cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5t43j95j
title: MassIVE Reanalysis RMSV000000355.5416 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, FBXO10 [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5t43j961
title: MassIVE Reanalysis RMSV000000355.5625 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, SIN3A [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5063/f1nc5zn4
title: Meta-Analysis of Ecosystem Services in the Coastal Zone of Louisiana

description: This dataset includes a meta-analysis of the ecosystem services provided in the coastal environments of Louisiana. This data comes from four sources, Blue Value, NOEP, Web of Science, and referenced in other literature. This data is presented as an excel file with all the ecosystem services, including disturbance regulation, fisheries, primary production, culture, TEV, recreation, habitat, waste regulation, gas regulation, and nutrient regulation. The ecosystems include salinity zones, from freshwater, brackish, and saltwater. Additionally, values for oyster reefs and barrier islands are included. The purpose of this dataset is to more effectively value the ecosystem services provided by the wetlands in coastal Louisiana. Data was collected from 29 studies and authors calculated the mean +/- 1 standard deviation of the service valuations.There are two datasets in this package, one is the coastal system meta-analysis data, and the other is the study-specific dataset of the studies used to conduct the mean calculations. In this dataset, the coastal ecosystem is more specific than fresh, brackish, and salt. Units for both datasets include stock and flow values. Stock values have units of per household, per person, per acre. Flow values have units of per acre/year, per household/year, per boat/year, total acres/year, per person/trip, and per user/year.
DOI: 10.25345/c5t43j97h
title: MassIVE Reanalysis RMSV000000355.6011 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, ICOS [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5063/f1ng4p39
title: A list of reported prey items associated with federally list species in Central California, USA

description: The primary objective of this research was to compile scientific evidence for prey items that support endangered and listed animal species within the Central Drylands of California. There are a total of 305 listed species for California, and 48 are animal species. Better understanding on the reliance of these listed species not just on habitat and refuge functions that regions provide but on prey availability is critical.Within Central California, a total 16 listed vertebrate species that all rely on some extent to invertebrate prey items were compiled. A total of 635 unique prey items associated with the diet of these listed species were retrieved using a formal systematic review. These data have significant ecological and conservation value in deepening our understanding of key factors that support persistence locally within this region of California.
DOI: 10.25345/c5t43j980
title: MassIVE Reanalysis RMSV000000355.6399 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, ACSM3 [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5t43j99g
title: MassIVE Reanalysis RMSV000000355.6786 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, MESDC1 [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5063/f1np22wg
title: The effect of shrubs on animals in Panoche Southern California

description: The following data was collected in Panoche hills in Southern California during the summer of 2019. 7 cameras were deployed facing shrub and open microsites and were rotated once. The cameras remained deployed for a total of 14 days.
DOI: 10.25345/c5t43jb0k
title: MassIVE Reanalysis RMSV000000355.6994 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, MYPOP [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5t43jb12
title: MassIVE Reanalysis RMSV000000355.7178 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, DLGAP2 [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5063/f1ns0sb8
title: Estimating Mountain Lion Habitat Connectivity to Guide Wildlife Conservation at The Nature Conservancy’s Jack and Laura Dangermond Preserve; University of California Santa Barbara; 2021-2022.

