cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2021-07.2269826/1951166
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274265/1951167
title: Data for EMSL Project 50686 from July 2021
DOI: 10.15151/esrf-es-1030811109
title: Anisotropic strength in Cu/Nb nanolaminates: an in situ X-ray microdiffraction study during tensile testing

description: Accumulative roll bonding (ARB) of metal-metal nanolaminates (NL) is a very promising approaches to fabricate metallic nanomaterials with excellent strength and good ductility. Interface resistance to dislocation transmission is believed to be one of the important mechanisms responsible for these unique properties, but many open questions remain. In recent micro-beam bending and nanoindentation experiments on FCC/BCC Cu/Nb NLs we have shown an anisotropic sliding behavior linked to the strong in-plane texture originating from the ARB process and consistent with MD simulation. The aim of the present experiment is to follow in situ the local stresses and plastic events in Cu/Nb nanolaminates during tensile loading. While previous rare tensile testing experiments have been reported the in situ monitoring of local strain by X-ray Diffraction (XRD) has never been performed. This experiment is a continuation of HC-4460, which was dedicated to in situ shearing of interfaces in ARB NLs.
DOI: 10.15151/esrf-es-1030814352
title: Assessing the role of astrocytes in manganese developmental neurotoxicity

description: Manganese (Mn) is both an essential nutrient and a neurotoxic element. High Mn content in drinking water has been associated with lower intelligence quotient and Attention-Deficit Hyperactivity Disorders. Although there is growing concern about the developmental neurotoxicity of Mn, it has only recently begun to be explored. We hypothesize that astrocytes can protect neurons from Mn toxicity by protecting synapses, whereas when astrocytic protection is overwhelmed, Mn can reach synapses, presumably through competition with Ca signaling pathways. X-ray fluorescence microscopy will enable to map and quantify Mn distribution in co-cultures of neurons and astrocytes. Primary rat hippocampal neurons will be cultured alone, or with astrocytes and exposed to non-toxic or toxic Mn concentrations. We expect a reduced Mn content in neurons cultured with astrocytes. We will elucidate if Mn reaches the synaptic compartments when the astrocytic protection is overpassed at high Mn exposure levels.
DOI: 10.25582/data.2021-07.2267084/1951168
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267201/1951169
title: Data for EMSL Project 50686 from July 2021
DOI: 10.15151/esrf-es-1030391080
title: In situ XAS investigation of a novel sintering method for fabricating rare-earth-free permanent magnets

description: Developing rare-earth-free permanent magnets with competitive magnetic performance is a necessary step forward to enable an ecological transition based on clean energy generation and green mobility (wind turbines, e-vehicles). Our research group has recently patented a novel cold sintering method for fabricating rare-earth-free hexaferrite-based magnets, which allows reducing sintering temperature, and in turn, lowering the energy consumption in &gt;30% compared to the sintering methods used in industry. We intend to replicate this process in a diamond anvil cell (DAC) and follow it in situ based on time-resolved X-ray absorption spectroscopy (XAS) measurements, with the aim of: (i) gaining a deeper understanding of the process itself and of the various phase changes undergone, (ii) refining the preparation method to better control the produced material, ideally further reducing the sintering temperature.
DOI: 10.15151/esrf-es-1030392293
title: Rationalization of CO2 and H2 Adsorptions on Ni Alloyed Nanocrystals Using In Situ Bragg Coherent Diffraction Imaging

description: The goal of this experiment is to study the CO2 and H2 adsorption/desorption at the surface of single alloyed Ni crystals using Bragg Coherent X-ray Diffraction Imaging (BCDI) under various in situ (temperature, T and pressure, p) conditions. The 3D intensity distribution in reciprocal space in the vicinity of a selected Bragg reflection contains information about the shape and strain states of the nanostructure, thus revealing insight into the nature of nanoscale deformation following chemical stimuli, such as adsorption of gases or changes in adsorbate structure. Here, we aim at imaging the strain evolution of pure Ni and alloyed NiFe particles as a function of CO2/H2 gas pressure (from 100 mbar to 800 mbar) and temperature (from 300 K to 773 K). Ni-Fe particles have been demonstrated as the best candidates for an “all-inclusive” material of choice for the carbon dioxide hydrogenation reaction.
DOI: 10.25582/data.2021-07.2275358/1951170
title: Data for EMSL Project 50686 from July 2021
DOI: 10.15151/esrf-es-1030394047
title: 2D Shock focusing

