cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7879938
title: PASS - A SCORING SYSTEM TO EVALUATE PERSISTENT ACUTE KIDNEY INJURY IN CRITICALLY ILL ADULT ICU PATIENTS

description: Acute Kidney Injury (AKI) and sepsis is highly prevalent in Intensive Care Units (ICU) and has direct effect on mortality and morbidity. We aimed to evaluate if course and recovery of sepsis-AKI can be predicted using variables collected at the time of admission, and to develop a formula - Persistent AKI Scoring System (PASS) to predict recovery. 63 consecutive patients admitted for sepsis-AKI in our ICUs were evaluated over a period of 8 months. Baseline demographic and clinical/laboratory parameters ,including serum creatinine (SCr), Base Excess (BE), Plethysmographic Variability Index (PVI), Caval Index, R wave Variability (RVI), Mean Arterial Pressure (MAP) and Renal Resistivity Index (RI) using renal doppler and need for inotropes were assessed on admission. Patients were managed with standard protocols with fluid resuscitation and other specific therapy. After 6 of hours of fluid resuscitation, hemodynamic status and volume status of the patient was assessed. Patients were classified as Volume Responders or Non-responders depending on hemodynamic stabilization using parameters like MAP. Re- assessment of all the variables was done at 24 hours and 72 hours after admission. The primary outcome was persistent AKI after 72hrs. The secondary outcome was initiation of dialysis and death within 15 days of admission. 34 subjects recovered from AKI. 32 patients showed volume response with respect to hemodynamic parameters and 31 patients were non-responders. Response to fluid, MAP at admission, MAP after 6 hours, BE at admission, inotrope requirement, and PVI at admission did not correlate with recovery of AKI. RI, RVI and S Cr at admission significantly correlated with recovery from AKI These 3 parameters were evaluated further to develop the model for predicting recovery from AKI.• Score&gt; 7.8 predicted recovery from AKI with sensitivity of 79.4%, specificity of 72.4%. PPV 81.8% and NPV 76.7%. PASS Score&gt;7.8 had AUROC of 0.85. Multiple logistic regression showed that SCr&lt; 2.36 mg%, RVI &gt; 14.45 and RI &lt; 0.8 on admission correlated with recovery. PASS SCORE Formula was created using the same. CONCLUSION: PASS score can be a simple bed side method to reliably identify salvageable cases of sepsis-AKI, guiding fluid resuscitation and also aiding early referrals from rural to higher centers for better management.
DOI: 10.15468/dl.jqyh6g
title: Occurrence Download

description: A dataset containing 1242 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Serrodes Guenée, 1852"
 ]
}

 The dataset includes 1242 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024743-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8kp25ycf63.1
title: On-orbit Electrical Power System Dataset of 1U CubeSat constellation for Machine Learning Models

description: This dataset contains on-orbit data samples of the Electrical Power System (EPS) from 4 different 1U CubeSats belonging to the BIRDS constellation. The EPS is responsible for providing uninterrupted power to the overall satellite both during sunlight and eclipse. The satellites are based on the BIRDS open-source standardized bus designed by Kyutech for research and education. BIRDS bus was used for six satellites that were delivered to International Space Station (ISS) onboard the Cygnus resupply spacecraft launched by Antares rocket and released into ISS orbit (altitude 400 km, inclination: 51.6º, duration: 92.6 min). The dataset contains the data of voltage (mV), current (mA) and temperature (Celsius) of the battery and solar panels attached to 5 sides of the satellite. This data is collected by the onboard computer every 90 seconds in nominal operation or every 10 seconds in fast sampling mode. The data is downloaded from the satellite memory by the ground station operators. The dataset contains one file per satellite, that includes data from solar panels and batteries since their deployment into orbit until the end of its life for the UGUISU, RAAVANA, and NEPALISAT satellites, the first two showing a failure in one of their panels during more than two years of operation on-orbit. The TSURU satellite dataset includes data since its deployment into orbit and will continue to be collected until the end of its life. The dataset generated will be useful for 1U CubeSat, such as BIRDS platform, users and satellite developers by using it as a reference to compare the behaviour of their Electric Power System under different operating scenarios and align their missions according to the available power on-orbit. At the same time, the dataset can help computer science researchers to build and validate new models for fault diagnosis and outlier detection.
DOI: 10.18128/d070.v6.2
title: IPUMS Health Surveys: National Health Interview Survey, Version 6.2

