cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24521842.v1
title: allhousein4miles_2.dta

description: We examine the impact of industrial park openings on the housing market in South Korea. First, the availability of multiple transactions within apartment complexes enables fixed-effects estimation and addresses unobserved heterogeneity across apartment complexes. Second, we trace the effect on housing values from the announcement stage to actual operation stage of industrial parks. Our results indicate housing prices positively respond to an announcement of industrial park opening, but less in areas close to the industrial park. Furthermore, housing prices in close proximity to industrial parks decline when industrial parks begin operation.
DOI: 10.5281/zenodo.10065157
title: Data Global Governance and Fragility
DOI: 10.5281/zenodo.10065156
title: Data Global Governance and Fragility
DOI: 10.5281/zenodo.10080979
title: Data Global Governance and Fragility
DOI: 10.5281/zenodo.10080347
title: SST forcing experiments
DOI: 10.5281/zenodo.10080346
title: SST forcing experiments
DOI: 10.5281/zenodo.10080998
title: Heting Hong_All Data_Experimental Assessment of Material Resistance to Freshwater Biofouling
DOI: 10.5281/zenodo.10080999
title: Heting Hong_All Data_Experimental Assessment of Material Resistance to Freshwater Biofouling
DOI: 10.5281/zenodo.10081021
title: Strong motion data of the 20230206 Mw6.6 aftershock
DOI: 10.5281/zenodo.10081020
title: Bridging seismic and aseismic slip during and after the complex rupture of the 2023 Kahramanmaraş earthquake doublet

description: Strong motion data of the 20230206 Mw6.6 aftershock(01:28:16 Nurdağı (Gaziantep) Earthquake)of the 2023 Mw 7.8 Nurdağı-Pazarcık earthquake
DOI: 10.5281/zenodo.10081034
title: Bridging seismic and aseismic slip during and after the complex rupture of the 2023 Kahramanmaraş earthquake doublet

description: Strong motion data of the 20230206 Mw6.6 aftershock(01:28:16 Nurdağı (Gaziantep) Earthquake)of the 2023 Mw 7.8 Nurdağı-Pazarcık earthquake
DOI: 10.5281/zenodo.7852529
title: Data and software: Stress and heat flux via automatic differentiation

description: glp-archive
Code and Data for "Stress and heat flux with automatic differentiation"
This repository contains data, code, and related artefacts supporting the following publication (preprint):
Stress and heat flux via automatic differentiation
by Marcel F. Langer, J. Thorben Frank, and Florian Knoop
J. Chem. Phys. 159, 174105 (2023) doi:10.1063/5.0155760
This repository is available at https://github.com/sirmarcel/glp-archive. Selected versions are archived on Zenodo, under doi:10.5281/zenodo.7852529.
Overview
Each subfolder in this repository contains a README.md with additional information. The subfolders are:

results/: Data and code that produced the figures in the manuscript
work/: Computational workflows, models, etc.
infra/: Project-specific infrastructure code
meta/: Scripts for assembling this archive; can be ignored but is retained for transparency.
Related external code
The work in this repository relies on a few tools that the authors maintain separately:

glp implements the quantities discussed in the manuscript
mlff implements the so3krates model
tools.mlff provides tools for the equation of state experiments
These tools were developed during the work in the manuscript. The following versions/tags reflect what was used to obtain results:

glp @ v0.1.0 (tag)
mlff @ v1.0 (branch)
mlff.tools @ v0.0.1
We additionally note that the GK-MD functionality has been factored out into gkx.
Versions

v1.1: published version, archived at doi:10.5281/zenodo.8406532
v1.0: arXiv submission v1, archived at doi:10.5281/zenodo.7852530
DOI: 10.5281/zenodo.7852530
title: Data and software: Stress and heat flux via automatic differentiation

description: <code>glp-archive</code> Code and Data for "Stress and heat flux with automatic differentiation" This repository contains data, code, and related artefacts supporting the following publication (preprint: <pre><code>Stress and heat flux via automatic differentiation by Marcel F. Langer, J. Thorben Frank, and Florian Knoop arXiv:2305.01401 </code></pre> This repository is available at https://github.com/sirmarcel/glp-archive. Selected versions are archived on Zenodo, under doi:10.5281/zenodo.7852530. Overview Each subfolder in this repository contains a <code>README.md</code> with additional information. The subfolders are: <code>results/</code>: Data and code that produced the figures in the manuscript <code>work/</code>: Computational workflows, models, etc. <code>infra/</code>: Project-specific infrastructure code <code>meta/</code>: Scripts for assembling this archive; can be ignored but is retained for transparency. Related external code The work in this repository relies on a few tools that the authors maintain separately: <code>glp</code> implements the quantities discussed in the manuscript <code>mlff</code> implements the so3krates model <code>tools.mlff</code> provides tools for the equation of state experiments These tools were developed during the work in the manuscript. The following versions/tags reflect what was used to obtain results: <code>glp @ v0.1.0</code> (tag) <code>mlff @ v1.0</code> (branch) <code>mlff.tools @ v0.0.1</code> We additionally note that the GK-MD functionality has been factored out into <code>gkx</code>. Versions <code>v1.0</code>: arXiv submission v1
DOI: 10.5281/zenodo.8406532
title: Data and software: Stress and heat flux via automatic differentiation

