cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5067/27a0p5m6lzbi
title: Lagrangian Snow Distributions for Sea-Ice Applications, Version 1
DOI: 10.5067/acytyzb9beos
title: MODIS/Terra Snow Cover 5-Min L2 Swath 500m, Version 5
DOI: 10.5067/aqua/airs/data321
title: Aqua AIRS Level 3 Standard Monthly Product using AIRS IR-only V6
DOI: 10.5067/ltvb4gpcotk2
title: MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio V5.12.4
DOI: 10.5067/measures/so2/data405
title: Multi-Satellite Volcanic Sulfur Dioxide L4 Long-Term Global Database V4
DOI: 10.5067/mpyg15waa4wx
title: Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 2
DOI: 10.5067/seabass/otz_whoi/data001
title: OTZ_WHOI
DOI: 10.5281/zenodo.10074413
title: Coupled Thermosphere-Ionosphere Tongue-like Structure During the Recovery Phase of the Geomagnetic Storm on May 12, 2021

description: The file named 'Indices' includes Kp, F10.7p, By and Bz indices, AE and Dst indices, which are used to plot Figure 1. The file named 'GOLD_131',  'GOLD_132' and 'GOLD_133' include the parameters of O/N2 and temperature from GOLD observations, which are used to plot for Figures 2 and S1. The file named 'GPS_131',  'GPS_132' and 'GPS_133' include the parameters of TEC from GPS observations, which are used to plot for Figures 4 and S2. The file named 'TIEGCM_131' and 'TIEGCM_133' includes O/N2, Temperature, horizontal winds, TEC and diagnostic analysis terms of O+ density from the TIEGCM simulations on DOY 131 and 133 in 2021, which are used to plot for Figures 3 and 4, Movie S1 and S2.
DOI: 10.5281/zenodo.10281549
title: PB260 draft assembly

description: Draft assembly of Hevea brasiliensis (Mull.Arg.) clone PB260. Obtained in the frame of RUBIS project rubis-project.org
DOI: 10.5281/zenodo.10429216
title: Coupling deep learning and physically-based hydrological models for monthly streamflow predictions

description: Revision in journal Water Resources Research, Manuscript number: 2023WR035618R

Abstract: This study proposes a new hybrid model for monthly streamflow predictions by coupling a physically-based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. It is then driven by bias corrected general circulation model (GCM) predictions to generate soil moistures for the forecasting months. Finally, model-simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future monthly streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) as the DL model, is applied to predict 1-, 3-, and 6-month ahead reservoir inflows for the Danjiangkou Reservoir in China. The results show that the hybrid model consistently performs better than VIC and CNN-GRU models with great improvement in Kling‐Gupta efficiency (KGE) values for lead times up to 6 months. Additional tests indicate that hybrid models based on CNN-GRU outperform those based on LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model greatly reduces the computation burden of rolling prediction. It also saves decision-makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. Overall, the new hybrid model demonstrates great potential for monthly streamflow prediction where training data are limited.

Keywords: monthly streamflow prediction; deep learning; physically-based distributed hydrological model; VIC model; soil moisture; hybrid model
DOI: 10.5281/zenodo.11241132
title: Challenges in submarine fiber-optic earthquake monitoring

description: This dataset contains three subsets of data from a Distributed Acoustic Sensing (DAS) experiment near Santorini, Greece. The file named microseisms* contains 10 minutes of data during a period of strong microseismic activity. The two files named earthquake* contain the data for two seismic events underneath the Kolumbo volcanic chain.
DOI: 10.5281/zenodo.11388020
title: Data sets used in Lee et al. (2024)

description: Data sets used in the following article (submitted):

 

