cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/jsgh6u
title: DFO Pacific Shorekeepers Intertidal Survey – a community based project

description: The Shorekeepers survey is a citizen science protocol based project that enables community groups to collect scientifically defensible information about the intertidal and sub-tidal habitats and their biota in British Columbia. 
 
Shorekeepers is a rigorous monitoring methodology. The key to getting reliable scientific data is to use standard survey methods so that everyone is collecting data in the same way over time at all sites. A monitoring guide developed by Fisheries and Oceans Canada presents an intertidal surveying methodology. The method is presented in a logical step-by-step manner starting from selecting a study area through each type of measurement to entering the field data into the database, and finally, reporting the results. After a few days of training in habitat mapping and species identification, community members can use these step-by-step protocols to routinely monitor their site of interest or concern. The challenge is to distinguish natural changes from human-induced ones over time. Nevertheless, the resulting data will help us all to gain a better understanding of these dynamic ecosystems, and to make wiser decisions about coastal development and marine conservation.

To detect gradual changes to habitats and local biodiversity, we need long-term data sets. Shorekeepers is about collecting detailed data annually at the same site(s) for three or more years.

As our population grows in coastal areas, so may our impacts on marine habitats. Protection of this fragile environment requires active participation and monitoring by local people and communities. Does there appear to be less marine life on a particular beach in recent years? Do you suspect that a nearby activity or industry is affecting the plants and animals of a particular shoreline?

Shorekeepers is stewardship of coastal marine habitats by people who live near them. It is a monitoring methodology designed specifically for community groups and for people who may not have a strong science background to be able to contribute to scientific data collection. Having local community groups and individuals take an active role in looking after their coastline fosters "local ownership" and interest.

DFO maintains the central database with the complete dataset from all shorekeeper surveys on the BC coast. The OBIS dataset is a subset of this larger collection and includes the date, location and percentage coverage of the intertidal area by various taxa. Quantitative abundance and biomass values are not included in this dataset. Information on the tidal cycle and the slope of the intertidal area is included in the notes.

The OBIS dataset is a subset of the Shorekeepers collection and contains presence information for intertidal zone taxa. For the full dataset users should contact DFO.
DOI: 10.15468/dl.fg243r
title: Occurrence Download

description: A dataset listing the 5559 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 -30,180 -30,180 -10,-180 -10,-180 -30))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Mammalia"
 ]
}

 The dataset's 5559 records were derived from 671 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032008-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.949982
title: Chemical composition of Pacific manganese nodules and micronodules analyzed at Scripps Institution of Oceanography using wet chemistry

description: Buried manganese nodules and micronodules from 2 sites in the Pacific Ocean were analyzed at Scripps Institution of Oceanography using wet chemistry. Samples were ground to &lt; 74 microns, pressed into pellets and dried at 110 Degrees Centigrade.
description: From 1983 until 1989 NOAA-NCEI compiled the NOAA-MMS Marine Minerals Geochemical Database from journal articles, technical reports and unpublished sources from other institutions. At the time it was the most extended data compilation on ferromanganese deposits world wide. Initially published in a proprietary format incompatible with present day standards it was jointly decided by AWI and NOAA to transcribe this legacy data into PANGAEA. This transfer is augmented by a careful checking of the original sources when available and the encoding of ancillary information (sample description, method of analysis...) not present in the NOAA-MMS database.
DOI: 10.1594/pangaea.931869
title: Pan-Arctic monthly maps of sea surface height anomaly and geostrophic velocity from the satellite altimetry Cryosat-2 mission, 2011-2020

