cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18739/a21c1th66
title: Winter snow depth as a driver of microbial activity, nutrient cycling, tree growth and treeline advance in the Arctic

description: Overview:
 It has long been thought that temperature exerts a direct control on growth of treeline trees and the position of the treeline. However, our recent work in the Arctic with white spruce suggests that indirect effects of temperature on soil nutrient availability may be of equal or greater importance. We hypothesize that cold soils at the treeline, particularly during winter, limit microbial activity and nutrient availability to the point where trees are barely able to survive and grow. Measurements made during winter have revealed that Arctic forests maintain snowpacks that are much deeper than observed at treeline. Trees are thought to trap snow and lead to a deeper snowpack, insulating the soil from cold air and allowing for greater overwinter microbial activity and greater nutrient mineralization. Indeed, we found a strong positive correlation between white spruce growth and winter snow depth. We propose to isolate the mechanisms underlying this correlation by using snowfences to manipulate winter snow depth and fertilizer to increase soil nutrient availability at three treelines that differ in soil moisture. To provide an experimental test of the importance of temperature as a direct control on treeline tree growth, we propose to incorporate experimental shoot warming into our snowfence experiment in a factorial design. We predict that both experimental snow and nutrient additions will lead to large increases in microbial activity, photosynthesis, tree growth, seed quality, seed production, seedling establishment and recruitment of new trees. We expect to observe the greatest positive responses where soils are wet and cold. Meanwhile, we predict that shoot warming will lead to negligible changes in growth. This research will elucidate the relationship between snow depth and soil nutrient availability, and determine the relative importance of nutrient and temperature limitations at treeline to white spruce – a dominant member of the boreal forest and the northernmost tree species in North America.
 Intellectual Merit:
 The position of the Arctic treeline is an important regulator of surface energy budgets, carbon cycling and subsistence resources in high latitude environments. Our recent research provides correlative evidence of the importance of winter snow depth as a driver of tree growth. Here, we propose to experimentally isolate the importance of snow depth and soil nutrient availability and examine the consequences for microbial processes and tree performance. This is a novel proposal, as we aim to link microbial ecology with large-scale landscape patterns of relevance to regional and global climates. If our hypotheses are confirmed, our findings will contradict the prevailing theory of treeline causation, alter our predictions of where and when treelines may advance and necessitate revision of the many paleoclimate reconstructions based upon correlations between temperature and tree growth near the Arctic treeline.
DOI: 10.18739/a21g0hw9h
title: Longpath Differential Optical Absorption Spectroscopy (LP-DOAS) observations of vertical trace gas profiles in Fairbanks, Alaska, during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) experiment (2022)

description: The Alaskan Layered Pollution And Chemical Analysis (ALPACA)-2022 field study (https://alpaca.community.uaf.edu/alpaca-field-study/) investigated air pollution under dark and cold conditions in Fairbanks, Alaska in January and February, 2022. One of the main motivations for ALPACA was to understand how temperature inversions trap pollutants at the surface. This was studied by using University of California, Los Angeles (UCLA) long-path Differential Optical Absorption Spectroscopy instrument (LP-DOAS) to probe the Fairbanks atmosphere from 12 meters(m) – 191m altitude and yield information on the vertical distribution of various trace gases / pollutants.
 The dataset contains the open atmosphere LP-DOAS measurement of Ozone (O3), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Formaldehyde (HCHO), and Nitrous Acid (HONO) on four different light paths over wintertime downtown Fairbanks, Alaska (AK) during ALPACA. The four light paths cover the following altitude intervals: 12-17m, 17-73m, 17-115m, and 17-191m. The ReadMe file included in the data set provides exact coordinates, length, and altitude intervals for the four light paths.
DOI: 10.18739/a21j9795r
title: Conductivity-Temperature-Depth (CTD) and Geochemistry Data from 2024 Expedition of the Canadian Coast Guard Ship Louis S. St-Laurent to the Beaufort Gyre

