cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p9tzldz2
title: Pesticides in pollinator tissue collected from margins near agricultural fields in Conservation Areas of Missouri

description: Wild bee and butterfly samples were collected from the margins of agricultural fields located on five Conservation Areas in Missouri. In 2016 and 2017, samples were collected and composited by genera for a total of 90 samples. Samples were extracted via pressurized liquid extraction and solid phase extraction cleanup. Samples were analyzed for 168 pesticides and degradates using both gas and liquid chromatography-tandem mass spectrometry. Overall, 16 pesticides were detected. Pesticides detected in greater than 2% of the composite samples included: metolachlor (24%), tebuconazole (22%), atrazine (18%), imidacloprid desnitro (13%), bifenthrin (9%), flumetralin (9%), p,p'-DDD (6%), tebupirimfos (4%), fludioxonil (4%), flutriafol (3%), cyproconazole (2%), and oxadiazon (2%). Concentrations for individual pesticides ranged from 2 to 174 ng/g. Results indicate that wild pollinators are exposed to a wide variety of pesticides.
DOI: 10.5281/zenodo.3906883
title: Supplemental Materials for MS Thesis - Crab Chapter

description: Supplemental files for linking to in my MS Thesis "Impacts of temperature on the molecular response of shellfish" - Crab Chapter (Chapter 2).
DOI: 10.5281/zenodo.3906882
title: Supplemental Materials for MS Thesis - Crab Chapter

description: Supplemental files for linking to in my MS Thesis "Impacts of temperature on the molecular response of shellfish" - Crab Chapter (Chapter 2).
DOI: 10.5281/zenodo.3755060
title: Facies Classification Benchmark

description: The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits. In addition, it is common for papers that apply machine learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have lead to subjective results and have greatly hindered the ability to compare different machine learning models against each other and understand the advantages and disadvantages of each approach. <br> To address these issues, we open-source a fully-annotated 3D geological model of the Netherlands F3 Block. This model is based on the study of the 3D seismic data in addition to 26 well logs, and is grounded on the careful study of the geology of the region. Furthermore, we propose two baseline models for facies classification based on a deconvolution network architecture and make their codes publicly available. Finally, we propose a scheme for evaluating different models on this dataset, and we share the results of our baseline models. In addition to making the dataset and the code publicly available, this work helps advance research in this area by creating an objective benchmark for comparing the results of different machine learning approaches for facies classification.
description: If you have found our code or data useful, we kindly ask you to cite our work. Our ArXiv preprint is: https://library.seg.org/doi/10.1190/INT-2018-0249.1 and our SEG Digital Library journal paper is: https://library.seg.org/doi/10.1190/INT-2018-0249.1 The BibTex file for our journal paper is copied below: @article{alaudah2019machine, author = {Yazeed Alaudah and Patrycja Michałowicz and Motaz Alfarraj and Ghassan AlRegib}, title = {A machine-learning benchmark for facies classification}, journal = {Interpretation}, volume = {7}, number = {3}, pages = {SE175-SE187}, year = {2019}, doi = {10.1190/INT-2018-0249.1}, URL = {https://doi.org/10.1190/INT-2018-0249.1}, eprint = {https://doi.org/10.1190/INT-2018-0249.1} }
DOI: 10.5281/zenodo.3755059
title: Facies Classification Benchmark

description: The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits. In addition, it is common for papers that apply machine learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have lead to subjective results and have greatly hindered the ability to compare different machine learning models against each other and understand the advantages and disadvantages of each approach. <br> To address these issues, we open-source a fully-annotated 3D geological model of the Netherlands F3 Block. This model is based on the study of the 3D seismic data in addition to 26 well logs, and is grounded on the careful study of the geology of the region. Furthermore, we propose two baseline models for facies classification based on a deconvolution network architecture and make their codes publicly available. Finally, we propose a scheme for evaluating different models on this dataset, and we share the results of our baseline models. In addition to making the dataset and the code publicly available, this work helps advance research in this area by creating an objective benchmark for comparing the results of different machine learning approaches for facies classification.
description: If you have found our code or data useful, we kindly ask you to cite our work. Our ArXiv preprint is: https://library.seg.org/doi/10.1190/INT-2018-0249.1 and our SEG Digital Library journal paper is: https://library.seg.org/doi/10.1190/INT-2018-0249.1 The BibTex file for our journal paper is copied below: @article{alaudah2019machine, author = {Yazeed Alaudah and Patrycja Michałowicz and Motaz Alfarraj and Ghassan AlRegib}, title = {A machine-learning benchmark for facies classification}, journal = {Interpretation}, volume = {7}, number = {3}, pages = {SE175-SE187}, year = {2019}, doi = {10.1190/INT-2018-0249.1}, URL = {https://doi.org/10.1190/INT-2018-0249.1}, eprint = {https://doi.org/10.1190/INT-2018-0249.1} }
DOI: 10.25919/5ef3ae9a22800
title: Parkes observations for project P1062 semester 2020APRS_02