description: This data set was created with the aim of supporting observational studies of the sea level and surface ocean circulation variability in the Arctic Ocean. The data set was created in the frame of the Regional Atlantic Circulation and global changE (RACE) project. It consists of monthly maps of the Arctic sea surface height anomaly and absolute geostrophic velocity generated from sea surface height observations taken by the European Space Agency's satellite mission Cryosat-2. The dataset spans the period 2011-2020 and was generated by interpolation of satellite altimetry along-track observations onto a regular latitude-longitude grid of resolution 0.25°X0.75°. The sea surface height anomaly observations along the Cryosat-2 satellite tracks used in this work are, for the ice-covered ocean, data from the Alfred Wegner Institute (AWI version 2.4, ftp://ftp.awi.de/sea_ice/projects/cryoawi_ssh), and for the open ocean, data from the Radar Altimetry Database System (RADS, http://rads.tudelft.nl/rads/rads.shtml). The merged AWI+RADS along track data, processed as described in Doglioni et al., 2022 and used to generate the monthly maps, are included in the present file. The along track observations are interpolated on the output grid using the Data-Interpolating Variational Analysis tool (Troupin et al., 2012) with a decorrelation length of 50 km. Detailed methodology for the derivation and assessment of the dataset can be found in Doglioni et al., 2022 (under review). As auxiliary variables, the bin-average of along track sea surface height anomaly, the bin-standard deviation and the number of observations per bin are included.
DOI: 10.1594/pangaea.933475
title: Dynamic World training dataset for global land use and land cover categorization of satellite imagery

description: The Dynamic World Training Data is a dataset of over 5 billion pixels of human-labeled ESA Sentinel-2 satellite image, distributed over 24000 tiles collected from all over the world. The dataset is designed to train and validate automated land use and land cover mapping algorithms. The 10m resolution 5.1km-by-5.1km tiles are densely labeled using a ten category classification schema indicating general land use land cover categories. The dataset was created between 2019-08-01 and 2020-02-28, using satellite imagery observations from 2019, with approximately 10% of observations extending back to 2017 in very cloudy regions of the world. This dataset is a component of the National Geographic Society - Google - World Resources Institute Dynamic World project.The dataset consists of two file types: GeoTIFF files of 510x510 pixel 10m resolution satellite image tiles markup provided by human labelers, and Excel (.xlsx) tables of metadata and class statistics for the above GeoTIFF files. The data is organized into three main folders. One folder contains training data labeled by a team of 25 expert human labelers recruited by National Geographic Society specifically for this project. A second folder contains training data labeled by a larger group of commissioned labelers provided by a commercial crowd-labeler service. The data in these folders is organized by hemisphere and biome number from the RESOLVE Ecoregions2017 biomes categories (https://ecoregions2017.appspot.com/). A third folder contains a validation dataset. This is a holdout set of training data for assessing model accuracy. None of this data is intended to be used in the formulation of the model. Each validation tile was independently labeled by three experts. The validation set contains two versions: the individual markup from each expert labeler, and the image composites of the individual markups.Each GeoTIFF file encodes information on the location of landscape feature classes as determined by a given labeler. Classes were labeled by visual examination of true color (RGB) composites of Sentinel-2 MultiSpectral Level-2A scenes. The Tier 1 class values used in this phase of the project are as follows: 0 No data (left unmarked), 1 Water, 2 Trees, 3 Grass, 4 Flooded Vegetation, 5 Crops, 6 Scrub, 7 Built Area, 8 Bare Ground, 9 Snow/Ice, 10 Cloud. This dataset does not include the original Sentinel-2 imagery tiles, but metadata on the exact image ID and date is provided The original Sentinel-2 imagery was obtained via Google Earth Engine.This data is available under a Creative Commons BY-4.0 license and requires the following attribution: This dataset is produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute. Development of the Dynamic World training data was funded in part by the Gordon and Betty Moore Foundation.
DOI: 10.15468/dl.gyaeda
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Poa pratensis domestica, Poa pratensis eu-pratensis, Poa pratensis filifolia, Poa pratensis laxiflora, Poa pratensis macounii, Poa pratensis muralis, Poa pratensis planiculmis, Poa pratensis retroflexa, Poa pratensis sabulosa, Poa pratensis stricta)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021582-250711103210423/datasets/export for details.
DOI: 10.1594/pangaea.359789
title: Water temperature real-time profiles from cruise YDLR94 (SCQC)

description: SCQC=quality controlled by Science Center. For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.15468/dl.qdn8se
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hesperis hyrcana"
 ]
}

 The dataset includes 49 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021586-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cby9a5
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myletes duriventris"
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021583-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.958489
title: Bounty Trough, seismic profile AWI-20110010

description: Processed data (SEGD) of seismic reflection profile AWI-2011010 collected during project SOPATRA with RV Sonne cruise SO213 in 2011. Migrated data collected with 3000 m digital streamer, 4 GI-guns were used as seismic source every 25 m. See cruise report of SO213 and readme file 20110010_segd_info for further details on acquisition parameters.
DOI: 10.15468/dl.vcsfmz
title: Occurrence Download

description: A dataset containing 10530 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Desmodium canadense"
 ]
}

