cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27752301
title: Supplementary Material for: Conservative management of intraocular graphite foreign body: A case report and literature review

description: Retained intraocular graphite foreign bodies are uncommon. Although they are generally inert, they have been reported to cause severe inflammatory reactions and progressive damage to intraocular structures. We report a case of a 7-year-old girl with a retained intraocular graphite pencil lead foreign body in the vitreous treated conservatively for more than 5 years without any consequences.
DOI: 10.17909/szf2-bg42
title: Data from program 1947, and paper "A JWST Survey of Supernova Remnant Cassiopeia A" by Milisavljevic et al. (2024)

description: Multi-filter mosaics of SNR Cas A using both NIRCam and MIRI, along with selected positions observed with MIRI MRS and NIRSpec IFU of the SNR ejecta.
DOI: 10.18154/rwth-2024-01907
title: Dataset to „Battery Electric Vehicles in Commercial Fleets: Use profiles, battery aging, and open-access data“

description: Electric vehicles are key to reducing emissions in the transportation sector, and the electric vehicle market is growing strongly worldwide. In research and development in the electric vehicle sector, it is important to understand electric vehicle use profiles and their impact on battery aging. The battery is the central element of a vehicle as it determines the range as well as the price. For techno-economic analysis, battery aging is of special interest as it is a major factor in the vehicle’s lifetime. Battery aging depends on the battery use profile, but up to now, operational data is scarce, and many publications are based on simulations and assumptions. To contribute, we analyze multi-year commercial vehicle and battery data collected from onboard data loggers in a field test conducted from 2014 to 2016. We also develop and evaluate periodic non-intrusive capacity tests carried out with a chassis dynamometer to assess battery aging in terms of capacity fade. Electric vehicles in a geriatric care fleet experienced high usage and regular driving patterns with annual driving distances of 9062 km to 15308 km and a mean state-of-charge between 70% and 80% while driving. Use profile regularity and state-of-charge values at the time of plug-in are major factors in the evaluation of vehicle-to-X application viability. Regarding aging, the batteries suffered a capacity fade from 3.1% to up to 13% during fleet operation of three years. We publish time series data of the vehicle and the battery in addition to mobility data for nine vehicles to fill the data gap for available electric vehicle data and allow for further analysis.
DOI: 10.20350/digitalcsic/16131
title: Unmanned Aerial Vehicle Imagery for Early Stage Weed Classification and Detection in Maize and Tomato Crops

description: [Description of methods used for collection/generation of data] Data were acquired through an RGB camera mounted on a UAV at an altitude of 11m. From the images obtained by the UAV, geomatic products were systematically generated for the creation of orthomosaics. This task was performed by incorporating information extracted from the RGB channels using Agisoft PhotoScan software (Agisoft LLC, St. Louis, MO). The resulting orthomosaics were subdivided into smaller sections. To identify and label weed species, malherbology experts carried out the task manually. This involved drawing bounding boxes around each plant and annotating various visible objects present in each divided image. This process was executed using the graphical tool labelImg open source software. The final result consists of a collection of individual images, each corresponding to a specific label, i.e., to each plant of each identified species.
description: The dataset, titled 'Unmanned Aerial Vehicle Imagery for Early Stage Weed Classification and Detection in Maize and Tomato Crops,' comprises RGB images captured from an unmanned aerial vehicle flying at an altitude of 11 meters. These images were taken during the early growing season of two summer crops: Maize (BBCH14) at the CSIC experimental farm La Poveda in Arganda del Rey, Madrid, Spain, and Tomato (BBCH501) in commercial plots located in Santa Amalia, Badajoz, Spain. The dataset includes 33,467 labels representing weeds (Atriplex patula, Chenopodium album, Convolvulus arvensis, Cyperus rotundus, Lolium rigidum, Portulaca oleracea, Salsola kali, Solanum nigrum) and crops (maize, tomato). All images are saved in *.jpg format and have been labeled using partitions extracted from an orthomosaic. The final result consists of a collection of individual images, each corresponding to a specific label, i.e., to each plant of each identified species.
DOI: 10.21227/ce9q-jr19
title: Automotive Li-ion Cell Usage Data Set