description: We propose to monitor the potential repeating FRBs in the Southern Sky with luminosity di- chotomy criterion. With a success of luminosity-guided detection of a repeating source by FAST, we can continue to search for more repeaters hidden in current FRB sample according to luminosity dichotomy. Specifically, we select FRB 171020 and FRB 180923 as the targets of proposed obser- vations, which are located in the repeater population in the luminosity function. We demonstrate that the observing with 20 hours for each source is optimistic using Parkes ultra-wide-bandwidth receiver (UWL), if there are repeaters among them.
DOI: 10.5066/p98s3sbv
title: Isotopic Analysis of Clear Lake Hitch collected from Clear Lake, Lake County, California, 2017

description: This dataset includes data from isotopic analysis of eye and muscle tissue from fish collected in Clear Lake, Lake County, California during June and July 2017. This data release includes all biological measurements and fish taxa included in the analysis.
DOI: 10.25592/uhhfdm.1077
title: 3D Model of a Acer pseudoplatanus L.

description: Acer pseudoplatanus_Berg-Ahorn<br> scientific name: Acer pseudoplatanus L.<br> vernacular name: Berg-Ahorn object: Holzbibliothek in Buchform gefertigt aus Holz von Berg-Ahorn, Buchrücken mit Rinde, Deckel mit Habitusaufnahme, innen mit belaubten Zweigen, Blüten und Früchten sowie für den Berg-Ahorn charakteristische Pilze
DOI: 10.25919/5ef3bfe1d25dc
title: Parkes observations for project P1069 semester 2020APRS_04

description: We propose to carry out Parkes follow-up observations of pulsar candidates identified in the Rapid ASKAP Continuum Survey (RACS). This is the first all-sky pulsar survey in the imaging domain in the southern hemisphere thanks to ASKAP. Eight sources have been selected because of (1) their very high fraction of circular polarisation; (2) their lack of optical counterparts; (3) strong variability, which might indicate either strong interstellar scintillation or eclipsing by companion stars if in binaries; (4) the non-detection of these relatively bright sources in previous pulsar surveys, which might indicate extreme nature of these candidates. High time and frequency resolution targeted search with UWL are proposed. Such observations will enable us to carry out deep searches of pulsars with very short spin periods or in compact binaries.
DOI: 10.7910/dvn/nqgfwj/ne7kxv
title: vdemaiddatamerge.do

description: :unav
DOI: 10.7910/dvn/nqgfwj/ffqr7g
title: V-Dem Codebook v8.pdf

description: :unav
DOI: 10.7910/dvn/nqgfwj/flbq1l
title: creating2001-2018replication.do

description: :unav
DOI: 10.7910/dvn/nqgfwj/apraec
title: qog_std_jan20.pdf

description: :unav
DOI: 10.7910/dvn/nqgfwj/dgs7nk
title: analysis.R

description: :unav
DOI: 10.7910/dvn/nqgfwj/rrt5tl
title: V-Demnotes.txt

description: :unav
DOI: 10.7910/dvn/nqgfwj/dram3i
title: ODA20002018allanddgtrimmed.tab

description: :unav
DOI: 10.7910/dvn/nqgfwj/b3pmgi
title: Codebook_Phase_2.pdf

description: :unav
DOI: 10.7910/dvn/nqgfwj/c0rpvc
title: Finkelreplication.do

description: :unav
DOI: 10.7910/dvn/nqgfwj/oaul5r
title: generateodadata.do

description: :unav
DOI: 10.7910/dvn/nqgfwj/rrpyvw
title: V-Dem Codebook v9.pdf

description: :unav
DOI: 10.7910/dvn/nqgfwj/taoxqg
title: QoG.do

description: :unav
DOI: 10.7910/dvn/nqgfwj/hh3ivh
title: oda20002018.tab

description: :unav
DOI: 10.7910/dvn/nqgfwj/vhb2w0
title: USAID20012018regionsplit.tab

