cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22008/fk2/rucp1j
title: Data methods applied in the Greenland diamond exploration package. Ministry of Mineral Resources Report, Government of Greenland, 56 pp.

description: The Report describes the structure and controls in formulating the Government of Greenland’s Diamond exploration data package. The structure and methodologies of attribution meet international standards applied in diamond exploration. The database compiles over 50 years of diamond exploration data and expands beyond samples derived from Greenland’s established areas of diamondiferous rocks in central West Greenland to include a country-wide coverage of regional exploration data. The Diamond exploration data package is the first of its kind to collate diamond exploration data country-wide in a publicly accessible fashion. It incorporates the locations of 25 000 diamond exploration samples and over 109 000 good-quality chemical analyses of mineral separate grains integrated into a standardised framework. One hundred discrete, named in-situ bodies, which have diamond potential (kimberlites, lamproites, ultramafic lamprophyres, and carbonatites) are included. These occur among over 3 000 compiled in situ occurrences of dykes, pipes, sills and blows. With considerable data generated from bulk sampling of diamondiferous bodies, notably Garnet Lake, Qeqertaa and Majuagaa, this part of the database considerably expands upon previous compilations of relevant Greenland rocks. For chronological context, 56 emplacement age determinations from 36 bodies are also reported, encompassing most of the geographic extent of Greenland’s known rocks with diamond potential. The Report describes the application of mineral chemical classification, addresses assumptions in application of petrological terms and the challenges presented by reporting of non-standardised data from multiple sources. By describing the formulation of the data package, it is intended that the user is able to more intelligently query and apply the data package content to further research and exploration. Furthermore, the Report acts as a template for application in future diamond exploration activities elsewhere.
DOI: 10.22008/fk2/vyjx5d
title: Greenland diamond exploration data package

description: The Government of Greenland’s Diamond exploration data package compiles over 50 years of diamond exploration data. In addition to samples derived from Greenland’s established areas of diamondiferous rocks in central West Greenland, a wide coverage of regional exploration data extending throughout the country is included.The Diamond exploration data package is the first of its kind to collate diamond exploration data country-wide in a publicly accessible fashion. It incorporates the locations of 25 000 diamond exploration samples. Associated with these samples are over 109,000 good-quality chemical analyses of mineral separate grains integrated into a standardised framework. In total, 100 discrete, named in-situ bodies, which in principle have diamond potential (kimberlites, lamproites, ultramafic lamprophyres, and carbonatites) have also been compiled in the diamond exploration data package. These occur among over 3000 compiled in situ occurrences of dykes, pipes, sills and blows.Size: 8.7 GB.
DOI: 10.23729/39cfac4f-4d0d-4fb4-83dc-6f67e8ba8dce
title: ISUH waveform, time and location data products from stimulations of deep geothermal wells in Espoo in 2018

description: In summer 2018 the energy company St1 carried out its first stimulation of deep geothermal wells in Otaniemi, Espoo, in the Helsinki metropolitan area, southern Finland. The Institute of Seismology of University of Helsinki (ISUH) monitored the induced seismicity during the stimulation, and also months before and after it. This dataset consists of seismic waveforms of 254 induced earthquakes that occurred between 5 June 2018 to 8 August 2018, with local magnitudes between 0.5 and 1.8. Waveform, location and timing data have been produced at ISUH using seismic stations of the Finnish National Seismic Network (doi: 10.14470/UR044600), the temporary HEL broadband network, the temporary borehole network of St1 (doi: 10.1785/0220190253), and a pool of lightweight mobile seismic instruments operated by ISUH (GIPP data cubes, doi: 10.5880/GIPP.201802.1). The deployment is described in Hillers et al., 2020, The 2018 Geothermal Reservoir Stimulation in Espoo/Helsinki, Southern Finland: Seismic Network Anatomy and Data Features, doi:10.1785/0220190253. Event data, event metadata, and station metadata are provided in distinct directories. For event data, each event is assigned a subdirectory. Data formats follow generally accepted seismological standards.
DOI: 10.23729/6d15a5ea-7671-4bab-88a1-71f4ed962276
title: IMS waveform, time and location data products from stimulations of deep geothermal wells in Espoo in 2018

