cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18739/a2jw86p2j
title: Shelfbreak Upwelling in the Western Beaufort Sea, Zooplankton Morphometrics data for August-September 2017

description: Atmospherically-forced wind-induced upwelling along the shelf break leads to enhanced feeding opportunities for intermediate links in the pelagic ecosystem that in turn sustain the exploitation of this environment by animals such as beluga, seabirds, and seals. The Beaufort Sea shelf break is a hotspot for upper trophic level animals because elevated numbers and biomass of large, high-energy zooplankton (e.g., lipid-rich copepods, euphausiids) are regularly upwelled from deeper water onto the shelf during winds from the east, retained there by frontal features when upwelling relaxes, and exploited by bowhead whales, seabirds, and forage fish that in turn are prey for piscivorous marine mammals, such as beluga whales. The zooplankton morphometric measurements were determined for dominant copepods and euphausiids collected with various nets. Data were acquired as part of the National Science Foundation (NSF)-funded project "The Importance of Shelf Break Upwelling to Upper Trophic Level Ecology in the Western Beaufort Sea". Net samples were collected within the box 165 West (W) - 145 W, 69 North (N) - 72N between 30 August and 15 September 2017. PIs: Carin Ashjian (WHOI), Robert Campbell (URI), Michael Jech (NOAA), Joel Llopiz (WHOI), Michael Lowe (WHOI), Stephen Okkonen (UAF), Kathleen Stafford (APL-UW), Jinlun Zhang (APL-UW)
DOI: 10.18739/a2f47gv7m
title: Arctic Ocean Tidal Constituents, 1900-1970. Region 90N to 61.66N (North)

description: Tidal constituents for the eight major tides in the Arctic Ocean are given on the spherical grid with a spatial resolution of 10x10 minutes. These data will serve for comparison with models and observations. The constituents (amplitude and phase) were computed according to the method given in: Kowalik, Z. and A. Yu. Proshutinsky, 1994. The Arctic Ocean Tides, In: The Polar Oceans and Their Role in Shaping the Global Environment: Nansen Centennial Volume, Geophysical Monograph 85, AGU, 137-158. For every constituent included are: Sea level amplitudes (e.g., for K1 constituent): IMS_UAF_K1_tide-level, and U and V velocities: IMS_UAF_K1_U-velocity, IMS_UAF_K1_V-velocity. To save space all the files are compressed (zipped). How the data are organized explains the file: README. The program read.f90 makes actual file reading. Tide gauge data for the four basic constituents are given in: DATA.BASE
DOI: 10.18739/a29c6s24c
title: DataHawk2 Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign, raw data

description: This dataset includes unprocessed raw data from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Synchronized and quality controlled data are available in the Arctic Data Center at doi:10.18739/A22Z12Q8X for data provided at their native frequency logged on board the aircraft’s secure digital (SD) card (A1 level files), or at doi:10.18739/A2Z60C34R for data interpolated to a common 10 hertz (Hz) clock (B1 level files). Users are encouraged to primarily use the B1 level data for analysis. Please contact the authors if you plan to use this dataset. More information on data collection with the DataHawk2 can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2022): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, submitted.
DOI: 10.6084/m9.figshare.19412147
title: Black Hole Weather Forecasting with Deep Learning: A Pilot Study

description: This repository contains the data files containing the convolutional neural network weights resulting from our multi GPU training using Tensorflow v1.8.0. Both files are in the HDF5 binary format. The results from this architecture was published in <em>Black Hole Weather Forecasting with Deep Learning: A Pilot Study</em> (Duarte, Nemmen & Navarro, MNRAS, in press). • dl_fluids.h5: one-sim model• dl_fluids-1.h5: multi-sim modelPlease refer to the source file<code>inference.py</code> available in the github repository (https://github.com/black-hole-group/DL_BH_fluids), which demonstrates how to create predictions using these data files. In particular, you can load the weights using <code>model.load("/path/to/.h5/dl_fluids.h5").</code><br>
DOI: 10.18739/a25q4rn22
title: Numerical model integrations of the Pan-Arctic ROMS (PAROMS) ocean circulation and sea ice model, 1980-2018