description: glp-archive
Code and Data for "Stress and heat flux with automatic differentiation"
This repository contains data, code, and related artefacts supporting the following publication (preprint):
Stress and heat flux via automatic differentiation
by Marcel F. Langer, J. Thorben Frank, and Florian Knoop
J. Chem. Phys. 159, 174105 (2023) doi:10.1063/5.0155760
This repository is available at https://github.com/sirmarcel/glp-archive. Selected versions are archived on Zenodo, under doi:10.5281/zenodo.7852529.
Overview
Each subfolder in this repository contains a README.md with additional information. The subfolders are:

results/: Data and code that produced the figures in the manuscript
work/: Computational workflows, models, etc.
infra/: Project-specific infrastructure code
meta/: Scripts for assembling this archive; can be ignored but is retained for transparency.
Related external code
The work in this repository relies on a few tools that the authors maintain separately:

glp implements the quantities discussed in the manuscript
mlff implements the so3krates model
tools.mlff provides tools for the equation of state experiments
These tools were developed during the work in the manuscript. The following versions/tags reflect what was used to obtain results:

glp @ v0.1.0 (tag)
mlff @ v1.0 (branch)
mlff.tools @ v0.0.1
We additionally note that the GK-MD functionality has been factored out into gkx.
Versions

v1.1: published version, archived at doi:10.5281/zenodo.8406532
v1.0: arXiv submission v1, archived at doi:10.5281/zenodo.7852530
DOI: 10.6084/m9.figshare.22811975.v3
title: <strong>A single-cell landscape of human liver transplantation reveals a pathogenic immune niche associated with early allograft dysfunction</strong>

description: <b>The complete database comprised the single-cell gene expression for samples taken from 7 patients, wherein liver grafts berfore liver transplantation (LT) were sampled after cold perfusion (CP) and liver grafts after LT were sampled affer 2h portal reperfusion (PP). Donors were from the healthy after brain death. RNA was extracted from the 12 samples derived from 3 patients without early allograft dysfunction (EAD) and 4 EAD patients and analyzed using 10X Genomics and Illumina sequencing technology.</b>
DOI: 10.6084/m9.figshare.24521950.v1
title: Molecular adaptation in the world's deepest‐living animal: Insights from transcriptome sequencing of the hadal amphipod <i>Hirondellea gigas</i>

description: Transcriptome data of the world-deepest animal Hirondellea gigas
DOI: 10.6084/m9.figshare.24521950
title: Molecular adaptation in the world's deepest‐living animal: Insights from transcriptome sequencing of the hadal amphipod <i>Hirondellea gigas</i>

description: Transcriptome data of the world-deepest animal Hirondellea gigas
DOI: 10.6084/m9.figshare.24354901
title: Data for Self-Attention Convolutional Long Short-Term Memory for Short-Term Arctic Sea Ice Motion Prediction Using Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz Data

description: The project includes the 2018 AMSR-E images and mask images that we used.
DOI: 10.6084/m9.figshare.24354901.v2
title: “使用先进微波扫描辐射计地球观测系统地球观测系统36.5 GHz数据进行短期北极海冰运动预测的自注意力卷积长短期记忆”数据

description: 该项目包括我们使用的 2018 AMSR-E 图像和掩模图像。
DOI: 10.6084/m9.figshare.24354901.v3
title: Data for Self-Attention Convolutional Long Short-Term Memory for Short-Term Arctic Sea Ice Motion Prediction Using Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz Data

description: The project includes the 2018 AMSR-E images and mask images that we used.
DOI: 10.57841/casdc.0003079
title: 惯性效应对超高速倾斜端面气膜密封稳动态特性影响
DOI: 10.6084/m9.figshare.24269353
title: Systems_ADN

description: We provide system parameters of two test systems that are used to verify our DNN-based method in the TF-ADN-OPF problem.
DOI: 10.6084/m9.figshare.24269353.v3
title: Systems_ADN

description: We provide system parameters of two test systems that are used to verify our DNN-based method in the TF-ADN-OPF problem.
DOI: 10.6084/m9.figshare.24269353.v2
title: Systems_DNN_TF_DN_OPF