Hoontaek Lee, Martin Jung, Nuno Carvalhais, et al. Spatial attribution of temporal variability in global land-atmosphere CO2 exchange using a model-data integration framework.  ESS Open Archive .
DOI: 10.5281/zenodo.11669623
title: Future wave climate in the Mediterranean Sea and associated uncertainty from an ensemble of 31 GCM-RCM wave simulations

description: The data provided is used in a study aimed at assessing future changes in the Mediterranean wave climate. A total of 31 GCM-RCM simulations were used to characterize the wave climate during the historical (1979-2005), mid-century (2034-2060), and end-century (2074-2100) periods. Changes in seasonal significant wave height (Hs) and peak period (Tp) wave parameters are evaluated for both the mean and intense (quantile 0.95) wave climate, along with the shift in wave direction (wave peak dominant direction, θp) for sea states characterized as intense. The robustness of the climate change signal is evaluated following the guidelines outlined in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change. Additionally, using a wave hindcast as a reference, the changes in future extreme events are assessed by fitting a GEV to a unique and coherent set of bias-corrected annual maxima from each model.

We provide data used to obtain the results of our study, comprising: 1) wave climate statistics for Hs, Tp, and θp for each model and each period studied; 2) two sets of annual maxima distribution for each model, which were bias-corrected assuming that the set of extreme events follows either a Gumbel distribution or a GEV distribution. For more details on the methods to obtain these files describing wave climate statistical as used in the study, please refer to Toomey et al., 2024: "Future wave climate in the Mediterranean Sea and associated uncertainty from an ensemble of GCM-RCM wave simulations."
DOI: 10.5281/zenodo.12760731
title: m6Am landscape of human cell lines

description: Tables of m6Am profiling results by CROWN-seq. 

Cell lines in included in this version are:



HEK293T (wild-type, PCIF1 KO, FTO KO)

A549

HepG2

Huh-7

CCD841 CoN

HT-29

HCT-116

K562

Jurkat E6.1


ReCappable-seq data for HEK293T and A549 (WT and PCIF1 KO) are also included.

Description of the table columns can be found in README.md.
DOI: 10.5281/zenodo.12783561
title: Arctic and Antarctic sea ice thickness climate data record from ERS-1, ERS-2, Envisat and CryoSat-2
DOI: 10.5281/zenodo.13359622
title: Materials for "Revisiting the Relationship between Induced Polarization and Surface Conductivity: Ratios from Laboratory to Field"

description: These files are for the work "Revisiting the Relationship between Induced Polarization and Surface Conductivity: Ratios from Laboratory to Field". 

They include two parts of Experiments and Simulations, details of each part being explained in the Readme.txt therein. 

Through these materials, results presented in this work can be independently validated.
DOI: 10.5281/zenodo.13495838
title: Lower latitude boundaries from AMPERE and SuperDARN

description: Combined SuperDARN and AMPERE boundaries (\Lambda_{Walach} and \Lambda_{Fogg}), respectively for the events described by Walach et al. (submitted to JGR: Space Physics), "Reliability of Matching AMPERE Field-Aligned Current Boundaries with SuperDARN Lower Latitude Ionospheric Convection Boundaries During Geomagnetic Storms": 18:00UT on 19 January 2016 to 18:00 UT on 21 January 2016; 20:20 UTon 19 December 2015 to 20:20 UT on 21 December 2015; 20:10 UTon 14 July 2012 to 20:10 UT on 16 July 2012.

 

The SuperDARN boundaries (file format: *_maria_hmb.txt) are in the format: year, month, day, hour, minute, second, HMB at midnight [geomagnetic latitude], n (the number of SuperDARN vectors in the map) and SYM-H. 

The AMPERE boundaries (file format: *.amphmb) are in the format: year, month, day, hour, minute, second, HMB at midnight [geomagnetic latitude].

All files are in ASCII format.
DOI: 10.5281/zenodo.13959654
title: OpenFE 1.0rc release: Benchmarking results

description: These are benchmarking results of the OpenFE 1.0rc release. Deposited are the calculated differences in binding free energy for ligands from 15 datasets from the Protein Ligand Benchmark set. The DDG values can be found in the "csv_files" folder, plots of the calculated DDG and DG values against the experimental data can be found in the folder "plots".