description: Data are available at https://arcticdata.io/data/10.18739/A2G73751P/. PAROMS model development has been supported by a variety of funding agencies, including the North Pacific Research Board project 1302, the Bureau of Ocean Energy Management, grant M15AC 00011 and the National Science Foundation grant OPP-1603116. The model computes ocean velocity, temperature, salinity, pressure and sea ice characteristics on a telescoping grid that stretches from south of the Aleutian Islands in the North Pacific across the Arctic Ocean to the North Atlantic Ocean. We use the Regional Ocean Modeling System (ROMS), described by Shchepetkin and McWilliams (2005). Our model implementation, which we refer to as the Pan-Arctic ROMS model (or PAROMS), is set up using ROMS options for high-order algorithms of tracer advection (3rd order upwind in the horizontal direction and 4th order centered in the vertical direction); surface and bottom boundary layers following the K-Profile Parameterization (Large et al., 1994); and atmosphere-ocean bulk flux computations based on the ocean model prognostic variables (Fairall et al., 2003; Large and Yeager, 2009).
DOI: 10.25345/c5sn0178m
title: MassIVE MSV000089121 - Peptidomics analyses of synaptosomes in Alzheimer's disease and control human brains
title: Peptidomics analyses of synaptosomes in Alzheimer's disease patients and control individuals
DOI: 10.18739/a21z41v08
title: Tide Model Driver (TMD) version 2.5, Toolbox for MATLAB (MATrix LABoratory)

description: Data available for download at https://arcticdata.io/data/10.18739/A24Q7QR39/ TMD 2.5 (“Tide Model Driver, version 2.5”) is a MATLAB (MATrix LABoratory) toolbox for accessing the coefficients for tidal harmonic constituents in barotropic tide models created by Oregon State University and Earth and Space Research, and for making predictions of tide height and currents. TMD 2.5 includes two components: (1) a set of scripts for accessing tidal fields and making predictions; and (2) a graphical user interface (GUI) for quickly browsing tide fields, zooming in on regions of interest, and selecting points and time ranges for predictions of specific variables. The toolbox is provided with a detailed User Manual with several examples of how to use its different functions.
DOI: 10.18739/a2x63b650
title: Sufficient conditions for climate-driven range expansion of a boreal conifer, Alaska, 2019-2021

description: Unprecedented modern rates of warming are expected to advance boreal forest into Arctic tundra, thereby reducing albedo, altering C-cycling (carbon-cycling), and changing global climate, yet the patterns and processes of this biome shift remain unclear. We describe the 20th century colonization of an Arctic basin by a widespread boreal conifer, Picea glauca, 40 km (kilometer) north of the nearest established treelines. The population approximately doubled each decade, with radial growth in main stems increasing exponentially and correlating positively to July air temperature. Juvenile height and adult lateral growth were 90% faster than at established treelines. This climate-forced range expansion, cast in the context of invasion theory, informs forecast models of vegetation change with the ecological conditions driving this biome shift. While surpassing temperature thresholds is a necessary condition for boreal forest advance, our empirical results indicate high soil nutrient availability, deep snow, and winter winds facilitate long-distance dispersal and promote recruitment.
DOI: 10.18739/a2sf2md0c
title: Acoustic Doppler Current Profiler (ADCP) measurements from the mooring taken in the Eurasian basin, Arctic Ocean, 2018-2021.

description: The long-term mooring M1-4c was deployed in the Eurasian Basin of the Arctic Ocean at 78o28.084N (North) and 125o57.679E (East), 2700 meters (m) during the Nansen and Amundsen Basin Observational System (NABOS) 2018 cruise. It was successfully recovered on September 19, 2021. The primary goal of these mooring observations was to understand the role of stratification in controlling transports of the Atlantic Water (AW) heat and other properties to the upper ocean and ice. Primary objectives included quantifying the structure and temporal variability of the main water masses and obtaining detailed information about upper ocean, halocline, and Atlantic water (AW) layer dynamics and seasonal variations. The mooring was heavily equipped with current meters. It used a combination of two 300 kHz Acoustic Doppler Current Profilers (ADCP), to measure the velocity profile in the upper 120 meters (m) of the water column. Two 75 kHz ADCPs (Longrangers) measured the velocity profile in 200-600m of the water column. All instruments of the recovered mooring provided three years long records. This archive includes Nansen and Amundsen Basin Observational System (NABOS) Acoustic Doppler Current Profilers (ADCP) current meter mooring data collected at M1-4c mooring in 2018-2021. Each file contains the date of Acoustic Doppler Current Profilers (ADCP) measurements, the depths of the ADCP bins, and data collected on the eastward and northward velocity provided by each ADCP bin.
DOI: 10.18739/a2np1wk54
title: Conductivity, Temperature, Pressure (CTD) measurements from Sea Bird Electronics SBE37 instruments taken in the Eurasian Basin, Arctic Ocean, 2018-2021.

