cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18739/a2z60c32s
title: Baseline of Next-Generation Arctic Marine Shipping Assessments - Oldest Pan-Arctic Satellite Automatic Identification System (AIS) Data Record of Maritime Ship Traffic, ancillary data 2009-2010

description: The Arctic is prominent in the history of the International Maritime Organization (IMO), following the RMS Titanic disaster in 1912 and soon signing in London of the Convention for the Safety of Life at Sea in 1914. Eighty years later, the IMO initiated a process to manage shipping in ice-covered oceans. In concert with the IMO Guidelines for Ships Operating in Arctic Ice-Covered Waters in 2002 and their 2004 release of the Arctic 2004 Arctic Climate Impact Assessment, the Arctic Council initiated the Arctic Marine Shipping Assessment (AMSA), which issued its final report in 2009. Primary sources of data for AMSA involved ship tracking from ground-station Automatic Identification System (AIS), shore-based radar systems and details of fishing vessels as well as other smaller ships provided by the Arctic nations. However, Arctic ship traffic fundamentally changed the year of the AMSA report, when satellite AIS records began providing continuous, synoptic, pan-Arctic coverage of individual ships with data pulsed over seconds to minutes. This data packages contains data collected by satellite AIS from from 1 September 2009 through 31 December 2010. Future questions can be considered to assess ship attributes (including vessel flag state, size and type) in view of biophysical and socio-economic variables, recognizing that shipping and sea ice are recognized as primary drivers of change in the Arctic Ocean. Contributions to these assessments come from all areas of science (inclusively defined as the study of change), across the natural and social sciences with Indigenous knowledge in an holistic (international, interdisciplinary and inclusive) manner to achieve Arctic sustainability across generations. As a practical outcome in a user-defined manner, this chapter reveals characteristics of next-generation Arctic marine shipping assessments, revealing patterns and trends that can be applied to informed decisionmaking about the governance mechanisms and built infrastructure as well as operations for multilateral stability and sustainable development in the new Arctic Ocean.
 This dataset contains ancillary data about individual ships monitored using the satellite Automatic Identification System (AIS) for 2009 and 2010.
DOI: 10.18739/a2td9n89z
title: Baseline of Next-Generation Arctic Marine Shipping Assessments - Oldest Continuous Pan-Arctic Satellite Automatic Identification System (AIS) Data Record of Maritime Ship Traffic, 2009-2016

description: The Arctic is prominent in the history of the International Maritime Organization (IMO), following the RMS Titanic disaster in 1912 and soon signing in London of the Convention for the Safety of Life at Sea in 1914. Eighty years later, the IMO initiated a process to manage shipping in ice-covered oceans. In concert with the IMO Guidelines for Ships Operating in Arctic Ice-Covered Waters in 2002 and their 2004 release of the Arctic 2004 Arctic Climate Impact Assessment, the Arctic Council initiated the Arctic Marine Shipping Assessment (AMSA), which issued its final report in 2009. The goal of this chapter is to build on AMSA as a case study of informed decisionmaking through the steps of questions to generate data, which are then integrated into evidence to reveal options (without advocacy), informing decisions by relevant institutions to address a ‘continuum of urgencies’ that involve shipping in the new Arctic Ocean with its transformed sea-ice cap, assessing whether shipping is increasing as sea ice is decreasing (‘ship-ice hypothesis’). Primary sources of data for AMSA involved ship tracking from ground-station Automatic Identification System (AIS), shore-based radar systems and details of fishing vessels as well as other smaller ships provided by the Arctic nations. However, Arctic ship traffic fundamentally changed the year of the AMSA report, when satellite AIS records began providing continuous, synoptic, pan-Arctic coverage of individual ships with data pulsed over seconds to minutes. This chapter reveals the oldest and longest continuous satellite AIS record (from 1 September 2009 through 31 December 2016), applying the ‘spacetime cube’ (which also was unavailable during AMSA) with more than 120,000,000 satellite AIS messages from SpaceQuest Ltd. to begin addressing synoptic questions with any level of granularity from points to regions to pan-Arctic over time. Future questions can be considered to assess ship attributes (including vessel flag state, size and type) in view of biophysical and socio-economic variables, recognizing that shipping and sea ice are recognized as primary drivers of change in the Arctic Ocean. Contributions to these assessments come from all areas of science (inclusively defined as the study of change), across the natural and social sciences with Indigenous knowledge in an holistic (international, interdisciplinary and inclusive) manner to achieve Arctic sustainability across generations. As a practical outcome in a user-defined manner, this chapter reveals characteristics of next-generation Arctic marine shipping assessments, revealing patterns and trends that can be applied to informed decisionmaking about the governance mechanisms and built infrastructure as well as operations for multilateral stability and sustainable development in the new Arctic Ocean.
DOI: 10.15468/dl.gu8ccj
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ledermanniella thalloidea (Engl.) C.Cusset"
 ]
}