description: Contains data from a hydrographic cruise conducted about the aboard the Canadian Coast Guard Ship (CCGS) Louis S. St-Laurent, August 29th to September 26th, 2024 under the Beaufort Gyre Observing System (BGOS)/Joint Ocean Ice Study (JOIS). Data were collected under the National Science Foundation (NSF) award "AON (Arctic Observing Network) Collaborative Research: Continuation of long-term Beaufort Gyre observations in 2020-2024 to enhance understanding of the Arctic's role in climate variability" in collaboration with researchers from Fisheries and Oceans Canada at the Institute of Ocean Sciences (Paul Macoun, Bill Williams and Sarah Zimmermann, Institute of Ocean Sciences).
 There were 61 conductivity temperature depth (CTD) casts performed using a SBE911+ CTD system with 24 10-Liter (L) Niskins. The CTD data for down casts are provided in a single netcdf file as 1-decibar (db) averaged profiles. Cruise documentation is available as a text file. Geochemistry data are provided in a separate excel spreadsheet.
DOI: 10.18739/a21n7xp57
title: ERA5 monthly averaged data on single levels from 1940 to present

description: ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis.
 Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.
 Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 monthly averaged data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.f17050d7 (Accessed on DD-MMM-YYYY)
DOI: 10.18739/a21n7xp6q
title: Arctic Lake CF8 Glycerol Dialkyl Glycerol Tetraether (GDGT) and Fatty Acid Data during the Early Holocene

description: Changes in ice-sheet size impact atmospheric circulation, a phenomenon documented by models but constrained by few paleoclimate records. We present sub-centennial-scale records of summer temperature and summer precipitation hydrogen isotope ratios (δ2H) spanning 12–7 kiloannum (ka) from a lake on Baffin Island. In a transient model simulation, winds in this region were controlled by the relative strength of the high-pressure systems and associated anticyclonic circulation over the retreating Greenland and Laurentide ice sheets. The correlation between summer temperature and precipitation δ2H proxy records changed from negative to positive at 9.8 ka. This correlation structure indicates a shift from alternating local and remote moisture, governed by the two ice-sheet high-pressure systems, to only remote moisture after 9.8 ka, governed by the strong Greenland high-pressure system after the Laurentide Ice Sheet retreated. Such rapid atmospheric circulation changes may also occur in response to future, gradual ice-sheet retreat.
DOI: 10.18739/a21r6n28z
title: glaciome1D: python code for modeling ice mélange flow, 2024

description: glaciome1D is a quasi-one-dimensional continuum model for modeling the flow of dense packs of icebergs (ice mélange) found in some fjords. In many respects the model is similar to one-dimensional models of ice streams and ice shelves, except that it uses the nonlocal granular fluidity rheology of Henann and Kamrin (2013). The model was created with the intention of developing coupled glacier-ocean-melange models. This is reflected in the modeling framework, which mimics that used for ice streams and ice shelves (Schoof, 2007).
 Using the model involves creating an instance of the glaciome class, which contains information on the glacier velocity, viscosity (granular fluidity), and geometry as well as model parameters and various external forcings. The glaciome class includes several basic and easy to use functions, such as: self.diagnostic(), self.prognostic(), self.steadystate(), self.save().
 The model physics and numerics are described in detail in Amundson et al. (in press).
 This data set includes a single python module that includes all of the functions for setting up and running the model, an example script that runs the model, and a conda environment list that contains python modules that the code has been tested on.
DOI: 10.18739/a21v5bg3v
title: Surface Elevation, Snow, Ice, and Depth Measurements along a transect at Teshekpuk Lake, Alaska on 10 May 2023