description: An effective use of electrochemical Energy Storage Systems (ESSs) is mandatory for improving the performances of next generation electric vehicles and hybrid electric vehicles, as well as of smart grid and microgrid systems.In particular, Battery Management Systems (BMSs) are useful devices aiming at monitoring, managing, and controlling the battery pack. One of the main task of any BMS is performing State of Charge estimation of the cells composing the ESS. Herein, one of the most promising approaches are model based techniques such as Esxtended Kalman Filters or Unscented Kalman Filters.The models to be used in the SoC estimation task should respect three main properties: first, they should provide useful insights on the physics of the cell for exposing information about SoC; second, they should approximate all the nonlinear relationships between the main physical quantities; third, system identification should be as flexible as possible.The latter property means that the tuning of the model should not be based on specific, rigid, and time consuming tests. Therefore, the enclosed data set aims at providing a generic and realistic use of electrochemical cells upon which it will be possible to test and validate novel models and the related system identification procedures.The data set has been collected autonomously by means of a self-developed programmable battery cycler that emulates the use of electrochemical cells in the realistic environment of a pure electric vehicle.Therefore, the data has been collected by simulating the use of a lithium polymer cell model ePLB C020 in an electric car similar to the Nissan Leaf. In particular, the specific cell used for the acquisition campaign was characterized by an effective capacity of 15 Ah. Two different trips have been considered for building the Training and the Test Sets. In order to consider a realistic environment, both the trips have been composed by mixing urban, extra-urban and highway driving cycles with rests and battery charging phases. The considered driving cycles  have been selected from the Federal Test Procedure repository aiming at considering a realistic use of the cell. The Training Set consists in a trip of 277.64 km for a total of about 12 hours of data, whereas the Test Set counts 163.24 km and about 7 hours of data. The measurements have been performed with a sampling time of 1 second and the SoC sequence has been evaluated by means of the Coloumb counting algorithm.
DOI: 10.48612/msupe/1zmv-hzvp-k852
title: Подростки в трудной жизненной ситуации (данные РБ)

description: Представлены данные подростков 12-18 лет из разных регионов РБ (N=425) по методикам: Многомерная шкала удовлетворенности жизнью (ШУДЖИ); Климат в классе; Шкала реагирования на неопределенность (URS); краткая версия методики изучения копинг-стратегий для подростков СОРЕ-30A; Шкала настойчивости. Исследование аналогично исследованию, проведенному на российской выборке (Одинцова и др., 2024a).
DOI: 10.5281/zenodo.10120306
title: Analysis scripts and dataset for Barthel et. al. (2023)

description: This archive contains post-processed data and plotting scripts needed to generate the results described in Barthel et.al. (2023) Machine learning correction operators  for under-resolved climate models using nudged simulations. The archive includes processed data from simulations of a 2  layer Quasi-Geostrophic model generated using an in-house solver, simulations of DOE's E3SM Atmosphere Model Version 2 (EAMv2), and ERA5 reanalysis data.  A detailed description of the model and simulations can be found in Barthel et. al. (2023).
DOI: 10.5281/zenodo.12091223
title: Data for: Multi-year field measurements of home storage systems and their use in capacity estimation

description: The dataset accompanies the Nature Energy publication by Figgener et al. (2024), Multi-year field measurements of home storage systems and their use in capacity estimation, DOI 10.1038/s41560-024-01620-9. In addition, we use the dataset in Figgener et al. (2024), Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data, DOI 10.48550/arXiv.2411.08025

The ISEA / CARL of RWTH Aachen University measured 21 private home storage systems in Germany over up to eight years from 2015 to 2022. All these storage systems are combined with residential photovoltaic systems to increase self-consumption. The measured quantities published are system-level battery current, voltage, power, battery pack housing temperature, and room temperature. The sample rate is one second. The dataset consists of 106 system years, 14 billion data points, and 1,270 monthly files stored in 21 system folders. 