 The dataset includes 10530 records from 231 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021584-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.br7e6q
title: Occurrence Download

description: A dataset containing 46089 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is NZL.3_1",
 "TaxonKey is Fungi"
 ]
}

 The dataset includes 46089 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021585-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15949977
title: "The Fan Does Not Inhale: Core-Induced Inflow in the SIIEM Model"

description: https://www.youtube.com/watch?v=Izoh9gOB8Ek

This dataset originates from long-term observational experiments using an axially aligned dual-fan setup. Despite repeatedly watching the experimental video over several years, the airflow phenomena captured could not be explained by existing principles of fluid mechanics, tornado models, or the second law of thermodynamics.

Initially, the structure appeared to be a simple mechanical fan-driven flow. However, further analysis revealed that the fan was not the source of suction. Instead, a naturally formed rotational core was inducing the inflow. A crucial breakthrough occurred when the same experiment was observed from a side angle, where a short-tailed vortex and an inflow without any mechanical cause were clearly visible—contradicting conventional aerodynamic theories.

Through high-level analytical collaboration with AI and continued hypothesis testing, these structures were formalized into a new model: the Suction-Induced Inflow Extension Model (SIIEM). This led to the derivation of 12 new physical equations, quantitatively describing the core formation, energy convergence, rotational inflow, and core collapse mechanisms—none of which can be explained by classical models.

Importantly, this research identifies that the rotational flow model proposed by Dalal & Balachandar fundamentally conflicts with existing fluid dynamics, tornado theories, and thermodynamic laws. To resolve these contradictions, we propose 6 additional physical equations, offering a new, order-based framework for understanding natural flow structures beyond conventional mechanical assumptions.

This dataset includes:





Front and side view experimental videos




AI-based structural flow analysis




Annotated images of vortex formation and inflow behavior




Documentation of the 12 derived physical equations



This research proposes a paradigm shift from mechanism-driven science to a structure- and order-centered interpretation of nature. It represents one of the first cases where AI helped uncover hidden physical order not perceptible to human intuition, marking a foundational step toward a new frontier in physics and natural analysis.
DOI: 10.5281/zenodo.15949976
title: "The Fan Does Not Inhale: Core-Induced Inflow in the SIIEM Model"

description: https://www.youtube.com/watch?v=Izoh9gOB8Ek

This dataset originates from long-term observational experiments using an axially aligned dual-fan setup. Despite repeatedly watching the experimental video over several years, the airflow phenomena captured could not be explained by existing principles of fluid mechanics, tornado models, or the second law of thermodynamics.

Initially, the structure appeared to be a simple mechanical fan-driven flow. However, further analysis revealed that the fan was not the source of suction. Instead, a naturally formed rotational core was inducing the inflow. A crucial breakthrough occurred when the same experiment was observed from a side angle, where a short-tailed vortex and an inflow without any mechanical cause were clearly visible—contradicting conventional aerodynamic theories.

Through high-level analytical collaboration with AI and continued hypothesis testing, these structures were formalized into a new model: the Suction-Induced Inflow Extension Model (SIIEM). This led to the derivation of 12 new physical equations, quantitatively describing the core formation, energy convergence, rotational inflow, and core collapse mechanisms—none of which can be explained by classical models.

Importantly, this research identifies that the rotational flow model proposed by Dalal & Balachandar fundamentally conflicts with existing fluid dynamics, tornado theories, and thermodynamic laws. To resolve these contradictions, we propose 6 additional physical equations, offering a new, order-based framework for understanding natural flow structures beyond conventional mechanical assumptions.

