cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18739/a2d21rm04
title: Interactive Multisensor Snow and Ice Mapping System Daily Northern Hemisphere Snow and Ice Analysis at 1 kilometer, 4 kilometer, and 24 kilometer Resolutions Version 1, northern hemisphere, 1997-present

description: This data set provides maps of snow cover and sea ice for the Northern Hemisphere from February 1997 to the present from the United States National Ice Center (USNIC). USNIC analysts produce these using the Interactive Multisensor Snow and Ice Mapping System (IMS). Maps are derived from a variety of data products including satellite imagery and in situ data. The data are provided in ASCII text and GeoTIFF formats in three different resolutions: 1 kilometer (km), 4 km, and 24 km.
 The purpose of the Interactive Multisensor Snow and Ice Mapping System (IMS) is to provide Satellite Analysis Branch (SAB) analysts with the ability to interactively create, save, and distribute highly accurate maps depicting the extent of daily hemispheric snow and ice coverage. In order to assist the user in making snow/ice determinations, the IMS incorporates a wide variety of satellite imagery (Advanced Very High Resolution Radiometer (AVHRR), Geostationary Operational Environmental Satellites (GOES), Special Sensor Microwave/Imager (SSMI), etc.) as well as derived mapped products (United States Air Force (USAF) Snow/Ice Analysis, advanced microwave sounding unit (AMSU), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), National Centers for Environmental Prediction (NCEP) models, etc.) and surface observations. The product is presently used as an operational input into several National Weather Service (NWS) computer weather prediction models as well as several other governmental agencies.
 U.S. National Ice Center. 2008, updated daily. IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. doi: https://doi.org/10.7265/N52R3PMC. [2020-11-06].
DOI: 10.5517/ccdc.csd.cc2g1rt3
title: CCDC 2265815: Experimental Crystal Structure Determination

description: Related Article: Ina Kopp, Patrick Cieslik, Karl Anger, Thomas Josephy, Lucca Neupert, Gunasekaran Velmurugan, Michael Gast, Hubert Wadepohl, Santiago Andrés Brühlmann, Martin Walther, Klaus Kopka, Michael Bachmann, Holger Stephan, Manja Kubeil, Peter Comba|2023|Inorg.Chem.|62|20754|doi:10.1021/acs.inorgchem.3c02340
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.10440483
title: Low-entropy Packed Binary Detection using Hardware Performance Counters

description: Malware analysis faces a critical challenge in accurately identifying packed executables, especially those with low entropy. Existing software-based solutions often fail in detecting packers used by malware, resulting in inaccurate classifications. To address this shortcoming, in this study we introduce a novel method usingHardware Performance Counters (HPCs) to facilitate the classification of binary packers due to HPCs’ minimal access overhead and ability to obviate the necessity for source code. We trained classic machine-learning models by selecting relevant hardware attributes associated with the unpacking procedure for detectingpackers used by low-entropy binary programs. Extensive experiments shows the substantial role played by Hardware Performance Counters in detecting binary packing characterized by low entropy,offering a promising avenue for further exploration and refinement of techniques in malware analysis

The following zip files are executables that represent low entropy versions of software packers using byte-padding. The name of the files are the names of the packers which are represened,  Acprotect, Armadillo, Aspack, Nspack, Pecompact, Petite, UPX, and Zprotect. These can be used to measure the unpacking process using hardware performance counters in order to test & train machine earning classifiers for accurate classification of low entropy packers.
DOI: 10.5281/zenodo.10440484
title: Low-entropy Packed Binary Detection using Hardware Performance Counters

description: Malware analysis faces a critical challenge in accurately identifying packed executables, especially those with low entropy. Existing software-based solutions often fail in detecting packers used by malware, resulting in inaccurate classifications. To address this shortcoming, in this study we introduce a novel method usingHardware Performance Counters (HPCs) to facilitate the classification of binary packers due to HPCs’ minimal access overhead and ability to obviate the necessity for source code. We trained classic machine-learning models by selecting relevant hardware attributes associated with the unpacking procedure for detectingpackers used by low-entropy binary programs. Extensive experiments shows the substantial role played by Hardware Performance Counters in detecting binary packing characterized by low entropy,offering a promising avenue for further exploration and refinement of techniques in malware analysis

