bhola, Jhalokati, Khulna, Lakshmipur, Noakhali, Patuakhali, Pirojpur, and Satkhira. The data layers for the upper region cover 9 districts: Chuadanga, Jessore, Jhenaidah, Kushtia, Meherpur, Naogaon, Natore, Pabna, and Rajshahi.
DOI: 10.5281/zenodo.4390083
title: High-resolution inundation dataset for coastal India and Bangladesh

description: This collection of gridded data layers provides the extent of inundation in May 2020 resulting from the cyclone Amphan in 39 coastal districts in India and Bangladesh. <strong>Input data:</strong> These geospatial data layers are derived from Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) data for pre-Amphan (May 5-18, 2020) and post-Amphan (May 22-30, 2020) periods. We accessed ready-to-use SAR data on Google Earth Engine (GEE). These input data were preprocessed using Ground Range Detected (GRD) border-noise removal, thermal noise removal, radiometric calibration, and terrain correction, to derive backscatter coefficients (σ°) in decibels (dB). We used VH polarisation instead of VV, since the latter is known to be affected by windy conditions as compared to VH. <strong>Methods:</strong> We developed a binary water/non-water classification scheme for the pre- and post-Amphan images using the automated Otsu thresholding approach that finds optimum threshold values based on clusters found in the histograms of pixel values. This analysis resulted in eight images: four each for pre-Amphan and post-Amphan periods (one each for coastal districts of Odisha and West Bengal and two for Bangladesh for each period). The pixels in these images have two values: 0 for non-water and 1 for water. We then used a decision rule to identify areas that changed from ‘non-water’ to ‘water’ after the cyclone. The decision rule generated the ‘inundation layer’ with the permanent water bodies such as river, lakes, oceans and aquaculture masked out. This analysis resulted in four images, each with pixels with a value of 1 for inundated regions. <strong>Data set format:</strong> The spatial resolution of all the derived datasets is 10m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis. <strong>Data set for download:</strong> A. Three data layers for Odisha, India: OD_pre_binary.tif OD_post_binary.tif OD_inundation.tif These data layers cover 10 districts: Baleshwar, Bhadrak, Cuttack, Jagatsinghpur, Jajpur, Kendrapara, Keonjhar, Khordha, Mayurbhanj and Puri. B. Three data layers for West Bengal, India: WB_pre_binary.tif WB_post_binary.tif WB_inundation.tif These data layers cover 9 districts: Barddhaman, East Midnapore, Haora, Hugli, Kolkata, Nadia, North 24 Parganas, South 24 Parganas, and West Midnapore. C. Six data layers for Bangladesh – three each for lower (L) region and upper (U) region. BNG_L_pre_binary.tif BNG_L_post_binary.tif BNG_L_inundation.tif BNG_U_pre_binary.tif BNG_U_post_binary.tif BNG_U_inundation.tif The data layers for the lower region cover 11 districts: Bagerhat, Barguna, Barisal, Bhola, Jhalokati, Khulna, Lakshmipur, Noakhali, Patuakhali, Pirojpur, and Satkhira. The data layers for the upper region cover 9 districts: Chuadanga, Jessore, Jhenaidah, Kushtia, Meherpur, Naogaon, Natore, Pabna, and Rajshahi.
DOI: 10.5281/zenodo.4391095
title: PatCit: A Comprehensive Dataset of Patent Citations