Input structures for the benchmark were taking from the protein ligand benchmark repository: 

https://github.com/openforcefield/protein-ligand-benchmark

Settings used in this benchmark: 



Kartograf atom mapper

Minimal spanning networks 

allowing element changes

Partial charge assignment using OpenEye ELF10 (user charges)

Small molecule forcefield: OpenFF 2.1


The ligand networks (including mappings and partial charges) that were run to obtain these results can be found here: 

https://zenodo.org/records/13165855
DOI: 10.5281/zenodo.14232044
title: Data for the Emerging Class of Double-Faced White Dwarfs

description: All polar-cap model fits to our time-resolved optical spectra of J0847+4842 and J0856+161 saved as pdfs, and fits files for all observations. The fits files are in zipped folders and named based on the target, observatory, and date taken. (arxiv:2501.05649)
DOI: 10.5281/zenodo.14713485
title: TCNN_model_dataset

description: The repository contains the code for the ML model TCNN.

final_code_no3_with_reg.py  contains data processing and calculation of correlation maps

final_code_TCNN-IG.ipynb contains instead the model applied to observations and model output with the integrated gradient case

final_code_TCNN-SHAP.ipynb contains instead the model applied to observations and model output with the  gradient shap case
DOI: 10.5281/zenodo.14940052
title: The North American Greenhouse Gas Budget: emissions, removals, and integration for CO2, CH4, and N2O (2010-2019): Results from the Second REgional Carbon Cycle Assessment and Processes Study (RECCAP2)

description: The dataset (in excel format) provides the annual (2000-2019) land and aquatic emissions and removals of greenhouse gases for all anthropogenic, managed, and natural sectors for North America, and separately for Canada, United States, Mexico, Central America and the Caribbean. The dataset includes the calculation of the total CO2-equivalent fluxes and the carbon budget used in the wiring diagrams of the accompanying publication.
DOI: 10.5281/zenodo.14946720
title: Remote sensing measurements of the nighttime D-region ionosphere based on very low frequency tweek observations in China

description: Figures, data, and code used in my paper describing "Remote sensing measurements of the nighttime D-region ionosphere based on very low frequency tweek observations in China"
DOI: 10.5281/zenodo.15110481
title: Was the Lhasa Block at Low Latitudes in the Middle Permian? Insights From New Paleomagnetic and Geochronological Data
DOI: 10.5281/zenodo.4278477
title: Data and Models for "Coevolving Early Afterslip and Aftershock Signatures of a San Andreas Fault Rupture"

description: The original GPS solutions are processed and archived at the Scripps Orbit and Permanent Array Center (SOPAC) (http://sopac-csrc.ucsd.edu/). You can access the following FTP directory (username: “anonymous”; password: your email) to obtain high-rate (1Hz) GPS solutions for the Parkfield earthquake: http://garner.ucsd.edu/pub/measuresESESES_products/EarthquakeDisplacements/Parkfield_20040928/GNSS/JiangBockKlein2021 The archive contains the following datasets and modeling files: <strong>1. High-rate (1Hz) GPS solutions with sidereal filtering</strong> The post-processed high-rate GPS solutions for 13 stations from September 22 to October 5, 2004 are provided in separate files. The four columns are for the time (in seconds) relative to the Parkfield earthquake initiation time (17:15:24 UTC, September 28, 2004), the east, north, and vertical components (in mm) of the displacements. “NaN” values indicate data gaps or removed outliers. <strong>2. Daily GPS solutions with corrections for the 2003 San Simeon Earthquake</strong> The post-processed daily solutions for 13 stations up till May 2019 are provided in separate files, following the same format as high-rate solutions. <strong>3. Downsampled GPS solutions</strong> The cumulative GPS displacements and error estimates for coseismic and postseismic periods are provided in separate files. The file “displacement_coseismic.txt” has station ID, east, north, and vertical displacement components (in mm), and their error estimates (in mm) in columns. The file “displacement_postseismic.txt” has station ID, postseismic time (relative to the Parkfield earthquake initiation time), east, north, and vertical displacement components (in mm), and their error estimates (in mm) in columns; every block of 13 rows indicates displacements at 13 stations for each epoch. <strong>4. Fault geometry for the SAF and SWFZ</strong> The geometries for the San Andreas Fault (SAF) and Southwest Fracture Zone (SWFZ) are provided in two formats. The “*lonlat.txt” files show the longitude, latitude, depth (km), strike angle (degree), dip angle (degree), area (km<sup>2</sup>), length (km), width (km), and index (starting from 0) for each fault patch in columns. The “*GMT.txt” files show the fault geometry in GMT format. <strong>5. Fault slip models and uncertainty estimates</strong> The strike-slip and dip-slip components (in mm) and their uncertainty estimates are provided in separate files for the coseismic period (“step0”) and cumulative postseismic periods (“step1…44”). Step0–26 models use the SWFZ fault geometry and step27–44 models use the SAF fault geometry. The time information is indicated in the header. The uncertainty estimates only include the variances of the posterior variance-covariance matrix, which are available upon request to the authors.
description: Support from NASA awards NNX17AD99G and NNH17ZDA001N, and Green Foundation Postdoctoral Fellowship
description: {"references": ["Jiang, Bock, and Klein, 2021"]}
DOI: 10.5281/zenodo.4770202
title: CIQUAL food composition table 2020