description: :unav
DOI: 10.7910/dvn/nqgfwj/vvflyi
title: V-Dem-CY+Others-v8cowchange.tab

description: :unav
DOI: 10.7910/dvn/nqgfwj/hmwqe0
title: USAIDdata20022018.tab

description: :unav
DOI: 10.7910/dvn/nqgfwj/iureh0
title: QoGnote.txt

description: :unav
DOI: 10.7910/dvn/nqgfwj/wwjeau
title: generateregionaldatastub.do

description: :unav
DOI: 10.7910/dvn/nqgfwj/i77ayu
title: readme.txt

description: :unav
DOI: 10.7910/dvn/nqgfwj/utyxil
title: prepped-gensynth.csv

description: :unav
DOI: 10.7910/dvn/nqgfwj/xyd3od
title: finkelmerge.tab

description: :unav
DOI: 10.7910/dvn/nqgfwj/o1szhr
title: LICENSE.txt

description: :unav
DOI: 10.7910/dvn/nqgfwj/v4xisv
title: DataDictionary_ForeignAidExplorer.pdf

description: :unav
DOI: 10.7910/dvn/nqgfwj/bputp5
title: us_foreign_aid_completecsv.csv

description: :unav
DOI: 10.7910/dvn/nqgfwj/bwmvpr
title: FPPcreation.do

description: :unav
DOI: 10.7910/dvn/nqgfwj/nmqjn8
title: extension.do

description: :unav
DOI: 10.7910/dvn/nqgfwj/8ltqu3
title: USAID_DG_Programs.tab

description: :unav
DOI: 10.7910/dvn/nqgfwj/gwi8oc
title: FPPdata20012018.tab

description: :unav
DOI: 10.7910/dvn/nqgfwj/ghobzt
title: QoGdata20012018.tab

description: :unav
DOI: 10.7910/dvn/nqgfwj/gdqtcv
title: V-Dem-CY+Others-v8.tab

description: :unav
DOI: 10.7910/dvn/nqgfwj
title: Replication Data for: Democracy Promotion and Electoral Quality: A Disaggregated Analysis

description: The international community spends significant sums of money on democracy promotion, focusing especially on producing competitive and transparent electoral environments. In theory, aid empowers a variety of actors, increasing competition and government responsiveness. We argue that to fully understand the effect of aid on democratization one must consider how democracy aid affects specific country institutions. Building on theory from the democratization and democracy promotion literature, we specify more precise causal linkages between democracy assistance and elections. Specifically, we hypothesize about the effects of democracy aid on the implementation and quality of elections. We test these hypotheses using V-Dem's detailed elections measures, using Finkel, Pérez-Liñán, Seligson’s (2007) data and modeling strategy, to examine the impact of democracy aid. Intriguingly, we find that there is no consistent relationship between democracy and governance aid and the improvement of disaggregated indicators of election quality, but aggregate measures still capture a relationship. We suggest that current evidence is more consistent with election-enhancing aid following democratization than with democratization following such aid.
DOI: 10.7910/dvn/nqgfwj/sp5wd0
title: finkelreplicationdata20012018.tab

description: :unav
DOI: 10.5281/zenodo.3906864
title: SPARC Reanalysis Intercomparison Project (S-RIP) reanalysis common grid files