The following zip files are executables that represent low entropy versions of software packers using byte-padding. The name of the files are the names of the packers which are represened,  Acprotect, Armadillo, Aspack, Nspack, Pecompact, Petite, UPX, and Zprotect. These can be used to measure the unpacking process using hardware performance counters in order to test & train machine earning classifiers for accurate classification of low entropy packers.
DOI: 10.5281/zenodo.10443562
title: Low-entropy Packed Binary Detection using Hardware Performance Counters

description: Malware analysis faces a critical challenge in accurately identifying packed executables, especially those with low entropy. Existing software-based solutions often fail in detecting packers used by malware, resulting in inaccurate classifications. To address this shortcoming, in this study we introduce a novel method usingHardware Performance Counters (HPCs) to facilitate the classification of binary packers due to HPCs’ minimal access overhead and ability to obviate the necessity for source code. We trained classic machine-learning models by selecting relevant hardware attributes associated with the unpacking procedure for detectingpackers used by low-entropy binary programs. Extensive experiments shows the substantial role played by Hardware Performance Counters in detecting binary packing characterized by low entropy,offering a promising avenue for further exploration and refinement of techniques in malware analysis

The following zip files are executables that represent low entropy versions of software packers using byte-padding. The name of the files are the names of the packers which are represened,  Acprotect, Armadillo, Aspack, Nspack, Pecompact, Petite, UPX, and Zprotect. These can be used to measure the unpacking process using hardware performance counters in order to test & train machine earning classifiers for accurate classification of low entropy packers.
DOI: 10.5281/zenodo.10443546
title: Midway RSS-related files backup (Data, cd2015)
DOI: 10.5281/zenodo.10443547
title: Midway RSS-related files backup (Data, cd2015)
DOI: 10.6084/m9.figshare.22144976.v1
title: Test data

description: The scRNA-seq data（including gene expression matrix and cell annotation）
DOI: 10.34740/kaggle/dsv/7305638
title: BBC News
DOI: 10.5281/zenodo.10443341
title: Midway RSS-related files backup (Data, fg2012)
DOI: 10.5281/zenodo.10443342
title: Midway RSS-related files backup (Data, fg2012)
DOI: 10.5281/zenodo.10443498
title: Midway RSS-related files backup (Data, ibd2015)
DOI: 10.5281/zenodo.10443497
title: Midway RSS-related files backup (Data, ibd2015)
DOI: 10.5281/zenodo.10443590
title: Midway RSS-related files backup (Data, cad2015)
DOI: 10.5281/zenodo.10443591
title: Midway RSS-related files backup (Data, cad2015)
DOI: 10.34740/kaggle/dsv/7305796
title: WallStreetBets 2022
DOI: 10.5281/zenodo.10443595
title: Midway RSS-related files backup (Data, bmi2015)
DOI: 10.5281/zenodo.10443596
title: Midway RSS-related files backup (Data, bmi2015)
DOI: 10.5281/zenodo.8012991
title: Supplement to: Absolute age and temperature of belemnite rostra: constrains on the Early Cretaceous cooling event.

description: U-Pb data, stable carbon and oxygen isotopic data, clumped isotope data raw, and Sr-isotopic data
DOI: 10.5281/zenodo.8012992
title: Supplement to: Absolute age and temperature of belemnite rostra: constrains on the Early Cretaceous cooling event.

description: U-Pb data, stable carbon and oxygen isotopic data, clumped isotope data raw, and Sr-isotopic data
DOI: 10.6084/m9.figshare.23787279.v1
title: A deep learning-based method for predicting the emerging degree of research topics using emerging index

description: This is the experimental data of the manuscript entitled “A deep learning-based method for predicting the emerging degree of research topics using emerging index” .
DOI: 10.5281/zenodo.10443677
title: Supplement to: Absolute age and temperature of belemnite rostra: constrains on the Early Cretaceous cooling event.
DOI: 10.5281/zenodo.10443676
title: Supplement to: Absolute age and temperature of belemnite rostra: constrains on the Early Cretaceous cooling event.
DOI: 10.5281/zenodo.10443599
title: Midway RSS-related files backup (Data, hr2013)
DOI: 10.5281/zenodo.10443600
title: Midway RSS-related files backup (Data, hr2013)
DOI: 10.5281/zenodo.10443655
title: Midway RSS-related files backup (Data, height2014, stage2)
DOI: 10.5281/zenodo.10443654
title: Midway RSS-related files backup (Data, height2014, stage2)
DOI: 10.6084/m9.figshare.24919818
title: CityAgent mock data

description: CityAgent mock data :<br>Mock data, we organize it by PASCAL VOC (The PASCAL Visual Object Classes)
DOI: 10.6084/m9.figshare.24919818.v1
title: CityAgent mock data

description: CityAgent mock data :<br>Mock data, we organize it by PASCAL VOC (The PASCAL Visual Object Classes)
DOI: 10.5281/zenodo.10443661
title: Stonhenge Rays

description: Ray-centric dataset derived from the images in Stonehenge synthetic dataset from the [Stanford NeRF Navigation project](https://mikh3x4.github.io/nerf-navigation/).