description: In summer 2018 the energy company St1 carried out its first stimulation of deep geothermal wells in Otaniemi, Espoo, in the Helsinki metropolitan area, southern Finland. The Institute of Seismology of University of Helsinki (ISUH) monitored the induced seismicity during the stimulation, and also months before and after it. In the second half of 2022 ISUH consulted the Australian company Institute of Mine Seismology (IMS, https://www.imseismology.org) for providing seismic location and time data based on ISUH waveform data, and the IMS algorithm that is based on machine learning (doi: 10.1785/0220210068). The IMS location and time data were later transferred to formats used by ISUH. The resulting dataset consists of seismic waveforms of 483 induced earthquakes that occurred between 5 June 2018 to 26 July 2018, with local magnitudes between -0.7 and 2.0. Seismic data derive from stations of the Finnish National Seismic Network (doi: 10.14470/UR044600) the temporary HEL broadband network, the temporary borehole network of St1 (doi: 10.1785/0220190253), and a pool of lightweight mobile seismic instruments operated by ISUH (GIPP data cubes, doi: 10.5880/GIPP.201802.1, ). The deployment is described in Hillers et al., 2020, The 2018 Geothermal Reservoir Stimulation in Espoo/Helsinki, Southern Finland: Seismic Network Anatomy and Data Features, doi:10.1785/0220190253. Event data, event metadata, and station metadata are provided in distinct directories. For event data, each event is assigned a subdirectory. Data formats follow generally accepted seismological standards. Due to an unfortunate miscommunication, the IMS amplitude readings in this data product need to be halved in order to be compatible with the ISUH 2018 dataset. This also has the effect of reducing the local magnitude values reported herein by ~0.25 units.
DOI: 10.23729/ee530ff5-a5cf-4c09-bb81-cba793d4d08b
title: A temporary array deployment of SmartSolo seismic sensors to monitor the 2020 EGS stimulation in Espoo/Helsinki, southern Finland

description: This repository contains continuous seismic data in miniseed format recorded in May and June 2020 in the Helsinki, Finland, area, to monitor the response to a second stimulation of a ~6 km deep enhanced geothermal system in the Espoo Otaniemi district that followed on the first larger stimulation in 2018. During the 2020 stimulation a total volume of 2600 cubic meter freshwater was pumped in several stages with maximum pumping pressures of 70 MPa, and the largest induced earthquake magnitude was ML 1.2. The provided data set was collected by 5-Hz three component SmartSolo instruments sampling at 500 Hz operated by the Institute of Seismology, University of Helsinki. The provided 43 stations are a subset of the full network that was operating during the 2020 stimulation. The full 2020 network and with it the deployment of the SmartSolo instruments is described in a Seismological Research Letter Data Mine Column by A. Rintamäki et al. This manuscript has been reviewed at the time of writing. The 2020 network is split into three subnetworks. The SmartSolo data are part of the OX subnetwork described by Rintamäki et al. (e.g., their Figs. 1, 3b). The locations of the SmartSolo stations are indicated in the provided html map that can be looked at variable scales in any web browser. (An accompanying data set of the OT subnetwork collected by short period CUBE stations from the Geophysical Instrument Pool Potsdam is available at https://doi.org/10.5880/GIPP.201925.1 .) The instruments were (mainly) powered by internal batteries. 33 of the 43 SmartSolo instruments were arranged in two nominally 17-station arrays that recorded in a 12-hour on/off cycle that was chosen to extend battery life. The two 180 m aperture, 17-sensor Storhemt (SH) and Munkkiniemi (MN) SmartSolo arrays were deployed 4.8 km and 2.7 km west and north-east of the injection site on a fallow agricultural field and in an open park area, respectively, configured in three circles of three, five, and eight sensors around a central station. The final MN data set consists of 16 stations. Four sensors were deployed on the Koirasaari island (KS array) and six in Lauttasaari (AS array). These were equipped with fence batteries to extend the recording period. The SmartSolo data set size is about 500 GB. The Otaniemi 2020 network can be understood in the context of seismic monitoring systems that evolve along with the different stages of an EGS. The data were collected to underpin a wide range of seismic analysis techniques for complementary scientific studies of the evolving reservoir processes and the induced event properties. These should inform the legislation and educate the public for transparent decision making around geothermal power generation.
DOI: 10.24381/cds.bd0915c6
title: ERA5 hourly data on pressure levels from 1940 to present
DOI: 10.24432/c54c8z
title: Land Mines
DOI: 10.24432/c5h30k
title: Seeds
DOI: 10.24432/c5w01b
title: Ionosphere
DOI: 10.48329/t89s-kf12
title: CPRD Aurum May 2022