description: The IPUMS NHIS is a harmonized set of data for over 50 years (1963-present) of the National Health Interview Survey (NHIS). The NHIS is the principal source of information on the health of the U.S. population, covering such topics as general health status, the distribution of acute and chronic illness, functional limitations, access to and use of medical services, insurance coverage, and health behaviors (such as exercise, diet, and tobacco and alcohol consumption). On average, the survey covers 100,000 persons in 45,000 households each year. IPUMS NHIS facilitates cross-time comparisons of these invaluable survey data by coding variables identically across time and providing detailed documentation covering comparability issues for harmonized variables. IPUMS NHIS data are distributed for free through an online data access system (ihis.ipums.org) to any person who agrees to follow the responsible use guidelines set by the National Center for Health Statistics.
DOI: 10.20383/102.0447
title: SuperDARN 2015 RAWACF

description: SuperDARN RAWACF files for the calendar year 2015. INSTRUMENT INFORMATION AND RULES OF THE ROAD: *** Before downloading and using the data, make sure to read the README file in the top directory of this collection. *** SuperDARN is an international collaboration operating high frequency (HF) radars deployed in the northern and southern hemispheres to measure ionospheric plasma circulation. Each partner institution secures funding and manages operations for their own facilities. The continued availability of SuperDARN data depends on the proper acknowledgement of data by its users. Guidelines for data acknowledgement are as follows: When data from an individual radar or radars are used, users must contact the principal investigator(s) of those radar(s) to obtain the appropriate acknowledgement information and to offer collaboration, where appropriate. Contact information is available in the README file for this collection. For all usage of SuperDARN data, users are asked to include the following standard acknowledgement text: “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.” While SuperDARN has an open data use policy, i.e., prior permission to access and analyse the data is not required, the data user is strongly encouraged to establish early contact with any Principal Investigator whose data are involved in the project to discuss the intended usage and collaboration. Data can be subject to limitations that are not immediately evident to users. In addition, some data are embargoed for use by designated Principal Investigators for a period of one year. SuperDARN and the organizations that contributed data must be acknowledged in all reports and publications that use SuperDARN data. The SuperDARN Executive Council (see list in the README) must be notified before data are redistributed through another database. The data are not to be used for commercial purposes. If you have any questions about appropriate use of these data, contact any SuperDARN Principal Investigator.
DOI: 10.20383/102.0451
title: SuperDARN 2011 RAWACF

description: SuperDARN RAWACF files for the calendar year 2011. INSTRUMENT INFORMATION AND RULES OF THE ROAD: *** Before downloading and using the data, make sure to read the README file in the top directory of this collection. *** SuperDARN is an international collaboration operating high frequency (HF) radars deployed in the northern and southern hemispheres to measure ionospheric plasma circulation. Each partner institution secures funding and manages operations for their own facilities. The continued availability of SuperDARN data depends on the proper acknowledgement of data by its users. Guidelines for data acknowledgement are as follows: When data from an individual radar or radars are used, users must contact the principal investigator(s) of those radar(s) to obtain the appropriate acknowledgement information and to offer collaboration, where appropriate. Contact information is available in the README file for this collection. For all usage of SuperDARN data, users are asked to include the following standard acknowledgement text: “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.” While SuperDARN has an open data use policy, i.e., prior permission to access and analyse the data is not required, the data user is strongly encouraged to establish early contact with any Principal Investigator whose data are involved in the project to discuss the intended usage and collaboration. Data can be subject to limitations that are not immediately evident to users. In addition, some data are embargoed for use by designated Principal Investigators for a period of one year. SuperDARN and the organizations that contributed data must be acknowledged in all reports and publications that use SuperDARN data. The SuperDARN Executive Council (see list in the README) must be notified before data are redistributed through another database. The data are not to be used for commercial purposes. If you have any questions about appropriate use of these data, contact any SuperDARN Principal Investigator.
DOI: 10.5281/zenodo.13144797
title: Ocean Drilling Program Site 959 Datasets