description: <em><strong>patCit: A Comprehensive Dataset of Patent Citations</strong></em> [Newsletter, GitHub] Patents are at the crossroads of many innovation nodes: science, industry, products, competition, etc. Such interactions can be identified through citations <em>in a broad sense</em>. It is now common to use front-page patent citations to study some aspects of the innovation system. However, <strong>there is much more buried in the Non Patent Literature (NPL) citations and in the patent text itself</strong>. <strong>patCit extracts and structures these citations.</strong> Want to know more? Read patCit academic presentation or dive into usage and technical guides on patCit documentation website. <strong><em>IN PRACTICE</em></strong> At patCit, we are building a <em>comprehensive</em> dataset of patent citations to help the community explore this <em>terra incognita</em>. patCit has the following features: global coverage front-page and in-text citations all categories of NPL documents <strong><em>Front-page</em></strong> patCit builds on DOCDB, the largest database of Non Patent Literature (NPL) citations. First, we deduplicate this corpus and organize it into 10 categories (bibliographical reference, database, norm & standard, etc). Then, we design and apply category specific information extraction models using spaCy. Eventually, when possible, we enrich the data using external domain specific high quality databases (e.g. Crossref for bibliographical references). <strong><em>In-text</em></strong> patCit builds on Google Patents corpus of USPTO full-text patents. First, we extract patent and bibliographical reference citations. Then, we parse detected in-text citations into a series of category dependent attributes using grobid. Patent citations are matched with a standard publication number using the Google Patents matching API and bibliographical references are matched with a DOI using biblio-glutton. Eventually, when possible, we enrich the data using external domain specific high quality databases (e.g. Crossref for bibliographical references). <strong>FAIR</strong> <strong>Find</strong> - The patCit dataset is available on BigQuery in an interactive environment. For those who have a smattering of SQL, this is the perfect place to explore the data. It can also be downloaded on Zenodo. <strong>Interoperate</strong> - Interoperability is at the core of patCit ambition. We take care to extract unique identifiers whenever it is possible to enable data enrichment for domain specific high quality databases. This includes the DOI, PMID and PMCID for bibliographical references, the Technical Doc Number for standards, the Accession Number for Genetic databases, the publication number for PATSTAT and Claims, etc. See specific table for more details. <strong>Reproduce</strong> - Our gitHub repository is the project factory. You can learn more about data recipes and models on the patCit documentation website.
DOI: 10.5281/zenodo.3710993
title: PatCit: A Comprehensive Dataset of Patent Citations

description: <em><strong>patCit: A Comprehensive Dataset of Patent Citations</strong></em> [Newsletter, GitHub] Patents are at the crossroads of many innovation nodes: science, industry, products, competition, etc. Such interactions can be identified through citations <em>in a broad sense</em>. It is now common to use front-page patent citations to study some aspects of the innovation system. However, <strong>there is much more buried in the Non Patent Literature (NPL) citations and in the patent text itself</strong>. <strong>patCit extracts and structures these citations.</strong> Want to know more? Read patCit academic presentation or dive into usage and technical guides on patCit documentation website. <strong><em>IN PRACTICE</em></strong> At patCit, we are building a <em>comprehensive</em> dataset of patent citations to help the community explore this <em>terra incognita</em>. patCit has the following features: global coverage front-page and in-text citations all categories of NPL documents <strong><em>Front-page</em></strong> patCit builds on DOCDB, the largest database of Non Patent Literature (NPL) citations. First, we deduplicate this corpus and organize it into 10 categories (bibliographical reference, database, norm & standard, etc). Then, we design and apply category specific information extraction models using spaCy. Eventually, when possible, we enrich the data using external domain specific high quality databases (e.g. Crossref for bibliographical references). <strong><em>In-text</em></strong> patCit builds on Google Patents corpus of USPTO full-text patents. First, we extract patent and bibliographical reference citations. Then, we parse detected in-text citations into a series of category dependent attributes using grobid. Patent citations are matched with a standard publication number using the Google Patents matching API and bibliographical references are matched with a DOI using biblio-glutton. Eventually, when possible, we enrich the data using external domain specific high quality databases (e.g. Crossref for bibliographical references). <strong>FAIR</strong> <strong>Find</strong> - The patCit dataset is available on BigQuery in an interactive environment. For those who have a smattering of SQL, this is the perfect place to explore the data. It can also be downloaded on Zenodo. <strong>Interoperate</strong> - Interoperability is at the core of patCit ambition. We take care to extract unique identifiers whenever it is possible to enable data enrichment for domain specific high quality databases. This includes the DOI, PMID and PMCID for bibliographical references, the Technical Doc Number for standards, the Accession Number for Genetic databases, the publication number for PATSTAT and Claims, etc. See specific table for more details. <strong>Reproduce</strong> - Our gitHub repository is the project factory. You can learn more about data recipes and models on the patCit documentation website.