 The dataset includes 12 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027864-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18739/a2pr7mv65
title: UCYN-A/haptophyte symbiosis presence revealed by sequencing nitriogenase gene (nifH) diversity in the Bering, Chukchi and Beaufort Seas, September 2016

description: This research investigated the extent of nitrogen fixation and the structure of the diazotroph (N2-fixing) community in the coastal Alaskan Arctic. Samples to investigate the diazotroph community structure using the amplification of the nitriogenase gene (nifH) were collected during the summer of 2016 encompassing coastal regions in the Bering, Chukchi and Beaufort Seas with a specific interest in the areas of high productivity surrounding the Hanna Shoal region of the Chukchi Sea. Analysis of the diazotroph community led to the discovery that an unusual marine symbiosis involving two single celled partners – a eukaryotic (Haptophyte) algae and a unicellular cyanobacterial diazotroph (called UCYN-A) – was present in the Bering Sea. This remarkable symbiosis is broadly distributed throughout the world’s oceans, in both coastal and open ocean ecosystems but was not expected in the Arctic, and this is the first demonstration that it can fix nitrogen in cold water at rates similar to that in temperate and subtropical waters. This discovery opens many questions about the biology of this symbiosis, as well as its global role in fixing nitrogen. In addition to the UCYN-A symbiosis, we detected a diverse community of non-cyanobacterial diazotrophs, which are enigmatic, poorly understood groups encompassing many lineages of bacteria.
DOI: 10.18739/a2057ct39
title: Profiles of temperature, salinity, and turbidity from Sermilik Fjord and the Southeast Greenland shelf during August 2013

description: Forty-one temperature, salinity, and turbidity profiles from Sermilik Fjord in Southeast Greenland, close to the town of Tasiilaq, and from two sections over the Southeast Greenland continental shelf. The profiles were collected with an RBR conductivity-temperature-depth (CTD) (#18859) during August 2013 from the Viking Mads Alex. The profiles were manually examined and are provided as 1-meter (m) bin averages. The survey is comprised of: an along-fjord section; a section spanning the mouth of the fjord; two sections crossing portions of the continental shelf. These data were collected as part of a long-term project to monitor Sermilik Fjord to determine what water masses flow into the fjord; in particular, if warm, Atlantic water from the Irminger Sea is able to penetrate into the fjord. Observations have been collected in the fjord since 2008.
DOI: 10.18739/a2k06x237
title: Network of permafrost temperature observations in Russia, 2018-2020.

description: This work coordinates data collection using standard equipment and protocols at the Alaskan and Russian borehole sites. These borehole temperature data sets provide the baseline to reconstruct past surface temperatures, to assess the future rates of change in near-surface permafrost temperatures and permafrost boundaries, and to provide spatial data for validation of climate scenario models and temperature reanalysis approaches. This represents the Russia contribution to the ongoing activities of Global Terrestrial Network for Permafrost that obtains temperatures in a large number of globally distributed boreholes in order to provide a snapshot of permafrost temperatures in both time and space. Included are files with the depth, temperature, and date of soil sampled at a number of sites. Site information (site name, latitude, longitude) by file name can be found in the file 'metadata.csv'.
DOI: 10.15468/dl.xcabc5
title: Occurrence Download

description: A dataset containing 3204 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Faunal Inventory of Morysin Nature Reserve"
 ]
}