description: -Version 1.1.0

Datasets updated with new Eocene Site 959 data added as supplement to:



"Global warming and equatorial Atlantic paleoceanographic changes during early Eocene carbon cycle perturbation V" by Kegel et al.


 

-Version 1.0.0: 

Data supplement to:





"Polar amplification of orbital-scale climate variability in the early Eocene greenhouse world" by Fokkema et al. (2024). 




"Tropical Warming and Intensification of the West African Monsoon during the Miocene Climatic Optimum" by Wubben et al. (2024).


"Early to Middle Miocene Orbitally-Paced Climate Dynamics in the Eastern Equatorial Atlantic" by Spiering et al. (2024). 


Updated age model and datasets of bulk magnetic susceptibility, bulk carbonate oxygen and carbon isotopes, bulk organic carbon isotopes, ICP-OES, palynology and GDGTs from Ocean Drilling Program (ODP) Leg 159 Site 959.

This upload contains datasets by Kegel et al. (2024); Fokkema et al. (2024); Wubben et al. (2024); Spiering et al. (2024); Cramwinckel et al. (2018); Frieling et al. (2018; 2019) and Van der Weijst et al. (2022).
DOI: 10.5281/zenodo.13235058
title: Derived seismic data products from paper: "Seismic Imaging of the Reykjanes Peninsular, Iceland: Crustal-scale context of geothermal areas and ongoing volcano-tectonic unrest"

description: Receiver Functions (RF) and Dispersion Curves derived from seismic stations throughout the Reykjanes Peninsular SW Iceland. 

RF are in Smurfpy python3 PICKLE format - details on format and scripts to read in here: https://github.com/sannecottaar/smurfpy.  Folder  has the following file structure:

RF_Data_Zenodo --> contains station folders: Network.Station --> contains folders  goodRF_crust2 and goodRF_crust6 where 2 and 6 refer to Gaussian pulse widths used to produce RF via iterative deconvolution --> contains RF files in format NETWORK.STATION_BAZ_EPIDIST_EVTIME.PICKLE and subfolders of identified highly similar waveform subsets in the format BAZ_NNN_NNN used in data inversions

Dispersion curves are in Fast Marching Surfacewave Tomography FMST input format (Rawlinson and Sambridge, 2005), details on format in FMST manual: https://iearth.edu.au/codes/FMST/instructions.pdf. Folder FMST_Dispersion_Inputs_Zenodo contains the following files:



otimes0.25Hz.dat

otimes0.2Hz.dat

otimes0.35Hz.dat 

otimes0.3Hz.dat

otimes0.45Hz.dat

otimes0.4Hz.dat

otimes0.5Hz.dat

sources.dat 

receivers.dat


 

References:

Rawlinson, Nick. "FMST: fast marching surface tomography package–Instructions." Research School of Earth Sciences, Australian National University, Canberra 29 (2005): 47.
DOI: 10.5281/zenodo.14132727
title: Data of GOES X-ray flux, GRACE neutral density, and TIEGCM Simulation for manuscript "On the Relationship between Traveling Atmospheric Disturbances and Traveling Ionospheric Disturbances"

description: There are data used in manuscript "On the Relationship between Traveling Atmospheric Disturbances and Traveling Ionospheric Disturbances":



GOES 15 0.1-0.8 nm X-ray flux: "xrsa_g15_20170907-08.mat"

GRACE neutral density: "GA_DNS_2017_09_07.mat", "GA_DNS_2017_09_08.mat"

Simulation of Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM): other files
DOI: 10.5281/zenodo.1485588
title: Esa Seom-Ias – Measurement And Acs Database O3 Uv Region

description: The database contains measurements and absorption cross sections generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.