description: The CPRD Aurum database contains longitudinal routinely-collected electronic health records (EHR data) from UK primary care practices using EMIS Web® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care. Access to anonymised data from CPRD is subject to a full licence agreement containing detailed terms and conditions of use. Anonymised patient datasets can be extracted for researchers against specific study specifications, following protocol approval. Further information is available at https://www.cprd.com/data-access.
DOI: 10.5066/f7d798h6
title: USGS HYDRoacoustic dataset in support of the Surface Water Oceanographic Topography satellite mission (HYDRoSWOT)

description: HYDRoSWOT HYDRoacoustic dataset in support of Surface Water Oceanographic Topography is a data set that aggregates channel and flow data collected from the USGS streamgaging network and includes 200,000+ records of USGS acoustic Doppler current profiler (ADCP) discharge measurements. The data set includes a variety of fields including: mean depth, mean velocity, discharge, stage, water-surface width, maximum depth, maximum velocity, and streamgage metadata for more than 5,000 stations in the United States. The hydraulic and channel geometry data is stored in the USGS hydroacoustic Doppler current profiler tables reported in the SiteVisit database and polled from 45 individual National Water Information System (NWIS) hosts. The database tables were de-normalized into a single flat file intended for further study in various analytical software, such as a spreadsheet or data frame. Each station can possess multiple measurements, as indicated by the measurement number recorded in the q_meas_no field. An individual measurement may have multiple sub-measurements when flow occurs in multiple channels. The multiple channels are recorded in the q_meas_chan_nu field. This dataset only contains ADCP measurement data. In the event of a multiple channel measurement, where other methods were used to measure flow, only ADCP measurements will be represented.
DOI: 10.5066/p14lu6cq
title: Model Predictions, Observations, and Annotation Data for Deep Learning Models Developed to Estimate Relative Flow at 11 Massachusetts Streamflow Sites, 2017-2024

description: This dataset consists of tabular data of observed streamflow, URL links to timelapse images, and deep learning model predictions for 11 sites in western Massachusetts. The dataset also includes a record of annotation data used to train the deep learning models. This data release is supporting information for an associated journal article describing the data collection, development of the deep learning models, and the interpretation of estimated relative streamflow produced by the models.
DOI: 10.5066/p9jz7ao3
title: National Land Cover Database (NLCD) 2021 Products

description: The U.S. Geological Survey (USGS), in partnership with several federal agencies, has now developed and released seven National Land Cover Database (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019, and 2021. Beginning with the 2016 release, land cover products were created for two-to-three-year intervals between 2001 and the most recent year. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. NLCD continues to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database. The NLCD 2021 release is update based, so the Land Cover and Impervious Surface products released in 2019 are unchanged and used directly with NLCD 2021 for change analysis though the NLCD timespan. Science products and the change index are updated and will need to be reacquired to contain the additional 2021 change. These new products use a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system. The overall accuracy of the 2019 Level I land cover was 91%. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2021 operational mapping (see https://doi.org/10.1080/15481603.2023.2181143 for the latest accuracy assessment publication). Questions about the NLCD 2021 land cover product can be directed to the NLCD 2021 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