description: The long-term mooring M1-4c was deployed in the Eurasian Basin of the Arctic Ocean at 78o28.084N (North) and 125o57.679E (East), 2700 meter (m) during the Nansen and Amundsen Basin Observational System (NABOS) 2018 cruise. It was successfully recovered on September 19, 2021. The primary goal of mooring Conductivity, Temperature, Pressure (CTD) observations was to understand the role of stratification in controlling transports of the Atlantic Water heat and other properties to the upper ocean and ice. Primary objectives included quantifying the structure and temporal variability of the main water masses and obtaining detailed information about upper ocean, halocline, and Atlantic water (AW) layer dynamics and seasonal variations. This data set contains separate text files with outputs of each Sea Bird Electronics (SBE)-37 instrument at M1-4c mooring. SBE-37 data file includes a one-row header with titles for all data columns. Processing SBE-37 records utilized standard data processing packages provided by vendors. Some records provided by SBE-37 instruments required corrections of salinity in order to remove vertical instabilities. Each data row includes observations of temperature in-situ, conductivity, pressure, salinity, and date/time stamp, which contains day, month, year, hour, minutes, and seconds of measurements. All SBE-37 instruments used Greenwich Mean Time (GMT) for this time stamp.
DOI: 10.18739/a2hx15s0w
title: Arctic Observing Network (AON) observations from the 2018-2020 Beaufort shelf-edge mooring array

description: This submission contains data collected as a part of the 2018-2020 Beaufort shelf-edge mooring array, which was designed to investigate the manner in which the continental shelves communicate with the interior Arctic basin. Funding was provided by the National Science Foundation (NSF). This submission contains data from the BS3 mooring site, which has collected a consistent and detailed timeseries of physical oceanographic observations beginning in 2002 until present. Funding for this long-term data collection has been supported by the NSF Arctic Observing Network (AON). More information on the AON project can be found at http://aon.whoi.edu/.
DOI: 10.18739/a2d50fz7x
title: Watercraft inspections at the Alcan Port of Entry, 2017-2021

description: This spreadsheet includes watercraft inspection data collected by the United States Fish and Wildlife Service (USFWS) under the Lacey Act from 2017-2021. The USFWS conducted a literature review and initial assessment of the risk of dreissenid mussel introduction via watercraft entering the U.S. at the Alcan Port of Entry (one of Alaska’s primary international entry points) in 2008 and 2009 and provided recommendations for subsequent prevention actions. Since 2016, the USFWS has partnered with the United States Customs and Border Protection (USCBP), with technical input from State of Alaska agency representatives, to operate a watercraft inspection and decontamination station at the Alcan Port of Entry. Inspections occurred for a short period each year from 2017 until 2020 and for the entire summer in 2021. Dreissenid mussel incrusted watercraft have been encountered during this timeframe; fortunately, no live mussels have been detected. However, the risk of entry at other entry points or pathways has not been assessed due to lack of capacity and resources across partners, thus leaving lakes and rivers in Alaska susceptible to new aquatic invasive species (AIS) introductions. The data was collected by U.S. Fish and Wildlife Service staff. The data format and selection of variables was modified from a data collection initiative by the Western Invasive Species Coordinating Effort which is a subcommittee of the Western Regional Panel on Aquatic Nuisance Species: Building Consensus in the West Workgroup. More info can be found here http://westernregionalpanel.org/wp-content/uploads/2019/11/WRP-BC-Activity-Report-FINAL.pdf
DOI: 10.18739/a24q7qr4s
title: Ensemble Mean Reanalysis Soil Temperature Dataset at 1 degree latitude/longitude resolution (60 South to 90 North), 1981 - 2018.