The measurements were recorded at the German Aersopace Center (DLR) to provide a new absorption cross section database for ozone according to the needs of the TROPOMI instrument aboard the Sentinel 5-P satellite. The data are compiled in 2  zip files, one for the measurements (O3_UV_region_measurement_database_13112018.zip), one for the absorption cross sections (O3_UV_region_absorption_cross_section_database_13112018.zip) and a readme file (ESA_SEOM_IAS_spectra_O3UVRegion_readme.docx)
DOI: 10.5281/zenodo.4559802
title: datasets for the paper in AMT: Consistency of total column ozone measurements between the Brewer and Dobson spectroradiometers of the LKO Arosa and PMOD/WRC Davos

description: Datasets for the publication in amt: Gröbner, J., Schill, H., Egli, L., and Stübi, R.: Consistency of total column ozone measurements between the Brewer and Dobson spectroradiometers of the LKO Arosa and PMOD/WRC Davos, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-497, in review, 2021.
description: {"references": ["Gr\u00f6bner, J., Schill, H., Egli, L., and St\u00fcbi, R.: Consistency of total column ozone measurements between the Brewer and Dobson spectroradiometers of the LKO Arosa and PMOD/WRC Davos, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-497, in review, 2021."]}
DOI: 10.6084/m9.figshare.19920662
title: A high-resolution climate simulation dataset for the past 540 million years

description: Here we perform 55 snapshot simulations for the past 540 million years, with a 10-million-year interval, using the Community Earth System Model version 1.2.2 (CESM1.2.2). The climate simulation dataset includes global distributions of monthly surface temperatures and precipitation, with a 1° horizontal resolution of 0.9° × 1.25° in latitude and longitude. This open access climate dataset is useful for multidisciplinary research, such as paleoclimate, geology, geochemistry, and paleontology. <br> Climate variables include monthly surface temperatures (T; unit: °C; Not surface air temperatures), precipitation (P; unit: mm month–1), fraction of surface land area (LANDFRAC; unit: fraction), surface geopotential (PHIS; unit: m2 s–2), surface albedo (SALB; unit: fraction), and zonal (U; unit: m s–1) and meridional (V; unit: m s–1) winds at 1000 hPa, averaged over the last 60 model years. T, P, SALB, U, and V have the dimensions of 55 (simulation) × 12 (month) × 192 (latitude) × 288 (longitude). LANDFRAC and PHIS have the dimensions of 55 (simulation) × 192 (latitude) × 288 (longitude).
DOI: 10.1285/i22390359v33p7
title: How to do things with modes. Transmodality in slides

description: Slideshows are ubiquitous in today's academic events such as university lectures. Thus, it appears imperative to investigate the evolution of the modes which are implemented in processes of knowledge communication through slideshows. In particular, this study focuses on the complex relations between texts and images and on the multifaceted functions that different modes assume. A corpus of lecture slides drawn from the MIT OpenCourseWare site is investigated from a transmodal perspective by adopting a qualitative approach. The analysis shows that the production of slides is based on the exploitation of multiple modes which co-act for the fulfillment of different pragmatic needs (e.g. informative, persuasive, or interactional) and assume a mutually constructive function. This paper also argues for the significance of transmodal research in academic contexts, especially in the light of the constant technological and epistemological shift which accompanies the evolution of teaching practices in higher education. Indeed, a transmodal approach can facilitate a wider understanding of the affordances and limitations of the different semiotic resources, which need to be conceived from a complementary and syncretic perspective.
DOI: 10.17632/kcmpdxky7p.1
title: ISL-CSLTR: Indian Sign Language Dataset for Continuous Sign Language Translation and Recognition

