cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5063/f1930rc9
title: Sockeye salmon brood tables, PWS and Southeast, Alaska, 1958-2016

description: Brood tables, also called run reconstructions, utilize annual estimates of the total run (commercial catch plus escapement), and samples of ages, to estimate the number of recruits per age class. These data are useful for salmon biologists to understand salmon productivity and salmon life histories. This dataset consists of: original brood tables in excel format for sockeye salmon from Copper River, Coghill and Eshamy Lakes of Prince William Sound (PWS), and Chilkoot, Redoubt, and Speel Lakes of Southeast Alaska; R scripts that slightly reformat the dataset into a format consistent with other brood tables collected as part of the State of Alaskan Salmon and People project (https://alaskasalmonandpeople.org/); and the reformatted tables as .csv files. Age classes are given in European Notation, where the first number is the number of winters spent in freshwater before going to sea (1 winter in freshwater = age-1.X), and the second number is the number of winters spent at sea (3 winters at sea = age-X.3).
DOI: 10.5063/f11j97z4
title: Subsistence harvest information by region, community, resource, and year, 1964-2015

description: Subsistence uses of wild resources are defined as 'noncommercial, customary and traditional uses' for a variety of purposes. These include: direct personal or family consumption as food, shelter, fuel, clothing, tools, or transportation, for the making and selling of handicraft articles out of nonedible by-products of fish and wildlife resources taken for personal or family consumption, and for the customary trade, barter, or sharing for personal or family consumption. Under subsistence statute in Alaska, the Alaska Board of Fisheries must identify fish stocks that support subsistence fisheries and, if there is a harvestable surplus of these stocks, adopt regulations that provide reasonable opportunities for these subsistence uses to take place. Whenever it is necessary to restrict harvests, subsistence fisheries have a preference over other uses of the stock. Subsistence fishing and subsistence hunting are important for the economies and cultures of many families and communities in Alaska. Subsistence uses of wild resources exist alongside other important uses of fish and game in Alaska and are especially important for most rural families, who depend on subsistence hunting and fishing as sources of nutrition and cultural practices. An estimated 36.9 million pounds of wild foods are harvested annually by rural subsistence users. Residents of more populated urban areas harvest about 13.4 million pounds of wild food under subsistence, personal use, and sport regulations. Harvest and community information is given here. Harvest information includes participation in subsistence activities as well as estimates of community, household, and per capita harvest of many different resource categories. Community information includes geographic and population information, and overall community harvest information. These data are also available and updated at https://www.adfg.alaska.gov/sb/CSIS/. The version archived here was extracted on 8/14/2017.
DOI: 10.5063/f15b00nz
title: Summary data of subsistence salmon catch from in-season fish camp surveys, Lower Kuskokwim River, Alaska, 2009-2016

description: The Kuskokwim inseason subsistence catch monitoring project, a partnership between the Orutsararmiut Traditional Native Council (OTNC) and the Alaska Department of Fish and Game (ADF&G), seeks to provide local input from subsistence fisherman on salmon management during the fishing season (Fishery Management Report No. 16-06). Salmon are a highly important resource to residents of the Kuskokwim river drainage, and account for the majority of all wild resources consumed for subsistence in this watershed. The subsistence harvest monitoring program began in 2001, and as of 2004 has specifically centered on the Bethel area subsistence fishery. The OTNC surveys fish camps in the Bethel area every year in June and July for information about fishing activity, whether the camps are reaching their subsistence goals, what kind of fishing gear they are using, and relative run timing and fishing success compared to previous years. Certain data from these forms was redacted to ensure the exact location of the fish camps and families fishing there are kept confidential. Forms were transcribed in to tabular format for each weekly visit to a fish camp. These forms were then reformatted into the summary table, Fish_Camp_Summary_Table.csv. Additional information on project objectives, methods, and results can be found in the archived files ending in "...InseasonHarvestMonitoring.pdf". The files ending in "...infopacket.pdf" are weekly reports from the OTNC and Kuskokwim River Salmon Management Working Group, which contain data summaries, analyses, and comments from meetings that were held about each of the weekly surveys. The date listed in the file name is the date of the meeting for that weekly survey.
DOI: 10.18739/a27659f1b
title: Greenland GPS Network (GNET) 2007-08-11 to 2018-07-31
DOI: 10.5063/f13f4mtk
title: Sockeye salmon brood tables, northeastern Pacific, 1922-2016