Use the data as follows:1. Download the data (Data_ID_01.zip to Data_ID_21.zip) and the belonging repository (Metadata_and_Code.zip)

2. Uncompress the files so that the uncompressed folders have the same name as the .zip files.

3. Copy all data folders in folder "Metadata_and_Code/00_Data/01_Operational_Data". Read and execute the file "StartUp_Read_and_Execute.m" and stay in this folder for any script you execute. 

In addition, a detailed description of the dataset and how to use it can be found in the supplementary information of the publication.
DOI: 10.5281/zenodo.13694272
title: Figure data for "Machine Learning For Early Dynamic Prediction Of Functional Outcome After Stroke"
DOI: 10.6084/m9.figshare.26461594
title: Dietary zinc deficiency promotes pathogenesis during <i>Acinetobacter baumannii</i> lung infection via an IL-13 dependent response

description: Source data for "Dietary zinc deficiency promotes Acinetobacter baumannii lung infection via IL-13 in mice"
Lauren D. Palmer, Kacie A. Traina, Lillian J. Juttukonda, Zachery R. Lonergan,, Dziedzom A. Bansah, Xiaomei Ren, John H. Geary, Christopher Pinelli,, Kelli L. Boyd, Tzushan S. Yang, Eric P. Skaar
DOI: 10.15468/dl.2mz2gk
title: Occurrence Download

description: A dataset containing 14439 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is IDN.1_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 14439 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011443-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/6s6ph9n8zg.1
title: Graphite//LFP synthetic training prognosis dataset

description: This training dataset was calculated using the mechanistic modeling approach. See the “Benchmark Synthetic Training Data for Artificial Intelligence-based Li-ion Diagnosis and Prognosis“ publication for mode details. More details will be added when published. The prognosis dataset was harder to define as there are no limits on how the three degradation modes can evolve. For this proof of concept work, we considered eight parameters to scan. For each degradation mode, degradation was chosen to follow equation (1). %degradation=a × cycle+ (exp^(b×cycle)-1) (1) Considering the three degradation modes, this accounts for six parameters to scan. In addition, two other parameters were added, a delay for the exponential factor for LLI, and a parameter for the reversibility of lithium plating. The delay was introduced to reflect degradation paths where plating cannot be explained by an increase of LAMs or resistance [55]. The chosen parameters and their values are summarized in Table S1 and their evolution is represented in Figure S1. Figure S1(a,b) presents the evolution of parameters p1 to p7. At the worst, the cells endured 100% of one of the degradation modes in around 1,500 cycles. Minimal LLI was chosen to be 20% after 3,000 cycles. This is to guarantee at least 20% capacity loss for all the simulations. For the LAMs, conditions were less restrictive, and, after 3,000 cycles, the lowest degradation is of 3%. The reversibility factor p8 was calculated with equation (2) when LAMNE &gt; PT. %LLI=%LLI+p8 (LAM_PE-PT) (2) Where PT was calculated with equation (3) from [60]. PT=100-((100-LAMPE)/(100×LRini-LAMPE ))×(100-OFSini-LLI) (3) Varying all those parameters accounted for more than 130,000 individual duty cycles. With one voltage curve for every 100 cycles. 6 MATLAB© .mat files are included: The GIC-LFP_duty_other.mat file contains 12 variables Qnorm: normalize capacity scale for all voltage curves P1 to p8: values used to generate the duty cycles Key: index for which values were used for each degradation paths. 1 -p1, … 8 - p8 QL: capacity loss, one line per path, one column per 100 cycles. File GIC-LFP_duty_LLI-LAMsvalues.mat contains the values for LLI, LAMPE and LAMNE for all cycles (1line per 100 cycles) and duty cycles (columns). Files GIC-LFP_duty_1 to _4 files contains the voltage data split into 1GB chunks (40,000 simulations). Each cell corresponds to 1 line in the key variable. Inside each cell, one colunm per 100 cycles.
DOI: 10.5066/p9xg876w
title: Thermoelectric-power condenser duty estimates by month and cooling type for use to calculate water use by power plant for the 2008-2020 reanalysis period for the conterminous United States