description: The French food composition database is run by CIQUAL in the Observatory of Food, unit of ANSES (the French agency for food, environmental and occupational health safety). Its mission is to collect, evaluate and make available nutritional composition of food consumed in France. This file provides the composition of 3185 foods for 67 components (for instance: carbohydrates, individual sugars and starch, proteins, fat and fatty acids, vitamins and minerals, energy...).
description: The present data and information are made available to the public by the French Agency for Food, Environmental and Occupational Health & Safety (ANSES). They must not be reproduced in any form without clear indication of the source: "Anses. 2020. Ciqual French food composition table." or, in more detail, for a scientific publication: "Anses. 2020. Ciqual French food composition table. Retrieved DD/MM/YYYY. https://ciqual.anses.fr/ "
DOI: 10.5281/zenodo.5841834
title: Kelmarsh wind farm data

description: This dataset contains: A kmz file for Kelmarsh wind farm in the UK (for opening in e.g. Google Earth) Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.) 10-minute SCADA and events data from the 6 Senvion MM92's at Kelmarsh wind farm, grouped by year from 2016 to mid-2021, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period Data mappings from primary SCADA to csv signal names Site substation/PMU meter data where available for the same period Site fiscal/grid meter data where available for the same period The dataset has been released by Cubico Sustainable Investments Ltd under a CC-BY-4.0 open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. I'll try and add or link to additional file formats that might be easier to work with (e.g. for use with specific analysis software), and update this dataset periodically (e.g. twice a year), but please prompt me as required. Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry. If you would like to get access to other datasets we may hold (e.g. more recent data, data from our other sites, ~30s resolution data, etc.), please let me know, and, if you have any questions or want to discuss open data and this or other initiatives, please contact me and I will endeavour to help. I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committee and my manager for supporting this initiative, as well as our partners GLIL for agreeing to release this data under an open license. I would also like to thank those I have talked to during the process of releasing this data under an open license and the encouragement and advice I have had on the way. For contact my email address is charlie.plumley@cubicoinvest.com. You can also access data from Penmanshiel wind farm here.
DOI: 10.5284/1028203
title: English Heritage Archaeological Monographs

description: English Heritage has a long tradition of producing highly illustrated archaeological monographs about key sites and topics of importance to the understanding of the historic environment in England. Many of the past titles have long been out of print and yet are still of value for reference purposes. As a service to the wider archaeological community, English Heritage is now making these titles available as ebooks, available from their website, and as PDFs which can be accessed from the ADS for free. The PDFs are, in the main, scans of the original publications and reproduce the pages of those publications. While we have tried to ensure that the pages are legible, file size limitations may mean that some pages are less clear and, in case of doubt, users should consult the original publication.
DOI: 10.5285/dd4d20c8626f4b9d99bc14358b1b50fe
title: Airborne LiDAR and RGB imagery from Sepilok Reserve and Danum Valley in Malaysia in 2020