 The dataset includes 3204 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027865-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.pilfogmb
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (72 records)
DOI: 10.15468/dl.7zfmnm
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ledermanniella variabilis (G.Taylor) C.Cusset"
 ]
}

 The dataset includes 8 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027866-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18739/a25q4rn03
title: ABoVE: Gridded 30-meter Aboveground Biomass, Shrub Dominance, North Slope, Alaska, 2007-2016

description: This dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with Normalized Difference Vegetation Index (NDVI) from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery. The data cover the period 2007-06-01 to 2016-08-31.
 The data provided are the best estimates from the described modeling and Monte Carlo approach for each 30-m pixel in the Landsat mosaic at the 50th percentile, and also at the 2.5 and 97.5 percentiles for each data type (plant AGB, shrub AGB, and shrub dominance) which together encompass 95% of predictions.
 The published field measurements of total plant and shrub AGB used in the modeling were collected between July 1998 and August 2008. The mean and standard error (SE) of plant and shrub AGB were also acquired or computed for the data at each site. The regional Landsat NDVI mosaic was derived from 1,721 summer scenes acquired between 2007 and 2016. Spectral reflectance information was extracted from these 'peak greenness' scenes on a per pixel basis. Empirical AGB-NDVI relationships were developed for the field sites and the relationships were applied to the mosaic. The Monte Carlo uncertainty analysis involved generating 1,000 regional maps of each ecosystem data type, where each map was produced by randomly permuting the underlying field and remote sensing datasets by their uncertainty due to sampling and sensor calibration errors. The data with this dataset are the 50th percentile (best estimates), the 2.5, and 97.5 percentiles of the 1,000 permutations.
 There are nine data files of mapped AGB and shrub dominance with this dataset in GeoTIFF (.tif) format and one shapefile (.shp) provided in compressed (.zip) format which provides the study locations. The study locations are also provided as a companion file in .kmz format for viewing in Google Earth. A companion file of the published field measurements of total plant and shrub AGB used in the modeling is also available as a .csv file.
DOI: 10.18739/a21z41t76
title: ABoVE: Landsat tundra greenness and summer air temperatures, Arctic tundra biome, 1985-2016

description: Rapid climate warming is impacting tundra ecosystems, but much remains unclear about the nature and extent of recent ecological change. This dataset provides information on annual tundra greenness and summer air temperatures from 1985 to 2016 across the Arctic tundra biome. Annual tundra greenness was assessed at about 50,000 random vegetated sampling sites using the annual maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. This dataset includes tabular data on annual NDVImax and SWI from 1985 to 2016 for each sampling site in the Arctic tundra biome. The dataset also includes raster data quantifying (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and 2000 to 2016; and (3) temporal correlations between annual NDVImax – SWI during these two periods. These raster data span the Arctic tundra biome at a 50 km resolution. Each component of this dataset includes estimates of uncertainty that were generated using Monte Carlo simulations. The repository also includes all of the scripts developed for the analysis by Berner et al. (2020).
DOI: 10.18739/a2vd6p58q
title: Profiles of temperature, salinity, and turbidity from Sermilik Fjord and Kangerlussuaq Fjord during September 2012

description: Twenty-five temperature, salinity, and turbidity profiles from Sermilik Fjord and Kangerlussuaq Fjord in Southeast Greenland. The profiles were collected with an RBR conductivity-temperature-depth (CTD) (#18859) during September 2012 from the M/V Fox. The profiles were manually examined and are provided as 1-meter (m) bin averages. The survey was comprised of: an along-fjord section of Sermilik; a section spanning the mouth of Sermilik Fjord; and seven profiles near Kangerlussuaq Fjord. These data were collected as part of a long-term project to monitor Sermilik Fjord to determine what water masses flow into the fjord; in particular, if warm, Atlantic water from the Irminger Sea is able to penetrate into the fjord. Observations have been collected in the fjord since 2008.
DOI: 10.18739/a2qn5zc52
title: Profiles of temperature, salinity, and turbidity from Sermilik Fjord during August 2011