description: The U.S. Geological Survey (USGS) developed models to estimate the amount of water that is withdrawn and consumed by thermoelectric power plants (Diehl and others, 2013; Diehl and Harris, 2014; Harris and Diehl, 2019). The thermoelectric water use models are based on linked heat-and-water budgets that are constrained by power plant generation and cooling system technologies, the amount of fuels consumed and electricity generated, and environmental variables. The heat-budget side of the models calculates the amount of waste heat (fuel heat that is not converted to electricity) that is removed from the steam used to drive the turbines that generate electricity and transferred to the cooling system in a thermoelectric power plant?s condenser, which is defined as the condenser duty (Diehl and others, 2013). Condenser duty is an intermediate calculation and an input to the water-budget side of the thermoelectric water use models that estimates plant-specific water withdrawal and consumption. The models provide consistent methods for water-use estimation across U.S. thermoelectric plants and estimates independent of plant operator-reported data. This dataset presents a historical reanalysis of monthly condenser duty estimates from 2008 to 2020, and associated information for 1360 water-using, utility-scale thermoelectric power plants in the conterminous United States operational within the time period. These monthly condenser duty estimates were input to the water-budget side of the thermoelectric water-use models for the 2008-2020 historical reanalysis of water withdrawals and consumption by HUC12, month, and year for the conterminous United States (Galanter and others, 2023).
DOI: 10.5281/zenodo.10125312
title: PocketGen datasets
DOI: 10.5281/zenodo.10425353
title: A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models

description: Dataset corresponding to the associated publication, "A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models."  The dataset includes high-resolution photoionization and photoabsorption cross section for O and N2 as well as high-resolution solar spectrum.  Photoionization rates from model runs obtained from AURIC and the Meier photoionization code are also included.  Please refer to the readme for information on the data structure.

***Please note that the paper is under review and has not been accepted yet.***
DOI: 10.5281/zenodo.10637353
title: Control of mitophagy initiation and progression by  the TBK1 adaptors NAP1 and SINTBAD

description: Mitophagy preserves overall mitochondrial fitness by selectively targeting damaged mitochondria for degradation. The regulatory mechanisms that prevent PINK1/Parkin-dependent mitophagy and other selective autophagy pathways from overreacting while ensuring swift progression once initiated are largely elusive. Here, we demonstrate how the TBK1 adaptors NAP1 and SINTBAD restrict the initiation of OPTN-driven mitophagy by competing with OPTN for TBK1. Conversely, they promote the progression of NDP52-driven mitophagy by recruiting TBK1 to NDP52 and stabilizing its interaction with FIP200. Notably, OPTN emerges as the primary recruiter of TBK1 during mitophagy initiation, which in return boosts NDP52-mediated mitophagy. Our results thus define NAP1 and SINTBAD as cargo receptor rheostats, elevating the threshold for mitophagy initiation by OPTN while promoting the progression of the pathway once set in motion by supporting NDP52. These findings shed light on the cellular strategy to prevent pathway hyperactivity while still ensuring efficient progression.
DOI: 10.5281/zenodo.10949792
title: Data and code for "Tensor product random matrix theory"

description: The data files and python scripts to generate Figure 3 of the manuscript "Tensor product random matrix theory" are uploaded.



In the 'scripts' folder there are two python files. The file 'SFF.py' was used to generate the dataset. The file 'plot.py' generates Figure 3.

In the 'processed data' folder there are .csv files with the data used to generate Figure 3.

In the 'figures' folder the generated figure is included.

