cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18739/a2cv4bs43
title: Simulated firn density, temperature, liquid water content, compaction rates and cold content at nine Greenland Climate Network (GC-Net) weather stations sites during 1998-2017

description: This dataset provides the hourly output of the firn model developed for Vandecrux et al. (2020, https://doi.org/10.1017/jog.2020.30). In that study, we filtered and gap-filled weather data from nine Greenland Climate Network (GC-Net) stations: Crawford Point 1 (CP1), Dye-2, NASA-SE, NASA-E, Saddle, South Dome, NASA-U, Summit and Tunu-N. We used these data to calculate the surface energy and mass balance which is used to force the firn model. The firn model is decribed in Vandecrux et al. (2018, https://doi.org/10.1029/2017JF004597, 2020, https://doi.org/10.1017/jog.2020.30) and its code is available at https://github.com/BaptisteVandecrux/SEB_Firn_model. The firn model has 200 layers. each composed of snow, ice and water compartments. Layers are managed in a Lagrangian framework with a splitting/merging strategy that keeps the resolution higher close to the surface. During snowfall, fresh snow is added as a new 4 centimeter (cm) w.e. thick layer with a density of 315 kilogram per cubic meter (kg m-3) to the top of the model. During melt, mass is taken from snow and ice compartments of the top layer and transferred to the water compartment. Downward flow follows Darcy's law as described by Langen et al. (2017, doi: 10.3389/feart.2016.00110) and Vandecrux et al. (2018, https://doi.org/10.1029/2017JF004597, 2020, https://doi.org/10.1017/jog.2020.30) . When the underlying layer is below freezing point, the water is refrozen, moved to the ice compartment of that layer until either the layer reaches melting point or the layer's bulk density reaches ice density. In the rare cases when it occurred, ponding of water over a layer of reduced permeability was allowed. No lateral runoff was allowed. The firn density is updated every time step for compaction as calculated from the overburden pressure (Vionnet et al., 2012, https://doi.org/10.5194/gmd-5-773-2012).
DOI: 10.18739/a2833n00p
title: Firn temperatures and measurement depths at nine Greenland Climate Network (GC-Net) weather stations, 1998-2017

description: This dataset contains the firn temperature measurements and estimated measurement depths from nine Greenland Climate Network (GC-Net) stations: Crawford Point 1 (CP1), Dye-2, NASA-SE, Saddle, NASA-U, NASA-E, Summit and Tunu-N. They were produced for, and described in Vandecrux et al. (2020, https://doi.org/10.1017/jog.2020.30). Firn temperatures were filtered from obvious erroneous measurements and from measurement noise. Measurement depths were calculated from installation depth and updated for accumulation of new snow at the surface and compaction of snow and firn above the sensor. Raw data used to produce this dataset are available at http://cires1.colorado.edu/steffen/gcnet/.
DOI: 10.18739/a24b2x56x
title: Surface and subsurface conditions at nine Greenland Climate Network (GC-Net) weather station sites, 1998-2017