description: Fifty-six temperature, salinity, and turbidity profiles from Sermilik Fjord in Southeast Greenland. The profiles were collected with an RBR conductivity-temperature-depth (CTD) (#18859) during August 2011 from the Viking Mads Alex. The profiles were manually examined and are provided as 1-meter (m) bin averages. The survey was comprised of six cross-fjord sections collected at various locations along the fjord and a section spanning the mouth of the fjord. These data were collected as part of a long-term project to monitor Sermilik Fjord to determine what water masses flow into the fjord; in particular, if warm, Atlantic water from the Irminger Sea is able to penetrate into the fjord. Observations have been collected in the fjord since 2008.
DOI: 10.18739/a2x63b631
title: Normalized Difference Vegetation Index-3rd generation (NDVI) using the Global Inventory Monitoring and Modeling System (GIMMS) 1981-2015

description: Normalized Difference Vegetation Index-3rd generation (NDVI) using the Global Inventory Monitoring and Modeling System (GIMMS): Vegetation indices are radiometric measures of photosynthetically active radiation absorbed by chlorophyll in the green leaves of vegetation canopies and are therefore good surrogate measures of the physiologically functioning surface greenness level of a region. For 30 years, Compton J. Tucker created the NDVI time series within the framework of the Global Inventory Monitoring and Modeling System (GIMMS) project. He carefully assembled it from different Advanced Very High Resolution Radiometer (AVHRR) sensors and accounting for various deleterious effects, such as calibration loss, orbital drift, volcanic eruptions, etc. The latest version of the GIMMS NDVI data set spans the period July 1981 to December 2015 and is termed NDVI3g (third generation GIMMS NDVI from AVHRR sensors).
DOI: 10.18739/a2sj19s1q
title: Temperature and Salinity profiles from Upernavik Fjord, Northwest Greenland, from 2013 to 2019

description: Meltwater from Greenland is an important source of freshwater for the North Atlantic Ocean. Temperature and salinity data collected from 2013 to 2019 in Upernavik Fjord, a major glacial fjord in northwest Greenland, are used to identify both the water masses that flow into the fjord from the shelf and the exported Glacially Modified Water. Profiles in this data set were collected during fieldwork on Research Vessel (R/V) Porsild (for profiles from 2013) and as part of National Aeronautics and Space Administration's (NASA) Oceans Melting Greenland project (for profiles from 2015-2019). All profiles were manually examined to remove spurious data and ensure consistency across sampling platforms.
DOI: 10.15468/dl.qz8r7g
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ledermanniella yiben Cheek"
 ]
}

 The dataset includes 13 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027868-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18739/a2ns0kz8f
title: Conductivity-Temperature-Depth (CTD) data from the 2018 Distributed Biological Observatory - Northern Chukchi Integrated Study (DBO-NCIS) cruise on USCGC Healy (HLY1801)

description: In 2018 a joint cruise took place between the Distributed Biological Observatory – Northern Chukchi Integrated Study (DBO-NCIS) and the Ecosystems and Fisheries-Oceanography Coordinated Investigations (Eco-Foci) aboard the US Coast Guard Cutter Healy, from 4–24 August. The overall goal of DBO-NCIS is to document and understand ongoing changes to the Pacific-Arctic ecosystem in light of the changing physical drivers. The main objectives for the cruise were (1) to service a suite of moorings deployed on the Chukchi shelf and slope; (2) to occupy key transects on the northern Bering and Chukchi shelves, including DBO lines 2-5, with an extensive suite of water column and benthic measurements; (3) to perform a rapid, high-resolution hydrographic survey of the outflow from Barrow Canyon; and (4) to collect numerous underway measurements of the atmosphere-ocean-ice system. This submission contains all shipboard CTD measurements from the HLY1801 cruise. For more information on the DBO-NCIS program, visit https://www2.whoi.edu/site/dboncis/.
DOI: 10.18739/a2j09w54g
title: Arctic Great Rivers Observatory III Biogeochemistry and Discharge Data, 2017-2019