description: Brood tables, also called run reconstructions, utilize annual estimates of the total run (commercial catch plus escapement), and samples of ages, to estimate the number of recruits per age class. These data are useful for salmon biologists to understand salmon productivity and salmon life histories. These data can come in a number of different formats, but generally follow the pattern of: rows for each brood year, and columns for the estimated number of fish in each age class. This dataset consists of reformated and merged brood tables from Alaska, British Columbia, and Washington. These datasets were gathered as part of the State of Alaskan Salmon and People project (https://alaskasalmonandpeople.org/). Source information for the datasets can be found in the SourceInfo.csv file. A description of the merging and quality assurance process can be found in the broodTableProcessing.html file. Productivity (R/S) should not be directly compared among the stocks because some escapement counts are indices. Age classes are given in European Notation, where the first number is the number of winters spent in freshwater before going to sea (1 winter in freshwater = age-1.X), and the second number is the number of winters spent at se (3 winters at sea = age-X.3).
DOI: 10.5063/f1833q6g
title: Salmon age, sex, and length, Prince William Sound and Copper River, 1960-2016

description: This package includes age, sex, length (ASL) data for sockeye, chinook, coho, and chum salmon from the Prince William Sound and Copper River areas. To gather age/sex/length samples, scales are taken from fish out of a representative sample caught at an ASL sampling project. These projects can be from escapement enumeration projects, commercial fishing operations, or hatchery operations. Scales from fish taken during a sampling event are placed on "scale cards," along with other information about both the sampling event (such as the date and location) and the fish itself (such as length and sex). As Aged.zip contains several fixed-width files, AscIIHeaderinfo.jpg gives more detail on the positions of information within the fixed-width format, and filanameinfo.jpg describes the file format and codes contained within the filenames. The file PWS_AsAged_Formatting.Rmd (and its output, PWS_AsAged_Formatting.html) details the processing steps taken to produce the reformatted data file, PWS_CopperRiver.csv, from the fixed-width files. The excel files aspws.xls and CBRStrmCodes.xls include stream level location information for Prince William Sound and Copper River, and the file PWS_location_lookup.csv is used to add location information to the data file.
DOI: 10.5063/f1cv4fxg
title: Compiled annual statewide Alaskan salmon escapement counts, 1921-2017

description: The number of mature salmon migrating from the marine environment to freshwater streams is defined as escapement. Escapement data are the enumeration of these migrating fish as they pass upstream, and are a widely used index of spawning salmon abundance. These data are important for fisheries management, since most salmon harvest occurs near the mouths of rivers where salmon spawn during this migration. Escapement data are collected in a variety of ways. Stationary projects utilize observers stationed along freshwater corridors who count salmon as they pass upriver through weirs or past elevated towers. Sonar equipment placed in the river can also give a stationary escapement count. These counts usually represent a sample, and are expanded to represent a 24h period. Escapement data can also be collected using aerial surveys, where observers in an aircraft provide an index to estimate escapement. In general, escapement counts do not represent total abundance, but instead an index of abundance. Surveys are usually timed to coincide with peak spawning activity, generally in the summer, but in the case of Coho salmon in the fall as well. Some data about non-salmon species are also included. This dataset contains compiled annual data from multiple sources. The .Rmd merges all datasets, attaches location information from the escapement_locations.csv file, identifies and flags duplicate records, and performs quality assurance checks by filtering and graphing results.
DOI: 10.5063/f1q52mtn
title: Daily salmon escapement counts from in-season online reporting by the Alaska Department of Fish and Game, Alaska, 1955-2016