Contains x and y, both numpy arrays:



x.shape = (199, 800, 800, 6), ray origins and directions for 199 images, all 800*800 resolution.

y.shape = (199, 800, 800, 3)), associated colors
DOI: 10.5281/zenodo.10443662
title: Stonhenge Rays

description: Ray-centric dataset derived from the images in Stonehenge synthetic dataset from the [Stanford NeRF Navigation project](https://mikh3x4.github.io/nerf-navigation/).

Contains x and y, both numpy arrays:



x.shape = (199, 800, 800, 6), ray origins and directions for 199 images, all 800*800 resolution.

y.shape = (199, 800, 800, 3)), associated colors
DOI: 10.5281/zenodo.10443764
title: The Joint Land Data Assimilation System:Data for the ROSATM road weather stations and AWS sites in Beijing municipal area
DOI: 10.25582/data.2022-12.2657997/2263543
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700411/2263544
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659676/2263545
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700532/2263546
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700775/2263547
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660530/2263548
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700979/2263549
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658595/2263550
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658022/2263551
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700468/2263552
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701249/2263553
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660762/2263554
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701032/2263555
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658117/2263556
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659975/2263557
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2665066/2263558
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701247/2263559
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660258/2263560
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701231/2263561
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700759/2263562
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701357/2263563
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701041/2263564
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701323/2263565
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660631/2263566
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660716/2263567
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2652110/2263568
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2653903/2263569
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659124/2263570
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659350/2263571
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658478/2263572
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658198/2263573
title: Data for EMSL Project 51287 from December 2022
DOI: 10.5281/zenodo.10443765
title: The Joint Land Data Assimilation System:Data for the ROSATM road weather stations and AWS sites in Beijing municipal area
DOI: 10.25582/data.2022-12.2660876/2263574
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2673313/2263575
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659241/2263576
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700347/2263577
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659922/2263578
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2651532/2263579
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2658809/2263580
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658396/2263581
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660264/2263582
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701452/2263583
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701534/2263584
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660451/2263585
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2651964/2263586
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2700717/2263587
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701347/2263588
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2651556/2263589
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-11.2652058/2263590
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2654821/2263591
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659098/2263592
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660799/2263593
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2657998/2263594
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658326/2263595
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701354/2263596
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700916/2263597
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660477/2263598
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659473/2263599
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700891/2263600
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658063/2263601
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2669530/2263602
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2655954/2263603
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658132/2263604
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660219/2263605
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658146/2263606
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659238/2263607
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659863/2263608
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2651386/2263609
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2656492/2263610
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659666/2263611
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700828/2263612
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2652076/2263613
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2658791/2263614
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2651534/2263615
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2658120/2263616
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659723/2263617
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660796/2263618
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660485/2263619
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2652161/2263620
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2658314/2263621
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2667847/2263622
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701149/2263623
title: Data for EMSL Project 51287 from December 2022
DOI: 10.6084/m9.figshare.24919935
title: Falls data

description: These files represent minimal, de-identified, aggregated data used to report baseline characteristics, our primary outcome data, and plot propensity score density curves and cumulative incidence for falls for our study regarding spinal manipulative therapy and fall risk.
DOI: 10.6084/m9.figshare.24919935.v1
title: Falls data

description: These files represent minimal, de-identified, aggregated data used to report baseline characteristics, our primary outcome data, and plot propensity score density curves and cumulative incidence for falls for our study regarding spinal manipulative therapy and fall risk.
DOI: 10.25582/data.2022-12.2700941/2263624
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2652362/2263625
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2658027/2263626
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658726/2263627
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2700776/2263628
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2652016/2263629
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2658725/2263630
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660421/2263631
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701212/2263632
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659351/2263633
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659940/2263634
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658647/2263635
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658870/2263636
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660146/2263637
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2652036/2263638
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-11.2652181/2263639
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2658401/2263640
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2672079/2263641
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701523/2263642
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-11.2651994/2263643
title: Data for EMSL Project 51287 from November 2022
DOI: 10.25582/data.2022-12.2658068/2263644
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659543/2263645
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2657984/2263646
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701524/2263647
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658783/2263648
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660003/2263649
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2667048/2263650
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2665437/2263651
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659400/2263652
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658620/2263653
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701517/2263654
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701182/2263655
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658571/2263656
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2660323/2263657
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701568/2263658
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658463/2263659
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658944/2263660
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659053/2263661
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701431/2263662
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659111/2263663
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701129/2263664
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2701272/2263665
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658256/2263666
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2658823/2263667
title: Data for EMSL Project 51287 from December 2022
DOI: 10.25582/data.2022-12.2659042/2263668
title: Data for EMSL Project 51287 from December 2022