description: The PARTNERS (Pan-Arctic River Transport of Nutrients, Organic Matter, and Suspended Sediments) and Arctic-GRO (Great Rivers Observatory) projects sample the biogeochemistry of the six largest rivers draining to the Arctic Ocean: the Yenisey, Ob', Lena, and Kolyma Rivers in Siberia and the Yukon and Mackenzie Rivers in North America. To the greatest extent possible, sample collection techniques are identical across rivers. Once collected, samples are returned to Woods Hole, MA, from where they are shipped to expert laboratories for analyses. The Arctic Great Rivers Observatory III (Arctic-GRO III; NSF-OPP-1602615) Project spans the years between 2017 and 2019, and continues a sample collection effort that has been ongoing since 2004. On each river, samples are collected bi-monthly (six times per year), with target sampling months alternating between years. Full field and laboratory details for Arctic GRO III can be found in the "Sample Collection and Analyses" document on this page. For real-time updates of the Arctic GRO dataset, please visit www.arcticgreatrivers.org/data.
DOI: 10.18739/a2d795b9m
title: Arctic Great Rivers Observatory III Biogeochemistry and Discharge Data, 2017-2019

description: The PARTNERS (Pan-Arctic River Transport of Nutrients, Organic Matter, and Suspended Sediments) and Arctic-GRO (Great Rivers Observatory) projects sample the biogeochemistry of the six largest rivers draining to the Arctic Ocean: the Yenisey, Ob', Lena, and Kolyma Rivers in Siberia and the Yukon and Mackenzie Rivers in North America. To the greatest extent possible, sample collection techniques are identical across rivers. Once collected, samples are returned to Woods Hole, MA, from where they are shipped to expert laboratories for analyses. The Arctic Great Rivers Observatory III (Arctic-GRO III; NSF-OPP-1602615) Project spans the years between 2017 and 2019, and continues a sample collection effort that has been ongoing since 2004. On each river, samples are collected bi-monthly (six times per year), with target sampling months alternating between years. Full field and laboratory details for Arctic GRO III can be found in the "Sample Collection and Analyses" document on this page. For real-time updates of the Arctic GRO dataset, please visit www.arcticgreatrivers.org/data.
DOI: 10.18739/a28g8fj5g
title: Conductivity-temperature-depth data for Barrow/Utqiagvik, Alaska area August-September 2006

description: August-September 2005-2015 CTD (conductivity-temperature-depth) downcast data were acquired to characterize and monitor variability of the physical oceanographic environment associated with the Utqiagvik (Barrow), Alaska bowhead whale feeding hotspot. Data were acquired prior to the onset of the fall whale hunt. Details of the latitude, longitude, date, and time are listed in the event log that is archived at this site. Dates/times are ADT (Alaska Daylight time). CTD data were collected by SeaBird SBE 19plus and most of the data were acquired along fixed established section lines. No post-processing of the data was done.
 This dataset contains CTD casts Barrow/Utqiagvik, Alaska August-September 2006
DOI: 10.18739/a24q7qr1b
title: Barrow/Utqiagvik, Alaska area CTD (conductivity-temperature-depth) data 2007

description: August-September 2005-2015 CTD (conductivity-temperature-depth) downcast data were acquired to characterize and monitor variability of the physical oceanographic environment associated with the Utqiagvik (Barrow), Alaska bowhead whale feeding hotspot. Data were acquired prior to the onset of the fall whale hunt. Details of the latitude, longitude, date, and time are listed in the event log that is archived at this site. Dates/times are ADT (Alaska Daylight time). CTD data were collected by SeaBird SBE 19plus and most of the data were acquired along fixed established section lines. No post-processing of the data was done.
 This dataset contains CTD casts Barrow/Utqiagvik, Alaska August-September 2007.