description: This submission is from a master's group thesis project at The Bren School of Environmental Science & Management at the University of California, Santa Barbara, and contains the final written report and associated datasets. The graduate student researchers who completed this project include: Meghan Fletcher, Alyssa Kibbe, Grace Kumaishi, Anna Talken, and Nikole Vannest.The California landscape has been fragmented by urban development, infrastructure, and agriculture. Maintaining connectivity between areas of wildlife habitat is important for the viability of many long-ranging species, such as the mountain lion (Puma concolor). Mountain lion populations are highly susceptible to habitat fragmentation, and face reduced access to resources and decreased genetic diversity. This study explores the habitat connectivity between the Jack and Laura Dangermond Preserve (JLDP), a 24,460 acre protected property owned by The Nature Conservancy (TNC), and neighboring protected areas to identify potential pathways of movement for mountain lions along the Central and Southern California coast. In this project, we: 1) determine regional connectivity and least cost paths between core habitats by modeling suitable mountain lion habitat, 2) estimate mountain lion habitat use and movement on JLDP by performing a site-level suitability and corridor analysis and 3) create a short film focused on highlighting our research, the role that JLDP plays in conservation, and the importance of habitat connectivity. The results of our project show that JLDP contains suitable habitat for mountain lions and may play a positive role in coastal connectivity. When considering the connectivity between JLDP and other regional protected areas, our analyses indicate that urbanized coastal regions act as barriers to mountain lions and contain pinch points that channelize movement. These results can guide TNC in developing management strategies for protecting mountain lions on JLDP and in the surrounding region.Analyses were conducted using ArcGIS, Google Earth Engine, MaxENT, Circuitscape, and Omniscape. The project began in April 2021 and ended in June 2022.
DOI: 10.25345/c5t43jb2j
title: MassIVE Reanalysis RMSV000000355.7388 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, PAEP [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5063/f1pg1q5s
title: Designcraft for Experiments: Solo Survey

description: A set of remote exercises exploring experimental design for the life sciences. A total of three independent labs were done to focus on different design principles, sampling techniques, and taxa. Exercises are described in full in this open access manual entitled Designcraft for experiments. Data were collected by students in a third-year biology course offering at York University in Toronto, ON, Canada. Students collected the data and published individual datasets to figshare with a CCBY4.0 licensing. Data were then compiled for each lab including balcony birdwatching, backyard bioblitz, and solo surveys. The provenance of each independent observation in the compiled data was recognized by attribution to the initials of the primary data author including citation via DOI and link to figshare.AttributionCJL designed the experiments.Students in course offering collected, innovated, and published individual, primary datasets.MZ and CJL reviewed the compiled data.MetadataRecord-levelyear is the calendar year of work not termexperiment describes the three experimental options in designcraft booksource is either full experiment or pilot experimentrep is the replicate per experimental setlocation is the site name locallyhabitat is the ecological classificationdate is the calendar date for each observation
DOI: 10.25345/c5t43jb31
title: MassIVE Reanalysis RMSV000000355.7566 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, CLDN7 [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5t43jb4h
title: MassIVE Reanalysis RMSV000000355.7774 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, CTXN1 [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5063/f1pn943m
title: The effect of shrub density on animals abundance in Panoche, California

description: The following data was collected during the 2019 field season in the Panoche in Southern California, United States. 16 Cameras were deployed at varying shrub densities ranging from no shrub individuals to a high number of individuals (11-12 shrubs). These cameras were deployed in 2 of our major sites. Cameras were not rotated and were deployed for a total of 27 continuous days. All animals in each photo were recorded as well as their behaviour, time of photo, and local density.
DOI: 10.25345/c5t43jb50
title: MassIVE Reanalysis RMSV000000355.8161 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, CEMP1 [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5t43jb6g
title: MassIVE Reanalysis RMSV000000355.8547 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, GBX2 [launched by dab8bba1f45f4e4082f7fa74fbbf30fb]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5063/f1pr7td6
title: Reproductive consequences of shrub association, artificial herbivory, and supplemental pollination for Malacothrix glabrata in the Mojave Desert.

description: We tested the effects of shrub facilitation, two types of artificial herbivory (folivory and florivory), and supplemental pollination on floral production and seed set of Malacothrix glabrata in the Mojave Desert.
DOI: 10.25345/c5t43jb7z
title: MassIVE Reanalysis RMSV000000676.2 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000081739 Pharmacoproteomic characterisation of human colon and rectal cancer - CPTAC Full Proteomes [Cohort: CMS1, BioReplicate: Sample_4E] [launched by 31fc25a7c92347c2b9f58f8f26be259f]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5063/f1q52n26
title: Global food system pressure data