description: We propose to apply a laser-driven 2D in-plane shock focusing technique developed by our group, that can reach ten’s of GPa with conventional mJ lasers, to scrutiny from time-resolved x-ray diffraction on the ID09 beamline, the shock loading (excitation, propagation and focusing) in crystalline samples. The measurements will be made on SrTiO3:Nb substrates, a very common substrate used for growth of thin- films, that can efficiently absorb the picosecond laser pulse and launch a ring-shaped surface shock-wave that acoustically focuses at the center of the ring where the pressure jumps very substantially. We intend to track the x-ray diffraction peaks shifts that correspond to in-plane shock stresses characteristics of standard surface (non-linear) acoustic waves. These studies will unveil the onset of the shock focusing and enable a very accurate calibration of the shock stresses for futur experiments on thin films of correlated materials deposited on SrTiO3:Nb substrates.
DOI: 10.15151/esrf-es-1030395172
title: Investigation on the Origin of Vanadium Catalyzed Electron Transfer-Oxygen Transfer Reactions in Liquid Phase via in-situ Valence to Core (v

description: Electron transfer (ET) and oxygen transfer (OT) reactions often occurs in enzyme catalyzed biological reactions for C-H bond activation and C-C bond cleavage. To follow the track of these type of reaction is very challenging with the conventional spectroscopic techniques. Hence, in the proposed study we plan to investigate on the role of V containing polyoxometalate (also known as heteropolyacids) towards ET-OT reactions with the help of high energy resolution fluorescence detected X-ray absorption near edge spectra (HERFD-XANES) and X-ray emission spectroscopy (XES). Moreover, the role of ion-exchanged and supported polyoxometalate (POM) will also be investigated during this study. It is also unclear how the POM is re-oxidized in presence of oxidants such as molecular oxygen or hydrogen peroxide, which we would like to find out throughout the proposed experiments.A successful outcome will certianly help other researchers to understand complex reactions.
DOI: 10.25582/data.2021-07.2273127/1951171
title: Data for EMSL Project 50686 from July 2021
DOI: 10.15151/esrf-es-1030396037
title: Complex 4f-5d metamagnetism of Nd2ZnIrO6

description: The combination of strong spin-orbit coupling, itinerancy and correlations in 5d transition metal oxides produces a wealth of emergent electronic behavior with potential for new functional phases. The magnetism of the double perovskite Nd2ZnIrO6 combines the disparate characters of 4f and 5d electrons, making it a model system to study local-itinerant duality. We have synthesized single crystals of this material and discovered an unusual magnetic behavior. Both 4f and 5d sublattices order at 16.5 K. In an applied field, the material undergoes a series of pronounced metamagnetic transitions, at 3.5 or 5.0 T (depending on the field direction), at around 20 T, and 50 T. The compound lends itself particularly well to ion-specific studies of magnetism, which we have already used in a resonant x-ray scattering study of the 3.5/5.0 T transition. Here, we propose to use the unique microcoil XMCD setup at ID24 to efficiently characterize the evolution of the Nd and Ir magnetic states up to 30T.
DOI: 10.15151/esrf-es-1030397492
title: Unravelling the ‘strange metal puzzle’ through the study of charge density fluctuations in overdoped cuprates