Please see National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021) - ScienceBase-Catalog for the 2019 release of NLCD which is used with the 2021 release for comparisons through the years. 
Also refer to the larger NLCD Community page for all things NLCD related National Land Cover Database (NLCD) - ScienceBase-Catalog.
DOI: 10.5067/gmevbwflwa7x
title: IceBridge BedMachine Greenland, Version 5
DOI: 10.5281/zenodo.10621070
title: Kalman Smoother Sea Level Reconstruction

description: This dataset contains the sea-level fields for the new Kalman Smoother reconstruction submitted for publication under the title as "Probabilistic reconstruction of sea-level changes and their causes since 1900" by Soenke Dangendorf, Qiang Sun, Thomas Wahl, Philip Thompson, Jerry X. Mitrovica, and Ben Hamlington.

The dataset contains the fields of total sea level and individual components (KSSLfin.mat) and their errors (KSSL_SEfin.mat).

Master_Final.m is the master MATLAB script that performs the analysis of the data and also contains an explainer of main acronyms. Please note that you need the m_map toolbox to perform most of the plots (https://www.eoas.ubc.ca/~rich/map.html). All other sub-functions have been added, e.g., the Singular Spectrum Analysis originally published and written by Aslak Grinsted (Moore, J. C., Grinsted, A., & Jevrejeva, S. (2005). New tools for analyzing time series relationships and trends. Eos, Transactions American Geophysical Union, 86(24), 226-232).

Also included are datasets from the following publications:

Camargo, C. M., Riva, R. E., Hermans, T. H., & Slangen, A. B. (2020). Exploring sources of uncertainty in steric sea‐level change estimates. Journal of Geophysical Research: Oceans, 125(10), e2020JC016551.

Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., ... & Wu, Y. H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393-397.

Palmer, M. D., Domingues, C. M., Slangen, A. B. A., & Dias, F. B. (2021). An ensemble approach to quantify global mean sea-level rise over the 20th century from tide gauge reconstructions. Environmental Research Letters, 16(4), 044043.

WCRP(2018). Global sea-level budget 1993--present. Earth System Science Data, 10(3), 1551-1590.
DOI: 10.5281/zenodo.12825036
title: Dataset of PM10 Level and Predictors in Addis Ababa

description: The dataset for PM10 concentration prediction was compiled from 25 predictor variables and PM10 measurements from 11 monitoring stations in Addis Ababa. The final dataset consists of 38,408 observations spanning two years, from August 2021 to August 2023. The predictor variables encompass various factors impacting PM10 levels, including atmospheric conditions such as atmospheric density (AD), temperature (Ta), wind exposure (WEX), wind speed (WS), and rainfall (RF); urban features like night light (NL), land surface temperature (LST), urban index (UI), road density (RD), and building density (BD); terrain characteristics such as elevation (ELV), slope (SLP), topographic wetness index (TWI), terrain roughness index (TRI), and Soil-Adjusted Vegetation Index (SAVI); and proximity to potential PM10 sources including airports, petrol stations, roads, quarries, railways, markets, waste sites, bus stations, construction sites, and industries. This dataset facilitates the development of robust predictive models and supports the creation of effective air quality management strategies for Addis Ababa and other urban areas facing similar challenges.
DOI: 10.5281/zenodo.13119261
title: scMultiSim Benchmarking Datasets

description: This collection contains the datasets used in scMultiSim's paper.