description: We provide system parameters of two test systems that are used to verify our DNN-based method in the TF-ADN-OPF problem.
DOI: 10.57841/casdc.0003080
title: 丁腈橡胶紫外线臭氧照射亲水改性及其水润滑性能研究
DOI: 10.6084/m9.figshare.24522067
title: Systems_DNN_TF_DN_OPF

description: We provide the descriptions and parameters of test systems that have been used in the submission titled "Advancing Performance Guarantees for DNN-based OPF in Topology-flexible Distribution Networks".
DOI: 10.5519/qd.689bz2ng
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5717361 records)
DOI: 10.25934/pr00009081
title: Antidepressant effects of bupropion, varenicline, nicotine replacement therapy, and placebo in tobacco users.

description: Major depressive disorder (MDD) is one of the most common psychiatric conditions, affecting around 20.6% of adults in the United States. It is characterized by a persistent low mood and/or a loss of interest in normally pleasurable activities. MDD can also lead to changes in appetite, sleep difficulties, fatigue, trouble concentrating, feelings of guilt, and thoughts of suicide. Additionally, people with depression often smoke cigarettes at higher rates and have a greater risk of becoming dependent on nicotine.

The EAGLES trial was the largest randomized clinical trial to date exploring smoking cessation treatments for people with psychiatric conditions, including MDD. The trial evaluated the comparative safety and efficacy of varenicline, bupropion, and nicotine replacement therapies. Varenicline (Champix/Chantix) is a non-nicotine-based medication used for smoking cessation. It provides relief from craving and withdrawal symptoms and diminishes the pleasurable sensation derived from smoking. Bupropion (Wellbutrin, Zyban) is an ‘atypical’ antidepressant that is also used for smoking cessation. It is known to act on two neurotransmitters called noradrenaline and dopamine, but its mechanism of action is not fully understood. Nicotine replacement therapies are alternative sources of nicotine (e.g. gum, nasal sprays, transdermal patches, sublingual tablets) that are used to reduce cravings and ease withdrawal symptoms. These three treatments were found to be well-tolerated and effective for helping the EAGLES trial participants quit smoking. In addition to the main analyses, several sub-analyses have been conducted, focusing on how well the treatments worked within specific groups of participants with different psychiatric conditions, most recently in MDD.