description: The strange metal phase, one of the grand challenges of strongly correlated systems, generally occurs close to a quantum critical point (QCP). This picture is also accepted in HTS: a QCP is postulated at p*, where the pseudogap ends at T→0. This scenario is overturned by recent experiments, hinting at a subordinal role of pseudogap. This either implies the absence of a QCP, or the presence of a QCP having different origin. To unravel this puzzle, we propose to study with RIXS charge density fluctuations (CDF), which permeate the strange metal at pp* and, if so, their doping dependence. The results will tell us if the HTS physics is connected to a quantum critical scenario, and if CDF are the quantum fluctuations related to the QCP.
DOI: 10.25582/data.2021-07.2269493/1951172
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273474/1951173
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276416/1951174
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268682/1951175
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269664/1951176
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270265/1951177
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267311/1951178
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267574/1951179
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269019/1951180
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276496/1951181
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275498/1951182
title: Data for EMSL Project 50686 from July 2021
DOI: 10.5281/zenodo.6371752
title: Reproducibility data for Yarza et al. 2022

description: Data to reproduce the results of Yarza et al. 2022 (submitted to ApJ). For the main reproducibility repository, see https://gitlab.com/ryarza/planetary-engulfment-hydro After downloading all the files, run unpack.py to extract them. The script will also put all grid2 files into the correct folder structure. As a result of size limitations, this repository contains only the hydrodynamical simulations used in the figures in the paper. Simulations at other times or for other parameters are available upon request.
DOI: 10.25582/data.2021-07.2269015/1951183
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270788/1951184
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270881/1951185
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269984/1951186
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270668/1951187
title: Data for EMSL Project 50686 from July 2021
DOI: 10.6084/m9.figshare.22126229
title: inflation

description: Iran's inflation
DOI: 10.6084/m9.figshare.22126229.v1
title: inflation

description: Iran's inflation
DOI: 10.25582/data.2021-07.2269072/1951188
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273951/1951189
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270104/1951190
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270359/1951191
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273519/1951192
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267140/1951193
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275647/1951194
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270502/1951195
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275587/1951196
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276471/1951197
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267508/1951198
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270421/1951199
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269380/1951200
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275516/1951201
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270264/1951202
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2266859/1951203
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269225/1951204
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268373/1951205
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268624/1951206
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267065/1951207
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275276/1951208
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268931/1951209
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267855/1951210
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273134/1951211
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267359/1951212
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273348/1951213
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274372/1951214
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274101/1951215
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276028/1951216
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267340/1951217
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269046/1951218
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275253/1951219
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268875/1951220
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270604/1951221
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273397/1951222
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273456/1951223
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275543/1951224
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273146/1951225
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268167/1951226
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273566/1951227
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2266865/1951228
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269796/1951229
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276324/1951230
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268177/1951231
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275526/1951232
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273157/1951233
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273237/1951234
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268665/1951235
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270779/1951236
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268612/1951237
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268598/1951238
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273110/1951239
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276540/1951240
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275842/1951241
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268881/1951242
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267119/1951243
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267331/1951244
title: Data for EMSL Project 50686 from July 2021
DOI: 10.5281/zenodo.7591310
title: Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

description: Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.
DOI: 10.5281/zenodo.7591311
title: Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

description: Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.
DOI: 10.5281/zenodo.7650566
title: Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

description: Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.
DOI: 10.25582/data.2021-07.2270837/1951245
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267143/1951246
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269326/1951247
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274546/1951248
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267965/1951249
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275499/1951250
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275720/1951251
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275541/1951252
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269271/1951253
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267419/1951254
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274409/1951255
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268933/1951256
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267361/1951257
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268467/1951258
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270411/1951259
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269950/1951260
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273032/1951261
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273922/1951262
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270292/1951263
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274080/1951264
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268008/1951265
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274164/1951266
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275930/1951267
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268048/1951268
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269210/1951269
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269654/1951270
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2274593/1951271
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273764/1951272
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2276001/1951273
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267561/1951274
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2275155/1951275
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269512/1951276
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2273203/1951277
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267346/1951278
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268645/1951279
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269274/1951280
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2268416/1951281
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269978/1951282
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2267603/1951283
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2269133/1951284
title: Data for EMSL Project 50686 from July 2021
DOI: 10.25582/data.2021-07.2270257/1951285
title: Data for EMSL Project 50686 from July 2021