Files starting with main_ are the main datasets; other files are names after the corresponding auxilluary dataset name, e.g., c.zip for dataset C.
DOI: 10.5281/zenodo.13882511
title: Dataset for Polarized Signatures of the Earth Through Time: An Outlook for the Habitable Worlds Observatory

description: The search for life beyond the Solar System remains a primary goal of current and near-future missions, including NASA's upcoming Habitable Worlds Observatory (HWO). However, research into determining the habitability of terrestrial exoplanets has been primarily focused on comparisons to modern-day Earth. Additionally, current characterization strategies focus on the unpolarized flux from these worlds, taking into account only a fraction of the informational content of the reflected light. Better understanding the changes in the reflected light spectrum of the Earth throughout its evolution, as well as analyzing its polarization, will be crucial for mapping its habitability and providing comparison templates to potentially habitable exoplanets. Here we present spectropolarimetric models of the reflected light from the Earth at six epochs across all four geologic eons. We find that the changing surface albedos and atmospheric gas concentrations across the different epochs allow the habitable and non-habitable scenarios to be distinguished, and diagnostic features of clouds and hazes are more noticeable in the polarized signals. We show that common simplifications for exoplanet modeling, including Mie scattering for fractal particles, affect the resulting planetary signals and can lead to non-physical features. Finally, our results suggest that pushing the HWO planet-to-star flux contrast limit down to 1 $\times$ 10$^{-13}$ could allow for the characterization in both unpolarized and polarized light of an Earth-like planet at any stage in its history.

This dataset contains all modeled spectropolarimetric signatures used to conduct this work. Each .zip file contains the output folders for one of the four eons: Hadean, Archean, Proterozoic, and Phanerozoic (i.e., Modern). Each of these top-level folders then contains individual folders for each specific model run. The names for each of these individual folders describes the specific model. Finally, each individual folder then contains files for the spectropolarimetric signatures, where each file contains four columns of data representing the spectra for that specific model at that specific planetary phase angle. Please refer to the README.txt as well as the accompanying manuscript for more information.
DOI: 10.5281/zenodo.14526360
title: GloHydroRes - a global dataset combining open-source hydropower plant and reservoir data

description:  
















Analyzing the impacts of drought and climate change on hydropower requires detailed data not only on hydropower attributes such as plant type, head, and installed capacity, but also on reservoir characteristics like area, depth, and volume. Current open-source hydropower datasets typically lack information on reservoirs, while reservoir datasets often omit hydropower details. GloHydroRes is a global dataset that integrates open-source hydropower and reservoir data, offering 29 attributes, including key information such as installed capacity, plant type, dam height, reservoir depth, area, volume, and river name. Overall, GloHydroRes provides data on 7,775 hydropower plants across 128 countries.
DOI: 10.5281/zenodo.14725145
title: Demand Starter Data Kit: Selected socio-economic and technical energy system demand modelling data for all 47 counties in Kenya.

description: This Demand Starter Data Kit contains data on population (total, urban, rural, growth), GCP (total, agriculture, construction, mining, manufacturing, services, energy, growth), county and national energy balances, energy intensities and electrification rates. This ‘starter kit’ of data aims to act as a starting point for teaching, capacity building efforts, and for further data collection, model development and scenario analysis to produce county resolution demand projections.

Counties included: Baringo, Bomet, Bungoma, Busia, Elgeyo-Marakwet, Embu, Garissa, Homa Bay, Isiolo, Kajiado, Kakamega, Kericho, Kiambu, Kilifi, Kirinyaga, Kisii, Kisumu, Kitui, Kwale, Laikipia, Lamu, Machakos, Makueni, Mandera, Marsabit, Meru, Migori, Mombasa, Murang’a, Nairobi City, Nakuru, Nandi, Narok, Nyamira, Nyandarua, Nyeri, Samburu, Siaya, Taita-Taveta, Tana River, Tharaka-Nithi, Trans Nzoia, Turkana, Uasin Gishu, Vihiga, Wajir, West Pokot.

Funding: This work was supported by the Climate Compatible Growth Programme (#CCG) and the UKPACT programme of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies.
DOI: 10.5281/zenodo.14740875
title: Perfect Coulomb drag in a dipolar excitonic insulator
DOI: 10.5281/zenodo.14995170
title: Review the role of oxygen-delivering nanobubbles in stroke therapy: A novel approach