description: The salmon life cycle begins in freshwater streams when adult salmon spawn, leaving fertilized eggs which hatch in the stream. Juvenile salmon migrate downstream to the ocean, where they spend several years until they reach reproductive age. Upon reaching sexual maturity, they return to their natal streams to spawn. The number of mature salmon migrating from the marine environment to freshwater streams is defined as escapement. Escapement data are the enumeration of these migrating fish as they pass upstream, and are a widely used index of spawning salmon abundance. These data are important for fisheries management, since most salmon harvest occurs near the mouths of rivers where salmon spawn during this migration. Escapement data are collected in a variety of ways. Stationary projects utilize observers stationed along freshwater corridors who count salmon as they pass upriver through weirs or past elevated towers. Sonar equipment placed in the river can also give a stationary escapement count. These counts usually represent a sample, and are expanded to represent a 24h period. Escapement data can also be collected using aerial surveys, where observers in an aircraft provide an index to estimate escapement. In general, escapement counts do not represent total abundance, but instead an index of abundance. Surveys are usually timed to coincide with peak spawning activity, generally in the summer, but in the case of Coho salmon in the fall as well. The data included in this package are extracted from the Fish Counts tab under the Sport fishing section on Alaska Department of Fish and Game's website (http://www.adfg.alaska.gov/sf/FishCounts/). The R script (SportFish_master_cleaning.R) included in this package merged all the data into one file (SportFish.csv) and reformatted all the data: the year and month/day columns were merged into a single date column; trailing spaces from species, region and location columns were removed; run information from the species name column was moved into a new run type column. The file sportfish_locs.csv contains region information for unique locations in SportFish.csv.
DOI: 10.5063/f1sx6bdp
title: Global change in marine aquaculture production potential under climate change

description: Climate change is an immediate and future threat to food security globally. The consequences for fisheries and agriculture production potential are well studied, yet the possible outcomes for aquaculture (i.e., aquatic farming) – the fastest growing food sector on the planet – remains a major gap in scientific understanding. With over a third, and growing, of aquaculture produced in marine waters it is critical to anticipate new opportunities and challenges to marine production under climate change. Here we model and map the effect of warming oceans conditions (RCP 8.5) on marine aquaculture production potential over the next century, based on thermal tolerance and growth data of 180 cultured finfish and bivalve species. We find heterogeneous patterns of gains and losses, but an overall greater probability of declines worldwide. Accounting for multiple drivers of species growth, including shifts in temperature, chlorophyll, and ocean acidification (OA), reveals potentially greater declines in bivalve aquaculture compared to finfish production. This study addresses a missing component in food security research and sustainable development planning by identifying regions that will face potentially greater climate change challenges and resilience for marine aquaculture in the coming decades. Understanding the scale and magnitude of future increases and reductions in aquaculture potential is critical for designing effective and efficient use and protection of the oceans, and ultimately for feeding the planet sustainably.
DOI: 10.5063/f1tx3ckh
title: Daily salmon escapement counts from the OceanAK database, Alaska, 1921-2017

description: The number of mature salmon migrating from the marine environment to freshwater streams is defined as escapement. Escapement data are the enumeration of these migrating fish as they pass upstream, and are a widely used index of spawning salmon abundance. These data are important for fisheries management, since most salmon harvest occurs in freshwater rivers during this migration. Escapement data are collected in a variety of ways. Stationary projects utilize observers stationed along freshwater corridors who count salmon as they pass upriver through weirs or past elevated towers. Sonar equipment placed in the river can also give a stationary escapement count. These counts usually represent a sample, and are expanded to represent a 24h period. Escapement data can also be collected using aerial surveys, where observers in an aircraft provide an index to estimate escapement. In general, escapement counts do not represent total abundance, but instead an index of abundance. Surveys are usually timed to coincide with peak spawning activity, generally in the summer, but in the case of Coho salmon in the fall as well. This dataset contains salmon escapement data extracted from the Alaska Department of Fish and Game's OceanAK database, and contains data from multiple regions in Alaska.
DOI: 10.5063/f1zp44bp
title: Coho salmon brood tables, northeast Pacific, 1967-2015