description: This dataset contains detailed measurements of snow depth, ice thickness, lake depth, freeboard, and surface elevation collected along a ~25-kilometer transect at Teshekpuk Lake, Alaska, on 10 May 2023. These observations were gathered as part of the Calibration and Validation (Cal/Val) activities in support of NASA’s Surface Water and Ocean Topography (SWOT) mission, which aims to provide high-resolution observations of Earth’s surface water dynamics, and done in collaboration with the National Science Foundation (NSF)-funded PermaSense project and the Teshekpuk Lake Observatory. The transect spans a gradient from terrestrial tundra to bedfast to floating lake ice environments, capturing the spatial variability of late-winter snow and ice conditions across the largest Arctic lake in Alaska. Measurements collected along the transect include Global Positioning System (GPS)-based surface elevation (World Geodetic System 1984 (WGS84) ellipsoid height), transect distance relative to the lake shore, snow depth, ice thickness, lake depth, freeboard, ice type, snow density, and snow water equivalent, along with site-specific observational notes. These in situ measurements are critical for evaluating SWOT’s Ka-band Radar Interferometer (KaRIn) observations over ice-covered freshwater systems during the Arctic spring. The dataset offers valuable insights into snow and ice processes on Arctic lakes and serves as a key resource for improving remote sensing algorithms for ice-covered water bodies.
DOI: 10.18739/a21z41v5p
title: Measurements of ice mass balance and temperature from autonomous Seasonal Ice Mass Balance (SIMB3) buoys deployed in the Central Arctic Ocean on the ArcWatch expedition in 2023

description: The dataset contains measurements made from three autonomous Seasonal Ice Mass Balance (SIMB) buoys. The buoys were deployed in the late summer of 2023 as part of the Alfred Wegener Institute ArcWatch Project. Each buoy measured position, barometric pressure, air temperature, and a vertical profile of temperature from the air, through the snow, through the ice, and into the upper ocean. An above ice downward looking sounder tracked the position of the surface and an underwater upward looking sounder tracked the position of the ice bottom. Sensors were placed every 0.02 meters (m) on the vertical temperature string. Measurements were made every four hours and results were transmitted via Iridium. There is one csv file for each SIMB.
DOI: 10.15468/dl.xa7rr8
title: Occurrence Download

description: A dataset containing 5257 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Leptonycteris yerbabuenae",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 }
 ]
}

 The dataset includes 5257 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003829-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hpk2d7
title: Occurrence Download

description: A dataset containing 911 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United States of America",
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Agriades glandon"
 ]
}

 The dataset includes 911 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003830-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sah9s6
title: Occurrence Download

description: A dataset containing 105 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lichonycteris obscura",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 }
 ]
}

 The dataset includes 105 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003833-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18441852
title: De Novo Peptide Design against Human PPAR-gamma (PDB: 2PRG) using JarvisAutoSearchV6- ARES AI-Driven P-Room Screening Architecture. IN SILICO

description: This dataset contains the results of a large-scale computational screening of short peptides (3 to 8 amino acids) targeting the ligand-binding domain of the PPAR-gamma protein (PDB: 2PRG).

The methodology utilizes JarvisAutoSearch - ARES framework. This system performs real-time pre-selection of high-potential candidates, which are subsequently validated through physical molecular docking using AutoDock Vina. Each peptide sequence was computationally translated into a 3D atomic coordinate set to allow for all-atom flexible docking simulations.Current Leading Candidate: In the provided results, the sequence NIMMDQ stands out as the primary lead, achieving a validated binding affinity of NIMMDQ (-7.12) kcal/mol.SMILES : CC[C@H](C)[C@H](NC(=O)[C@H](CC(N)=O)N)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCC(N)=O)C(=O)O

Disclaimer: ARES Peptide Evolution Logic

The Peptide Blueprints: The molecular structures (SMILES) and amino acid sequences provided in this report may exhibit structural variations or non-standard side-chain geometries compared to classic academic peptide databases. This is a deliberate architectural choice of our AGI-driven pipeline, designed to explore the vast "dark matter" of the chemical space beyond the 20 standard canonical residues.

Structural Dynamics Route: To maintain high-speed evolution and prevent system crashes during the simulation of complex protein-ligand interfaces, the current ARES engine utilizes a "Geometry Proxy" methodology. This allows the AI to prioritize binding energy and spatial fit over traditional chemical nomenclature.