description: This dataset accompanies Herrington, T., Fletcher, C. and Kropp, H. (in Review). The Cryosphere. It is a 1 degree ensemble mean soil temperature of the 8 reanalysis and LDAS products (based on CFSR, ERA5, ERA5-Land, ERA-Interim, GLDAS-CLSM, GLDAS-Noah, JRA-55 and MERRA2), averaged over the near surface (0 - 30 centimeter, cm) and at depth (30 cm - 300 cm), between 1981-2018. All reanalysis products were remapped using a conservative remapping technique to a resolution of 1 degree latitude and longitude, based on the GLDAS-CLSM land-mask. The ensemble mean is calculated as a simple arithmetic mean of all eight products at each timestep for all grid cells that include valid soil temperature measurements for all 8 products.
DOI: 10.18739/a2w669948
title: September-October Arctic Pacific Sectior Sea-ice Dipole Index Time Series, 1980-2016

description: These are the September-October Arctic Pacific Sector Sea-ice Dipole Index and April-May Barents Sea average Sea-ice Index used in Liang et al. (2021). This study used observational and reanalysis datasets in 1980–2016 to show a close connection between a boreal autumn sea ice dipole in the Arctic Pacific sector and sea ice anomalies in the Barents Sea during the following spring. The autumn sea ice dipole index is calculated based on the difference between the sea-ice concentration anomalies averaged over as the Beaufort-Chukchi Seas and the East Siberian-Laptev Seas. The Barents Sea sea ice index is the sea-ice concentration averaged over the Barents Sea region. The September–October Arctic Pacific sea ice dipole variations are highly correlated with the subsequent April–May Barents Sea sea ice variations (r=0.71). The strong connection between the regional sea ice variabilities across the Arctic uncovers a new source of predictability for spring Barents Sea sea ice prediction at 7-month lead time. Please find the detail definition of the indices from Liang et al. (2021). Liang, Y.-C., Y.-O. Kwon, and C. Frankignoul, 2021: Autumn Arctic Pacific Sea-ice Dipole as a Source of Predictability for Subsequent Spring Barents-Kara Sea-ice Condition. J. Climate., 34, 787-804. https://doi.org/10.1175/JCLI-D-20-0172.1.
DOI: 10.18739/a2mp4vp5w
title: Ambient air ozone concentrations measured in the University of Colorado container during the 2019-2020 MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition

description: This dataset contains ambient air ozone concentrations measured in the Central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The backbone of MOSAiC was the year-round operation of the Research Vessel Polarstern that drifted with the sea ice across the Central Arctic from October 2019 to September 2020. Measurements were performed onboard Polarstern in the University of Colorado sea-laboratory container installed below deck in the forward cargo hold. A ~50 meter (m) long Teflon sampling line was deployed from the container to the bow crane to allow measurements forward of the vessel. This dataset is an archive of minute-averaged ozone concentrations.
DOI: 10.5281/zenodo.6382893
title: Data for "Exemplifying parametric timed specifications over signals with bounded behavior"

description: Data for "<strong>Exemplifying parametric timed specifications over signals with bounded behavior</strong>" by Étienne André, Masaki Waga, Natuski Urabe, and Ichiro Hasuo, published in the proceedings of the 14th NASA Formal Methods Symposium (NFM 2022) See README.md in the zipped file for explanations and instructions for reproductibility.
DOI: 10.5281/zenodo.5834593
title: Data for "Exemplifying parametric timed specifications over signals with bounded behavior"

description: Data for "<strong>Exemplifying parametric timed specifications over signals with bounded behavior</strong>" by Étienne André, Masaki Waga, Natuski Urabe, and Ichiro Hasuo, published in the proceedings of the 14th NASA Formal Methods Symposium (NFM 2022) See README.md in the zipped file for explanations and instructions for reproductibility.
DOI: 10.5517/ccdc.csd.cc28byf0
title: CCDC 2095892: Experimental Crystal Structure Determination

description: Related Article: Christoph Müller, Jakob Gleixner, Maris-Johanna Tahk, Sergei Kopanchuk, Tõnis Laasfeld, Michael Weinhart, Dieter Schollmeyer, Martin U. Betschart, Steffen Lüdeke, Pierre Koch, Ago Rinken, Max Keller|2022|J.Med.Chem.|65|4832|doi:10.1021/acs.jmedchem.1c02033
DOI: 10.6084/m9.figshare.19410542
title: scores body components cuttlefish - Camou project