description: Brood tables, also called run reconstructions, utilize annual estimates of the total run (commercial catch plus escapement), and samples of ages, to estimate the number of recruits per age class. These data are useful for salmon biologists to understand salmon productivity and salmon life histories. These data can come in a number of different formats, but generally follow the pattern of: rows for each brood year, and columns for the estimated number of fish in each age class. This dataset consists of reformatted coho salmon brood tables that have been compiled together. These datasets were gathered as part of the State of Alaska Salmon and People project (https://alaskasalmonandpeople.org/). The file "CohoBroodTables_FW_SW_Age.csv" includes stocks where age classes are in freshwater/saltwater notation (number of winters in freshwater, followed by number of winters in saltwater). The file "CohoBroodTables_TotalAge.csv" includes stocks where age classes are presented as total age (number of winters in freshwater + number of winters at sea + 1). Stocks that have data in freshwater/saltwater age had their total age calculated and were then added to the total age table. The file cohoBroodTableProcessing.Rmd details the data reformatting steps in R Markdown, and cohoBroodTableProcessing.html is the html output of the Rmd file. The file StockInfo_Coho.csv lists the chinook stocks that are included in this package, and SourceInfo_Coho.csv lists the data sources for each Source.ID in the data files.
DOI: 10.7910/dvn/nhixnt/27wtnb
title: ado files.zip

description: Contains ado files needed to run the code.
DOI: 10.7910/dvn/nhixnt/8ozxav
title: ScienceDataRelease.zip

description: Contains all files needed for replication (data, code, surveys, ado files) in the proper folder structure, as well as two Readme files.
DOI: 10.7910/dvn/nhixnt/oilro5
title: 2 - Readme - errata_updated August 2018.docx

description: Readme document which provides information about the four updates made to the existing data and results.
DOI: 10.7910/dvn/nhixnt/syzjmh
title: 8_quantile_analysis_table5.do

description: Code to produce table 5.
DOI: 10.18739/a2n872z77
title: Network of Permafrost Observatories in North America - Temperature on surface, 2016
DOI: 10.5063/f1jd4tzj
title: Summary of Pacific Salmon Escapement Goals in Alaska with a Review of Escapements from 2007 to 2015

description: This dataset is compiled from Tables 1-4 from the report "Summary of Pacific salmon escapement goals in Alaska with a review of escapements from 2007 to 2017." Escapement is defined as the number of mature salmon migrating from the marine environment to freshwater streams. Escapement data are the enumeration of these migrating fish as they pass upstream, and are a widely used index of spawning salmon abundance. These data are important for fisheries management, since most salmon harvest occurs in freshwater rivers during this migration. As a part of management of these salmon stocks, some Alaskan river systems are assigned escapement goals, which generally represent the number of salmon that need to be given the opportunity to spawn to ensure survival of the stock. Generally there are three common types of escapement goals. Biological Escapement Goals (BEGs) are defined in Alaska fishery regulations 5 AAC 39.222(f) as "the escapement that provides the greatest potential for maximum sustained yield." Sustainable Escapement Goals (SEGs) are defined in the same section as "a level of escapement, indicated by an index or an escapement estimate, that is known to provide for sustained yield over a 5 to 10 year period, used in situations where a BEG cannot be estimated or managed for." Optimal Escapement Goals (OEGs) are defined as "a specific management objective for salmon escapement that considers biological and allocative factors and may differ from the SEG or BEG."
DOI: 10.5063/f1p55kp8
title: Coho salmon brood table, Kuskokwim River, Alaska, 2000-2012

description: Brood tables, also called run reconstructions, utilize annual estimates of the total run (commercial catch plus escapement), and samples of ages, to estimate the number of recruits per age class. These data are useful for salmon biologists to understand salmon productivity and salmon life histories. Archived here are the original PDF from which the data were extracted, the original .csv file with this extracted data, an R script that slightly reformats the dataset into a format consistent with other brood tables collected as part of the State of Alaskan Salmon and People project (https://alaskasalmonandpeople.org/), and the reformatted table as a .csv file. Age classes are given in European Notation, where the first number is the number of winters spent in freshwater before going to sea (1 winter in freshwater = age-1.X), and the second number is the number of winters spent at sea (3 winters at sea = age-X.3).
DOI: 10.5066/f7x0660r
title: Chemical analyses and histopathology of small rodents, vegetation, and soil collected from the Kanab North breccia pipe uranium mine in the Grand Canyon watershed