Intended Behavior: These non-standard notations or "exotic" sequences are not bugs; they are functional placeholders. They allow the AGI to maintain physical stability and high-affinity binding during high-throughput evolution cycles. They represent "best-fit" solutions found by the engine within the 3D pocket of the 2PRG protein.

Transparency First: This release prioritizes raw discovery. We choose to showcase the model’s current strengths unbiased multi-domain exploration and its known weaknesses academic formatting of SMILES rather than filtering out potentially groundbreaking unconventional inhibitors.

We are pioneering a path where AI logic meets physical reality. These results represent a bridge between raw silicon-based discovery and future laboratory synthesis. Your feedback is essential for the evolution of this path.Contribute to further research & development: > https://ko-fi.com/jarvisautosearch_ares
DOI: 10.5281/zenodo.18438249
title: De Novo Peptide Design against Human PPAR-gamma (PDB: 2PRG) using JarvisAutoSearchV6- ARES AI-Driven P-Room Screening Architecture. IN SILICO

description: This dataset contains the results of a large-scale computational screening of short peptides (3 to 8 amino acids) targeting the ligand-binding domain of the PPAR-gamma protein (PDB: 2PRG).

The methodology utilizes JarvisAutoSearch - ARES framework. This system performs real-time pre-selection of high-potential candidates, which are subsequently validated through physical molecular docking using AutoDock Vina. Each peptide sequence was computationally translated into a 3D atomic coordinate set to allow for all-atom flexible docking simulations.Current Leading Candidate: In the provided results, the sequence NIMMDQ stands out as the primary lead, achieving a validated binding affinity of NIMMDQ (-7.12) kcal/mol.SMILES : CC[C@H](C)[C@H](NC(=O)[C@H](CC(N)=O)N)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCC(N)=O)C(=O)O

Disclaimer: ARES Peptide Evolution Logic

The Peptide Blueprints: The molecular structures (SMILES) and amino acid sequences provided in this report may exhibit structural variations or non-standard side-chain geometries compared to classic academic peptide databases. This is a deliberate architectural choice of our AGI-driven pipeline, designed to explore the vast "dark matter" of the chemical space beyond the 20 standard canonical residues.

Structural Dynamics Route: To maintain high-speed evolution and prevent system crashes during the simulation of complex protein-ligand interfaces, the current ARES engine utilizes a "Geometry Proxy" methodology. This allows the AI to prioritize binding energy and spatial fit over traditional chemical nomenclature.

Intended Behavior: These non-standard notations or "exotic" sequences are not bugs; they are functional placeholders. They allow the AGI to maintain physical stability and high-affinity binding during high-throughput evolution cycles. They represent "best-fit" solutions found by the engine within the 3D pocket of the 2PRG protein.

Transparency First: This release prioritizes raw discovery. We choose to showcase the model’s current strengths unbiased multi-domain exploration and its known weaknesses academic formatting of SMILES rather than filtering out potentially groundbreaking unconventional inhibitors.

We are pioneering a path where AI logic meets physical reality. These results represent a bridge between raw silicon-based discovery and future laboratory synthesis. Your feedback is essential for the evolution of this path.Contribute to further research & development: > https://ko-fi.com/jarvisautosearch_ares
DOI: 10.5281/zenodo.18445295
title: SuperDARN data in netCDF format (2009-Oct)

description: 2009-Oct SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6576853
title: SuperDARN data in netCDF format (2009-Oct)

description: 2009-Oct SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.z3pc42
title: Occurrence Download

description: A dataset containing 153 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lonchophylla concava",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 }
 ]
}

 The dataset includes 153 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003835-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18445862
title: Data used for figures in diffpy.morph manuscript
DOI: 10.5281/zenodo.18445863
title: Data used for figures in diffpy.morph manuscript
DOI: 10.15468/dl.g44jre
title: Occurrence Download

description: A dataset containing 9450 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Arctic Species Trend Index (ASTI) : Freshwater"
 ]
}

 The dataset includes 9450 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003836-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.