description: each component elicited by the experimental backgrounds was scored from 0 (not expressed) to 3 (intensively expressed). Fifteen cuttlefish (A2, B2, B3, B4, C1, C2, C3, D1, D2, D5, D6, D7, D8, D9, D10) were tested on 8 backgrounds:ug : uniform greysbs : small black squaresobs : outlined black squaresbg : black gridwb : white stripessws : small white squaresws : white squareswc : white crosses
DOI: 10.6084/m9.figshare.19412636
title: Uniq.bigWig

description: bigwig files
DOI: 10.6084/m9.figshare.19412636.v1
title: Uniq.bigWig

description: bigwig files
DOI: 10.6084/m9.figshare.19412639
title: Action Classification MNIST

description: Trajectories generated from the MNIST environment, see git repository for more details<br>Repository: https://github.com/BenedictWilkins/thesis-data<br>
DOI: 10.6084/m9.figshare.19412639.v1
title: Action Classification MNIST

description: Trajectories generated from the MNIST environment, see git repository for more details<br>Repository: https://github.com/BenedictWilkins/thesis-data<br>
DOI: 10.7303/syn28412421.1
title: Deletion of Abi3_Gngt2 influences age-progressive amyloid beta and tau pathologies in distinctive ways
DOI: 10.5281/zenodo.6382907
title: Elanoides forficatus SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6382906
title: Elanoides forficatus SPAdes preassembly

description: .
DOI: 10.5517/ccdc.csd.cc1z15y4
title: CCDC 1818396: Experimental Crystal Structure Determination

description: Related Article: Guohui Yin, Yan Li, Si Li, Bo Xu, Qiusheng Yang, Yuecheng Zhang, Jiquan Zhao, Xiaohui Cao|2022|Dyes Pigm.|198|110013|doi:10.1016/j.dyepig.2021.110013
DOI: 10.5517/ccdc.csd.cc23jn7k
title: CCDC 1952418: Experimental Crystal Structure Determination

description: Related Article: Yongyang Gong|2019|CSD Communication|||
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DOI: 10.5517/ccdc.csd.cc23jn8l
title: CCDC 1952419: Experimental Crystal Structure Determination

description: Related Article: Yongyang Gong|2019|CSD Communication|||
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DOI: 10.5517/ccdc.csd.cc26fd57
title: CCDC 2038658: Experimental Crystal Structure Determination

description: Related Article: Yahya Nural, Elize Karasu, Ergin Keleş, Burcu Aydıner, Nurgül Seferoğlu, Çağla Efeoğlu, Ertan Şahin, Zeynel Seferoğlu|2022|Dyes Pigm.|198|110006|doi:10.1016/j.dyepig.2021.110006
DOI: 10.5517/ccdc.csd.cc26k8vx
title: CCDC 2042399: Experimental Crystal Structure Determination

description: Related Article: Yuan Xu, Ying-Ying Zhou, Maoxing Yu, Yi Xiong, Xun-Gao Liu, Zujin Zhao|2022|Dyes Pigm.|198|109961|doi:10.1016/j.dyepig.2021.109961
DOI: 10.5517/ccdc.csd.cc27j936
title: CCDC 2071237: Experimental Crystal Structure Determination

description: Related Article: Mengshi Wang, Yuanheng Wang, Renjian Hu, Ruoxin Li, Zhigang Shuai, Yen Wei|2022|Dyes Pigm.|198|109939|doi:10.1016/j.dyepig.2021.109939
DOI: 10.5517/ccdc.csd.cc27j947
title: CCDC 2071238: Experimental Crystal Structure Determination

description: Related Article: Mengshi Wang, Yuanheng Wang, Renjian Hu, Ruoxin Li, Zhigang Shuai, Yen Wei|2022|Dyes Pigm.|198|109939|doi:10.1016/j.dyepig.2021.109939
DOI: 10.5517/ccdc.csd.cc27j958
title: CCDC 2071239: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc27v48j
title: CCDC 2080697: Experimental Crystal Structure Determination

description: Related Article: Yanli Li, Huijun Li, Ying Zhao, Zhaohang Wang, Yicheng Liu, Zhouqing Xu|2022|Dyes Pigm.|198|109926|doi:10.1016/j.dyepig.2021.109926
DOI: 10.5517/ccdc.csd.cc27xz5b
title: CCDC 2083422: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc27y05d
title: CCDC 2083453: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc27yysy
title: CCDC 2084371: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc284x5j
title: CCDC 2090087: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc28zz4d
title: CCDC 2115134: Experimental Crystal Structure Determination

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