description: This dataset contains data used and produced for Vandecrux et al. (2020, https://doi.org/10.1017/jog.2020.30) at nine Greenland Climate Network (GC-Net) stations: Crawford Point 1 (CP1), Dye-2, NASA-SE, Saddle, South Dome, NASA-E, NASA-U, Summit, Tunu-N for the period 1998-2017. They also include updated data first presented in Vandecrux et al. (2018, https://doi.org/10.1029/2017JF004597). This parent data package consists of:
 1. The hourly gap-filled and standardized meteorological data and calculated surface energy budget.
 2. The simulated firn temperature, density, liquid water content and compaction.
 3. The firn temperature measurements and estimated measurement depths at each station.
 Raw data used to produce these three datasets are available at http://cires1.colorado.edu/steffen/gcnet/.
DOI: 10.18739/a20k26c25
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: 10.15468/dl.djgemj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((35.81667 -0.0533333, 35.81667 -0.0541667, 35.815 -0.0541667, 35.815 -0.0566667, 35.81417 -0.0566667, 35.81417 -0.0583333, 35.82 -0.0583333, 35.82 -0.0575, 35.82167 -0.0575, 35.82167 -0.0741667, 35.82083 -0.0741667, 35.82083 -0.075, 35.82167 -0.075, 35.82167 -0.08, 35.8225 -0.08, 35.8225 -0.0808333, 35.82417 -0.0808333, 35.82417 -0.08166669999999999, 35.825 -0.08166669999999999, 35.825 -0.0833333, 35.82583 -0.0833333, 35.82583 -0.0841667, 35.82667 -0.0841667, 35.82667 -0.08500000000000001, 35.8275 -0.08500000000000001, 35.8275 -0.0858333, 35.82833 -0.0858333, 35.82833 -0.0866667, 35.82917 -0.0866667, 35.82917 -0.08749999999999999, 35.83167 -0.08749999999999999, 35.83167 -0.0866667, 35.8325 -0.0866667, 35.8325 -0.08500000000000001, 35.83167 -0.08500000000000001, 35.83167 -0.0841667, 35.83083 -0.0841667, 35.83083 -0.0833333, 35.83 -0.0833333, 35.83 -0.08166669999999999, 35.82917 -0.08166669999999999, 35.82917 -0.0775, 35.82833 -0.0775, 35.82833 -0.07583330000000001, 35.8275 -0.07583330000000001, 35.8275 -0.0725, 35.82667 -0.0725, 35.82667 -0.0741667, 35.82583 -0.0741667, 35.82583 -0.0766667, 35.82333 -0.0766667, 35.82333 -0.07000000000000001, 35.82417 -0.07000000000000001, 35.82417 -0.0683333, 35.825 -0.0683333, 35.825 -0.0666667, 35.82583 -0.0666667, 35.82583 -0.065, 35.82667 -0.065, 35.82667 -0.06416669999999999, 35.82833 -0.06416669999999999, 35.82833 -0.0633333, 35.83167 -0.0633333, 35.83167 -0.0625, 35.83417 -0.0625, 35.83417 -0.0591667, 35.8325 -0.0591667, 35.8325 -0.06, 35.83167 -0.06, 35.83167 -0.0591667, 35.82917 -0.0591667, 35.82917 -0.06, 35.8275 -0.06, 35.8275 -0.0608333, 35.82583 -0.0608333, 35.82583 -0.06, 35.825 -0.06, 35.825 -0.0575, 35.82667 -0.0575, 35.82667 -0.0566667, 35.8275 -0.0566667, 35.8275 -0.0558333, 35.82833 -0.0558333, 35.82833 -0.0541667, 35.8275 -0.0541667, 35.8275 -0.0533333, 35.81667 -0.0533333))",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027810-241107131044228/datasets/export for details.
DOI: 10.6084/m9.figshare.27888897
title: Supplementary Table 5

description: <b>Supplementary Table 5. The subgroup analysis of the preventive effect of vitamin D levels on non-toxic thyroid nodule in the PSM cohort.</b>
DOI: 10.15468/dl.x29j24
title: Occurrence Download

description: A dataset containing 7992072 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Netherlands",
 "OccurrenceStatus is Present",
 "TaxonKey is Anseriformes",
 "Year 2010-2024"
 ]
}

 The dataset includes 7992072 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027801-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18739/a2vx06410
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 2011-2012

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 January 2011 through 31 December 2012. 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 2011 and 2012.
DOI: 10.15468/dl.bdwbqy
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Notoseris khasiana (C.B.Clarke) N.Kilian)"
 ]
}

 The dataset includes 27 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027811-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18739/a2r49g970
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 2013-2014

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 January 2013 through December 31 2014. 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 2013 and 2014.
DOI: 10.71687/00046950
title: KMD Institution (1992-2009)
DOI: 10.1594/pangaea.973635
title: A detailed snow POLDER bidirectional reflectance distribution function (BRDF) database in Arctic, version 2

description: The POLDER multi-angle dataset stands as one of the most significant datasets in multi-angle quantitative remote sensing, with previous applications primarily focusing on non-snow-covered surfaces. This dataset provides a more detailed classification of POLDER snow Bidirectional Reflectance Distribution Function (BRDF) data located within the Arctic Circle in 2008 based on the previous database (Breon and Maignan, 2017). Initially, constraints were applied to the angular sampling distribution, retaining pixels only with observations present in both forward and backward scattering directions on the principal plane. Subsequently, the high-precision snow BRDF model RossThick-LiSparse Reciprocal Snow model (RTLSRS) (Jiao et.al, 2019) was employed to constrain the data uncertainty, removing pixels with RMSE greater than 0.04 in red band. After screening, a total of 153 pixels were retained, followed by further detailed classification, validation using corresponding MOD10A2 products, supplemented by ArcticDEM and vegetation distribution maps within the Arctic Circle. Ultimately, these datasets were categorized into snow (shady slope, sunny slope) and non-snow (vegetation-dominated, mountain complex-dominated) categories. Such datasets hold significant importance for future applications of multi-angle data on snow-covered surfaces and represent further analysis and optimization of existing snow databases.
DOI: 10.18739/a2mc8rh2c
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 2015-2016

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. 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 January 2015 through December 31 2016. 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 2015 and 2016.
DOI: 10.18739/a2gm81p80
title: Baseline of Next-Generation Arctic Marine Shipping Assessments - Periodical Oldest 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. 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 for certain days from September 2009 through September 2016. 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 ship location data collected from a satellite Automatic Identification System (AIS).