description: Sign language is a cardinal element for communication between deaf and dumb community. Sign language has its own grammatical structure and gesticulation nature. Research on SLRT focuses a lot of attention in gesture identification. Sign language comprises of manual gestures performed by hand poses and non-manual features expressed through eye, mouth and gaze movements. The sentence-level completely labelled Indian Sign Language dataset for Sign Language Translation and Recognition (SLTR) research is developed. The ISL-CSLTR dataset assists the research community to explore intuitive insights and to build the SLTR framework for establishing communication with the deaf and dumb community using advanced deep learning and computer vision methods for SLTR purposes. This ISL-CSLTR dataset aims in contributing to the sentence level dataset created with two native signers from Navajeevan, Residential School for the Deaf, College of Spl. D.Ed & B.Ed, Vocational Centre, and Child Care & Learning Centre, Ayyalurimetta, Andhra Pradesh, India and four student volunteers from SASTRA Deemed University, Thanjavur, Tamilnadu. The ISL-CSLTR corpus consists of a large vocabulary of 700 fully annotated videos, 18863 Sentence level frames, and 1036 word level images for 100 Spoken language Sentences performed by 7 different Signers. This corpus is arranged based on signer variants and time boundaries with fully annotated details and it is made available publicly. The main objective of creating this sentence level ISL-CSLRT corpus is to explore more research outcomes in the area of SLTR. This completely labelled video corpus assists the researchers to build framework for converting spoken language sentences into sign language and vice versa. This corpus has been created to address the various challenges faced by the researchers in SLRT and significantly improves translation and recognition performance. The videos are annotated with relevant spoken language sentences provide clear and easy understanding of the corpus data. Acknowledgements: The research was funded by the Science and Engineering Research Board (SERB), India under Start-up Research Grant (SRG)/2019–2021 (Grant no. SRG/2019/001338). And also, we thank all the signers for their contribution in collecting the sign videos and the successful completion of the ISL-CSLTR corpus. We would like to thank Navajeevan, Residential School for the Deaf, College of Spl. D.Ed & B.Ed, Vocational Centre, and Child Care & Learning Centre, Ayyalurimetta, Andhra Pradesh, India for their support and contribution.
DOI: 10.20383/102.0446
title: SuperDARN 2016 RAWACF

description: SuperDARN RAWACF files for the calendar year 2016. INSTRUMENT INFORMATION AND RULES OF THE ROAD: *** Before downloading and using the data, make sure to read the README file in the top directory of this collection. *** SuperDARN is an international collaboration operating high frequency (HF) radars deployed in the northern and southern hemispheres to measure ionospheric plasma circulation. Each partner institution secures funding and manages operations for their own facilities. The continued availability of SuperDARN data depends on the proper acknowledgement of data by its users. Guidelines for data acknowledgement are as follows: When data from an individual radar or radars are used, users must contact the principal investigator(s) of those radar(s) to obtain the appropriate acknowledgement information and to offer collaboration, where appropriate. Contact information is available in the README file for this collection. For all usage of SuperDARN data, users are asked to include the following standard acknowledgement text: “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.” While SuperDARN has an open data use policy, i.e., prior permission to access and analyse the data is not required, the data user is strongly encouraged to establish early contact with any Principal Investigator whose data are involved in the project to discuss the intended usage and collaboration. Data can be subject to limitations that are not immediately evident to users. In addition, some data are embargoed for use by designated Principal Investigators for a period of one year. SuperDARN and the organizations that contributed data must be acknowledged in all reports and publications that use SuperDARN data. The SuperDARN Executive Council (see list in the README) must be notified before data are redistributed through another database. The data are not to be used for commercial purposes. If you have any questions about appropriate use of these data, contact any SuperDARN Principal Investigator.