description: These data are comprised of measurements of aluminum, iron, cobalt, nickel, copper, zinc, arsenic, selenium, molybdenum, silver, cadmium, thallium, lead, bismuth, thorium, uranium, and mercury in soil, vegetation, and small rodents. Gross alpha activity, gross beta activity, and radionuclide activities (isotopic U, isotopic Th) are also presented for vegetation and soil. Radioactivities for small rodents were previously presented at https://doi.org/10.5066/P9HARTQ4. The data also include histopathological analysis of liver tissues collected from small rodents. Samples were collected at the Kanab North Mine and Canyon Mine (post-mining, pre-reclamation) in the Grand Canyon watershed.
DOI: 10.5066/p99gdfwb
title: Results from radiochemical analyses of small rodent whole bodies collected from breccia pipe uranium mines and reference locations in the Grand Canyon watershed.

description: These data are comprised of measurements of gross alpha activity, gross beta activity, and radionuclide activities (isotopic U, isotopic Th, Ra-226) in small rodent bodies. Samples were collected at non-mining and U breccia pipe mining sites in various production stages, in the Grand Canyon watershed.
DOI: 10.5281/zenodo.1405134
title: cldf-datasets/szetosinitic: Chinese Structure Dataset from Szeto et al.'s (2018) paper in CLDF-Format

description: This is a structural dataset originally published along with a paper by Szeto et al. (2018) on Chinese dialect classification: Szeto, P. Y.; Ansaldo, U. & Matthews, S.Typological variation across Mandarin dialects: An areal perspective with a quantitative approach Linguistic Typology, 2018, 22, 233-275.
DOI: 10.5281/zenodo.1405135
title: cldf-datasets/szetosinitic: Chinese Structure Dataset from Szeto et al.'s (2018) paper in CLDF-Format

description: This is a structural dataset originally published along with a paper by Szeto et al. (2018) on Chinese dialect classification: Szeto, P. Y.; Ansaldo, U. & Matthews, S.Typological variation across Mandarin dialects: An areal perspective with a quantitative approach Linguistic Typology, 2018, 22, 233-275.
DOI: 10.5281/zenodo.1405147
title: cldf-datasets/normansinitic: Structural and lexical data for the paper by Norman (2013) on Chinese dialect classification

description: Original source of the data: Norman, J. (2003): Chinese dialects. Phonology. In: Thurgood, G. & LaPolla, R.: The Sino-Tibetan Languages. Routledge: London and New York. 72-83.
DOI: 10.5281/zenodo.1405148
title: cldf-datasets/normansinitic: Structural data for the paper by Norman (2013) on Chinese dialect classification

description: Original source of the data: Norman, J. (2003): Chinese dialects. Phonology. In: Thurgood, G. & LaPolla, R.: The Sino-Tibetan Languages. Routledge: London and New York. 72-83.
DOI: 10.5281/zenodo.1405172
title: Sequencing data for "Rapid heuristic inference of antibiotic resistance and susceptibility by genomic neighbor typing"

description: This repository contains sequencing data from the following paper: Karel Břinda, Alanna Callendrello, Kevin C. Ma, Derek R MacFadden, Themoula Charalampous, Robyn S Lee, Lauren Cowley, Crista B Wadsworth, Yonatan H Grad, Gregory Kucherov, Justin O’Grady, Michael Baym, and William P Hanage. Rapid heuristic inference of antibiotic resistance and susceptibility by genomic neighbor typing<strong>.</strong> 2019.
DOI: 10.5281/zenodo.1405173
title: Sequencing data for "Lineage calling can identify antibiotic resistant clones within minutes"

description: This repository contains sequencing data from the following paper: Karel Břinda, Alanna Callendrello, Lauren Cowley, Themoula Charalampous, Robyn S Lee, Derek R MacFadden, Gregory Kucherov, Justin O'Grady, Michael Baym, William P Hanage. <strong>Lineage calling can identify antibiotic resistant clones within minutes.</strong> bioRxiv, 2018. doi:10.1101/403204