This dataset includes:





Front and side view experimental videos




AI-based structural flow analysis




Annotated images of vortex formation and inflow behavior




Documentation of the 12 derived physical equations



This research proposes a paradigm shift from mechanism-driven science to a structure- and order-centered interpretation of nature. It represents one of the first cases where AI helped uncover hidden physical order not perceptible to human intuition, marking a foundational step toward a new frontier in physics and natural analysis.
DOI: 10.15468/dl.a6nnf3
title: Occurrence Download

description: A dataset containing 130 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Blumeria graminis",
 "Country is Australia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 130 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021588-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ws8jv2
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lepidium hickenii"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021587-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wnauju
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rorippa insularis"
 ]
}

 The dataset includes 53 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021589-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.980477
title: Vertical profiles of the stratospheric aerosol extinction coefficient from OMPS-LP limb-scatter measurements V2.1 (single profiles), 2012-2024

description: The data set contains single vertical profiles (Level 2 data) of the stratospheric aerosol extinction coefficient at a wavelength of 868.6 nm retrieved from the limb-scatter measurements made by the Ozone Mapper and Profiler Suite - Limb Profiler (OMPS-LP) instrument on the joint NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite mission. The data set contains V2.1 retrieval results and covers 2012-2024 period. The data were retrieved at the Institute of Environmental Physics (IUP) at the University of Bremen using the algorithm described by Rozanov et al. (2024). The vertical coverage of the profiles is 8.5 - 49.5 km, vertical sampling 1 km and vertical resolution is 2-3 km.
description: Data files contain stratospheric aerosol extinction profiles at 868.6 nm, corresponding noise error profiles, a priori information, vertical resolution, averaging kernels and auxiliary information (date, time, geolocation orbit number etc.) in a self-explaining NetCDF format. Names of the files contain the version number of the retrieval processor and the year of measurements.Acknowledgement: Computing time through Alliance to bundle resources of high performance computing power / Verein für Nationales Hochleistungsrechnen (NHR-Verbund), project nr. hbk00098.
DOI: 10.1594/pangaea.899091
title: AMSR-E ASI sea ice concentration data, Antarctic, version 5.4 (NetCDF) (June 2002 - September 2011)

description: The sea ice concentration product from the University of Bremen, Institute of Environmental Physics (IUP), has been retrieved with the ARTIST Sea Ice (ASI) algorithm, applied to microwave radiometer data of the sensor AMSR-E (Advanced Microwave Scanning Radiometer for EOS) on the NASA satellite Aqua. The data are gridded on a polar stereographic grid (EPSG code 3412, Antarctic) with 6.25 km grid resolution. After the failure of AMSR-E in 2011, the ASI algorithm has been applied to the data of the sensor AMSR2 (Advanced Microwave Scanning Radiometer 2) on the JAXA satellite GCOM-W1 (see the datasets of sea ice concentration from AMSR2). This means that sea ice concentration data have been almost continuously produced since 2002, using AMSR-E data from 2002 until 2011 (this data set), and AMSR2 data since 2012. As several details of the processing chain have changed over the years, in 2018, all ASI ice concentration data for the Arctic and Antarctic based on AMSR-E and AMSR2 have been reprocessed with exactly the same parameters, settings and software. The result are ASI data, version 5.4. The details are explained in the ASI User Guide (https://seaice.uni-bremen.de/fileadmin/user_upload/ASIuserguide.pdf).
description: PLEASE NOTE:In the summer months, masking out of spurious sea ice (which can be caused by weather effects) was not correctly applied. The masking is described in section 3.5 "Maximum ice extent masking" of the ASi User Guide, https://seaice.uni-bremen.de/fileadmin/user_upload/ASIuserguide.pdf As a result, spurious sea ice can show up in summer in regions that are ice.-covered in winter.A newer version of this data is available, please see "Replaced by" reference.
DOI: 10.1594/pangaea.757729
title: Abundance of plankton in the upper 200 m layer in tropical oligotrophic regions of the Pacific and Indian Oceans

description: Quantitative distribution of plankton (mostly mesoplankton) is studied in the upper 200 m layer of oligotrophic waters in tropical anticyclonic gyres of the Pacific and Indian Oceans. Some general features of its trophic and taxonomic structures and vertical distribution are described.
description: Supplement to: Vinogradov, Mikhail E; Musaeva, Eteri I (1989): Peculiarities of plankton distribution in oligotrophic tropical ocean regions. Oceanology, 29(3), 375-381
DOI: 10.15468/dl.jc98h3
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ammodendron bifolium)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 87 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021590-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.943586
title: South Pacific Convergence Zone (SPCZ) Titanium data

description: SPCZ Titanium data from Lake Lanoto'o on 'Upolu (Samoa), and Lake Emaotul on Efate (Vanuatu)