Our project aims to build upon these findings by investigating the differential antidepressant effects of these three treatments compared to a placebo (any treatment that has no active properties, such as a sugar pill). Based on the already-published EAGLES trial data, we hypothesize that none of the trial's treatments will show significantly better antidepressant effects than placebo. This finding would be significant, since bupropion is an evidenced-based treatment for depression itself. To test our main hypothesis, we will use a statistical model (called “Mixed Effects Model”) that can account for individual variations in depression outcomes over time while also establishing whether or not there are differences between the three treatment groups compared to the placebo group. Our results will be valuable for physicians and researchers aiming to better understand the antidepressant effects of bupropion, in relation to other evidence-based treatments for tobacco use disorder, in a sample of smokers. We plan to publish our findings in a peer-reviewed scientific article and present any significant results at relevant academic conferences, such as the Canadian Psychiatric Association annual meeting.
DOI: 10.25582/data.2022-05.2509335/2189009
title: Data for EMSL Project 51545 from May 2022
DOI: 10.25582/data.2022-04.2479357/2189010
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-02.2440308/2189012
title: Data for EMSL Project 51545 from February 2022
DOI: 10.25582/data.2022-04.2479364/2189014
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-04.2500390/2189016
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-05.2511818/2189017
title: Data for EMSL Project 51545 from May 2022
DOI: 10.25582/data.2022-05.2509035/2189018
title: Data for EMSL Project 51545 from May 2022
DOI: 10.25582/data.2022-04.2496848/2189019
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-05.2508623/2189020
title: Data for EMSL Project 51545 from May 2022
DOI: 10.25582/data.2022-04.2479358/2189021
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-05.2508073/2189022
title: Data for EMSL Project 51545 from May 2022
DOI: 10.25582/data.2022-03.2450936/2189023
title: Data for EMSL Project 51545 from March 2022
DOI: 10.25582/data.2022-04.2501356/2189024
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-04.2498649/2189025
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-04.2501179/2189026
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-04.2495959/2189027
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-05.2513623/2189028
title: Data for EMSL Project 51545 from May 2022
DOI: 10.25582/data.2022-04.2496154/2189029
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-04.2500956/2189030
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-04.2499228/2189032
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-05.2509797/2189033
title: Data for EMSL Project 51545 from May 2022
DOI: 10.25582/data.2022-04.2501243/2189034
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-05.2509743/2189035
title: Data for EMSL Project 51545 from May 2022
DOI: 10.25582/data.2022-04.2496089/2189036
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-04.2501026/2189037
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-04.2495982/2189038
title: Data for EMSL Project 51545 from April 2022
DOI: 10.25582/data.2022-05.2509103/2189039
title: Data for EMSL Project 51545 from May 2022
DOI: 10.25582/data.2022-08.2591958/2192256
title: Data for EMSL Project 60079 from August 2022
DOI: 10.25582/data.2022-05.2514517/2192257
title: Data for EMSL Project 60079 from May 2022
DOI: 10.25582/data.2022-06.2542862/2192258
title: Data for EMSL Project 60079 from June 2022
DOI: 10.25582/data.2022-05.2512636/2192259
title: Data for EMSL Project 60079 from May 2022
DOI: 10.25582/data.2022-05.2520856/2192260
title: Data for EMSL Project 60079 from May 2022
DOI: 10.25582/data.2022-06.2543051/2192261
title: Data for EMSL Project 60079 from June 2022
DOI: 10.25582/data.2022-04.2470809/2192262
title: Data for EMSL Project 60079 from April 2022
DOI: 10.25582/data.2022-06.2543128/2192263
title: Data for EMSL Project 60079 from June 2022
DOI: 10.25582/data.2022-06.2543420/2192264
title: Data for EMSL Project 60079 from June 2022
DOI: 10.25582/data.2022-07.2554358/2192265
title: Data for EMSL Project 60079 from July 2022
DOI: 10.25582/data.2022-05.2513228/2192266
title: Data for EMSL Project 60079 from May 2022
DOI: 10.25582/data.2022-06.2543150/2192267
title: Data for EMSL Project 60079 from June 2022
DOI: 10.25582/data.2022-06.2543567/2192268
title: Data for EMSL Project 60079 from June 2022
DOI: 10.25582/data.2022-06.2542896/2192269
title: Data for EMSL Project 60079 from June 2022
DOI: 10.25582/data.2022-08.2588611/2192270
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2535473/2192271
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-08.2588608/2192272
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2537174/2192273
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2537309/2192274
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-08.2588638/2192275
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2541525/2192276
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-09.2598700/2192277
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-06.2541516/2192278
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2535492/2192279
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2537053/2192280
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2540485/2192281
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-09.2596547/2192282
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-08.2589837/2192283
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2537360/2192284
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-09.2596166/2192285
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-08.2588645/2192286
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-09.2599050/2192287
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-09.2598962/2192288
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-08.2588591/2192289
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-09.2598315/2192290
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-09.2596694/2192291
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-06.2537338/2192292
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2021-10.2363980/2192293
title: Data for EMSL Project 60070 from October 2021
DOI: 10.25582/data.2022-06.2541329/2192294
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2537318/2192295
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2535518/2192296
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-09.2596553/2192297
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-08.2593504/2192298
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-09.2594100/2192299
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-08.2593322/2192300
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2535422/2192301
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-08.2588598/2192302
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-08.2588640/2192303
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2537333/2192304
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2021-10.2363679/2192305
title: Data for EMSL Project 60070 from October 2021
DOI: 10.25582/data.2022-08.2588607/2192306
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2537161/2192307
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-08.2588615/2192308
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-09.2595303/2192309
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-06.2540510/2192310
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2535375/2192311
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-09.2596499/2192312
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-09.2595858/2192313
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-09.2596245/2192314
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-06.2537488/2192315
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2021-10.2364717/2192316
title: Data for EMSL Project 60070 from October 2021
DOI: 10.25582/data.2022-09.2596513/2192317
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-08.2588630/2192318
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-09.2595013/2192319
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-06.2537303/2192320
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-08.2589820/2192321
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-09.2598907/2192322
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-06.2537167/2192323
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-09.2599088/2192324
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-08.2589839/2192325
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2538335/2192326
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2535221/2192327
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-08.2591737/2192328
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-09.2598307/2192329
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-06.2541682/2192330
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-08.2591532/2192331
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2540824/2192332
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2537392/2192333
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2537381/2192334
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-08.2588635/2192335
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2540320/2192336
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-06.2536958/2192337
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-09.2594927/2192338
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-08.2592136/2192339
title: Data for EMSL Project 60070 from August 2022
DOI: 10.25582/data.2022-06.2537297/2192340
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-09.2598896/2192341
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-06.2537282/2192342
title: Data for EMSL Project 60070 from June 2022
DOI: 10.25582/data.2022-09.2596280/2192343
title: Data for EMSL Project 60070 from September 2022
DOI: 10.25582/data.2022-06.2541422/2192344
title: Data for EMSL Project 60070 from June 2022