In the 'additional plots' folder, three data sets and corresponding plots not incorporated in the paper are included.
DOI: 10.5281/zenodo.13343897
title: Dataset for: Room-Temperature Optically Detected Coherent Control of Molecular Spins

description: Dataset for: Room-Temperature Optically Detected Coherent Control of Molecular Spins
description: This work was supported by UK Research and Innovation (Grant No. MR/W006928/1); the UK Engineering and Physical Sciences Research Council (Grant No. EP/W027542/1, No. EP/F039948/1, No. EP/F04139X/1, and No. EP/V048430/1); the Australian Research Council via the ARC Centre of Excellence in Exciton Science (Grant No. CE170100026); the Sydney Quantum Academy, Sydney, NSW, Australia; the EPSRC and SFI Centre for Doctoral Training in the Advanced Characterization of Materials (CDT-ACM) (Grant No. EP/S023259/1).
DOI: 10.5281/zenodo.13363432
title: Dataset for 'Development of Super Plasma Bubbles during the September 7, 2017 Geomagnetic Storm Revealed by Coupled GITM-SAMI3 Simulations'

description: This dataset includes the binary files of a simulation and the plotting programs.
DOI: 10.5281/zenodo.18557
title: Presence-Only Locations Of Tapirus Bairdii

description: Presence-only locations of Baird's Tapir, used in creation of a species distribution model.
DOI: 10.5281/zenodo.7390791
title: The FORCIS database: A global census of planktonic Foraminifera from ocean waters

description: The FORCIS (Foraminifera Response to Climatic Stress) database is a synthesis grouping datasets on living planktonic foraminifera. We assembled foraminiferal diversity and distribution data in the global oceans from 1910 until 2018, curating published and unpublished datasets. This database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump from the global ocean.


The FORCIS database version 01 is composed of 5 files (“.csv” format). All data coming from different sampling devices were put into separate “.csv” files. Only the data of the CPR from the Southern Hemisphere have been separated from the Northern Hemisphere CPR data as the data structure is not the same (species counts resolved vs. binned total counts, respectively).  


Apart from the file of  CPR data from the Northern Hemisphere that contains only metadata and binned total counts, all the remaining four files contain 4 blocks:



	

	
Block 1: metadata (from column 1 to 71)
	
	

	
Block 2: original counts (from column 72 to 274)
	
	

	
Block 3: generated counts based on the validated taxonomy (from column 275 to 331). We added “_VT” to each species name to distinguish it from other taxonomy levels. E.g. “g_bulloides” became “g_bulloides_VT”. The number of species counted per subsample is also reported in the column “number_of_species_counted_VT”
	
	

	
Block 4: generated counts based on the lumped taxonomy (from column 332 to 379). In this case, we added “_LT” to each species name. E.g. “n_dutertrei” became “n_dutertrei_VT”. We also calculated the number of species counted per subsample and reported it in the column “number_of_species_counted_LT”
	



Foraminifera abundance data counts are reported in different categories in the blocks 1,2 and 3 and described in the table below:



	
		
			
			
count_type
			
			
			
unit
			
		
		
			
			
Absolute
			
			
			
ind/m3
			
		
		
			
			
Relative
			
			
			
%
			
		
		
			
			
Raw
			
			
			
number of individuals
			
		
		
			
			
Fluxes
			
			
			
ind/m2/day
			
		
		
			
			
Bin_Absolute
			
			
			
ind/m3
			
		
		
			
			
Bin_Relative
			
			
			
%
			
		
		
			
			
Bin_Raw
			
			
			
number of individuals
			
		
		
			
			
Bin_Fluxes
			
			
			
ind/m2/day
			
		
	



 


For more details about the FORCIS database column description, please check the data descriptor paper Chaabane et al. (2023) (https://doi.org/10.1038/s41597-023-02264-2).


The database is kept open for any new entries and the updated version will be released in csv format. The labels of updated versions of the released “.csv” files will contain the date of their publication and versioning number.
DOI: 10.5281/zenodo.8366717
title: Data underlying: High-Field Optical Cesium Magnetometer for Magnetic Resonance Imaging

description: This is the data used in the work "High-Field Optical Cesium Magnetometer for Magnetic Resonance Imaging" by Hans Stærkind, Kasper Jensen, Vincent O. Boer, Esben Thade Petersen and Eugene S. Polzik. Scripts for calculations done in the article are also included.