description: This dataset contains LiDAR and RedGreenBlue (RGB) Imagery data collected from a helicopter over two forest sites in Sabah, Malaysia in February 2020. Point cloud data are included in LAS (LASer) format as well as RGB data summary rasters in .tif format. The raster images were processed with LAStools using default parameters. Canopy Height Model (CHM), Digital Surface Model (DSM), Digital Terrain Model (DTM) and pulse density (pd) are also present. The RGB data are provided as jpgs and are organised by flight julian day (JD). The Sepilok Reserve was scanned in full between 15 February 2020 (julian day 46). This is a total area of 27 square kilometres. In Danum Valley the scanning was distributed into two contiguous areas, the protected area (20 square kilometres) and the reduced impact logging area (9 square kilometres) on the 19-22 February 2020 (julian day 50-53). Importantly, these areas were chosen because of the availability of prior airborne LiDAR data collected by NERC in 2014 and by Ground Data Solutions in 2013. The helicopter flew at approximately 350 m altitude above the forest canopy and at a speed of approximately 100 km/hr. The data were georeferenced using ground control points and are provided in the UTM 50N coordinate system.
DOI: 10.5285/ff79d140824f42dd92b204b4f1e9e7c2
title: ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0

description: This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. It provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Data are only available for the NH winter months, October - April.
DOI: 10.5676/eum_saf_cm/claas/v003
title: CLAAS-3: CM SAF CLoud property dAtAset using SEVIRI - Edition 3

description: The CLAAS-3 record provides cloud properties derived from intercalibrated measurements (Meirink et al., 2013) of the SEVIRI sensor onboard METEOSAT second generation (MSG) satellites. CLAAS-3 is the latest edition of CLAAS with previous editions documented in Stengel et al. (2014) and Benas et al. (2017). CLAAS-3 includes the following cloud properties: cloud mask/type, cloud top temperature/pressure/height, cloud thermodynamic phase, cloud optical thickness, cloud particle effective radius and cloud water path. Additionally, cloud droplet number concentration and cloud geometrical thickness are provided for liquid clouds. All data are available on multiple processing levels spanning level-2 (native SEVIRI resolution, i.e. 15 minutes repeat cycle and 3 km (nadir) spatial resolution) to level-3 (spatio-temporal aggregations such as daily averages, monthly averages and monthly histograms on a 0.05° x 0.05° grid, as well as monthly mean diurnal cycles and joint cloud optical thickness – cloud top pressure histograms on a 0.25° x 0.25° grid). CLAAS-3 covers the time period 2004/01 until 2020/12 as climate data record (CDR), but is operationally extended as interim climate data record (ICDR) to the present with a latency of 10 days. Some key features of CLAAS-3 compared to previous editions are: 1) The ICDR contains the same (full) product portfolio as the CDR, 2) Significant improvements for cloud detection with provision of a cloud probability, 3) Significant improvements for vertical placement of clouds (pressure, temperature, height), 4) New cloud effective radius products using the 3.9 µm in addition to the 1.6 µm channel, 5) New cloud droplet number concentration and geometrical thickness products for liquid clouds, and 6) Full uncertainty portfolio for all level-2 and level-3 products. A comprehensive evaluation was conducted and results are summarized in the Validation Report which composes, along with the Product User Manual and the Algorithm Theoretical Baseline Documents a rich set of CLAAS-3 documentation. With CLAAS-3, regional and large-scale cloud processes at temporal scales of quarter-hours to years can be studied. Furthermore, due to its increasing record length (19 years and growing), CLAAS-3 becomes a suitable source for climate monitoring applications.
DOI: 10.60520/ieda/113301
title: The chemical and isotopic compositions of natural gases from the Songliao Basin

description: Six natural gas samples for analysis of gas compositions, bulk stable isotopes and methane clumped isotope are collected at different gas fields in the Xujiawei Fault Depression (XFD) of the Songliao Basin. Abundances of singly and doubly-substituted isotopologues of methane (13CH4, 12CH3D, 13CH3D, and 12CH2D2) are measured on a Panorama (Pan004), an ultra–high–resolution gas–source multiple collector Mass Spectrometer at Guangzhou Institute of Geochemistry (GIG).