description: These global raster data describe the environmental footprint of the majority of food production for 2017. Please refer to the accompanying publication (and supporting methods; Halpern et al. 2022. The environmental footprint of global food production. Nature Sustainability) for more information.We map four dominant global pressures of food production: disturbance (km2eq); blue freshwater consumption (m3 water); excess nutrients (tonnes NP); and greenhouse gas emissions (tonnes CO2eq). Categories of food include 26 crop categories (plus fodder, which is only consumed as feed); 6 livestock animals (cattle, buffalo, goats, sheep, pigs, chickens); 7 categories of marine fisheries, including forage fish species used for fishmeal and oil, other small pelagics, medium pelagics, large pelagics, benthic, demersal, and reef-associated; freshwater fisheries, with one group for all sizes and taxa; and 6 categories of marine aquaculture, including salmonids, unfed or algae fed shellfish, shrimp and prawns, tuna, other marine finfish, other crustaceans. We primarily assess pressures from sources occurring within the farm-gate. In most cases, we exclude activities occurring beyond the farm-gate, such as processing and transportation of product, manufacture of equipment, and extraction of fuel because we were generally unable to map the location of these activities. For feed components, we map the pressures to the location where the crops are likely grown, after accounting for trade, or fish are captured (vs. where they are fed to animals). In addition to the raw data files, we also provide rescaled pressure data and a cumulative pressure index that combines the 4 pressures. To calculate the cumulative pressure, we adopted similar methods as other cumulative measures, rescaling each individual pressure (GHG, FW, NP, D) by dividing the values in each pixel (i) by the total global pressure summed across all food systems and pixels (T), such that each pixel describes its proportional contribution to the global total for that pressure. We then summed these rescaled pressure layers to obtain a total cumulative pressure score (CP) for each pixel i, such that CPi = GHGi/GHGT + FWi/FWT + NPi/NPT + Di/DT (raster cell values are multiplied by 1000000 to avoid small numbers and rounding errors). The result is that each rescaled pixel is a unitless value describing its proportional contribution to the total global pressure. The cumulative pressure index allows direct comparisons among foods, regions, and pressures to identify where: individual pressures are high relative to other pressures, multiple pressures overlap, and hotspots of cumulative pressure are located. This information provides a more complete picture of the environmental pressures occurring at any global area and from each food type. We us the Gall-Peters equal area coordinate reference system with a resolution of 36km2.
DOI: 10.25345/c5t43jb8f
title: MassIVE Reanalysis RMSV000000362.111 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000081607 Confetti, ArgC-LysC-Trypsin HCD [launched by 31fc25a7c92347c2b9f58f8f26be259f]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5t43jb9x
title: MassIVE Reanalysis RMSV000000678.18 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000083043 CPTAC, TCGA Cancer Proteome Study of Breast Tissue [Notes: Proteome, Mixture: 24] [launched by 31fc25a7c92347c2b9f58f8f26be259f]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5063/f1qc01zq
title: Walleye abundance and length-at-age in Oneida Lake, New York,1957 to present

description: This dataset contains walleye abundance at different stage and age groups in Oneida Lake and is part of the collection "Cornell Oneida Lake Data". The Cornell Biological Field Station (CBFS) serves as a primary field site for aquatic research at Cornell University (more information can be found at http://cbfs.dnr.cornell.edu/index.html) and is part of the Department of Natural Resources, College of Agriculture and Life Sciences. The centerpiece of the station's research program is a 60-year database on the food web of Oneida Lake, New York, that has been collected with support from the Cornell University Brown Endowment and from the New York State Department of Environmental Conservation. The data are collected by personnel from the Cornell Biological Field Station and include limnology, benthos, zooplankton, phytoplankton, and fish survey data, primarily from Oneida Lake from 1957 to the present. The walleye data are based on standard trawl (since 1961) and gill nets catches (since 1959) (Trawl Data-package and Gillnet Data- package), mark-recapture estimates for age 4 and older walleye, Miller sampler surveys for larval walleye, and information from the Constantia Fish Cultural Station on the number of 3-day-old larval walleye stocked each year. There are three data tables, one with the estimated abundance for age-1 and older walleye, the second with back-calculated length at annulus formation for males and females, the third with estimated abundance, length, and sampling dates for the first year of life (larvae, juveniles, age-1 in the following spring). An additional table gives the catchabilities for trawl and gillnets used in the calculations of abundance..