description: This is the reanalysis common grid data set produced as part of the SPARC (Stratosphere-troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (S-RIP). These data have been used in several chapters in the S-RIP report, including Chapter 4 (Overview of Ozone and Water Vapou<strong>r</strong> - See Davis et al., ACP, 2017) and Chapter 8 (Tropical Tropopause Layer - See Tegtmeier et al., ACP, 2020). Notes about this data set: - Data set size: ~26 GB<br> - Reanalyses included are CFSR, ERA-I, JRA-25, JRA-55, MERRA, MERRA-2<br> - Variables include T (variable name ta), u (ua), v (va), O3 (tro3), WV (hus), and GPH (zg)<br> - Data are based off of the ana4mips data set<br> - Data are on a 2.5° x 2.5° grid<br> - All values are monthly means<br> - Pressure levels are (1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1, .7, .5, .3, .1 hPa)<br> - 3 time periods are provided:<br> - The “full” time period (different for each reanalysis)<br> - The “S-RIP base period” (i.e., 197901-201312)<br> - The “climatology” period (198101-201012)<br> - Both 3D (lon,lat,level) and zonal mean 2D (lat, level) files are provided<br> - Both timeseries and climatology data are provided. Files with “ltm” in the name are climatologies<br> - Reanalysis “Ensemble” files are located in the /ensemble directory (See note below*) <br> *Regarding the “ensemble” data — I highly recommend using the “ensemble” files in the “ensemble” folder for climatological intercomparisons.<br> You can use a single file (e.g., hus_Amon_reanalysis_ENS_198101-201012.cg.ltm.nc for water vapor) that contains the climatologies for each reanalysis.<br> The data in these files are of dimension (lon, lat, level,time(==12 months ), “record”), where record 0 = CFSR, 1=ERA-I, 2=JRA-25, 3=JRA-55, 4=MERRA, 5=MERRA2. There is also a corresponding “ensemble mean” file (e.g., hus_Amon_reanalysis_ENS_198101-201012.cg.ltm.ensmean.nc) that is simply the ensemble mean climatology from the 4 core reanalyses (CFSR, ERA-I, JRA-55, MERRA — EXCLUDING JRA-25 & MERRA2).
DOI: 10.5281/zenodo.3754752
title: SPARC Reanalysis Intercomparison Project (S-RIP) reanalysis common grid files

description: This is the reanalysis common grid data set produced as part of the SPARC (Stratosphere-troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (S-RIP). These data have been used in several chapters in the S-RIP report, including Chapter 4 (Overview of Ozone and Water Vapou<strong>r</strong> - See Davis et al., ACP, 2017) and Chapter 8 (Tropical Tropopause Layer - See Tegtmeier et al., ACP, 2020). Notes about this data set: - Data set size: ~26 GB<br> - Reanalyses included are CFSR, ERA-I, JRA-25, JRA-55, MERRA, MERRA-2<br> - Variables include T (variable name ta), u (ua), v (va), O3 (tro3), WV (hus), and GPH (zg)<br> - Data are based off of the ana4mips data set<br> - Data are on a 2.5° x 2.5° grid<br> - All values are monthly means<br> - Pressure levels are (1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1, .7, .5, .3, .1 hPa)<br> - 3 time periods are provided:<br> - The “full” time period (different for each reanalysis)<br> - The “S-RIP base period” (i.e., 197901-201312)<br> - The “climatology” period (198101-201012)<br> - Both 3D (lon,lat,level) and zonal mean 2D (lat, level) files are provided<br> - Both timeseries and climatology data are provided. Files with “ltm” in the name are climatologies<br> - Reanalysis “Ensemble” files are located in the /ensemble directory (See note below*) <br> *Regarding the “ensemble” data — I highly recommend using the “ensemble” files in the “ensemble” folder for climatological intercomparisons.<br> You can use a single file (e.g., hus_Amon_reanalysis_ENS_198101-201012.cg.ltm.nc for water vapor) that contains the climatologies for each reanalysis.<br> The data in these files are of dimension (lon, lat, level,time(==12 months ), “record”), where record 0 = CFSR, 1=ERA-I, 2=JRA-25, 3=JRA-55, 4=MERRA, 5=MERRA2. There is also a corresponding “ensemble mean” file (e.g., hus_Amon_reanalysis_ENS_198101-201012.cg.ltm.ensmean.nc) that is simply the ensemble mean climatology from the 4 core reanalyses (CFSR, ERA-I, JRA-55, MERRA — EXCLUDING JRA-25 & MERRA2).
DOI: 10.21227/pfyb-wz24
title: BioMASS Space Model Dataset Benchmark

description: This dataset contains the results of the simulation runs of the experiments performed to evaluate and compare the proposed spatial model for situated multi-agent systems. The model was introduced in a paper entitled "BioMASS, a spatial model for situated multiagent systems that optimizes neighborhood search". In this paper we presented a new model to implement a spatially explicit environment that supports constant-time sensory (neighborhood search) and locomotion functions for situated multiagent systems. In contrast, the spatial models currently provided by agent-based modeling and computer simulation (ABMS) platforms have computational costs that grow quadratically with perception range and linearly with the number of agents. This model makes the functions largely independent of the environment size, perception range, and the number of agents. We conduct a theoretical and experimental study of BioMASS compared to other spatial models. Experiments performed using a prey-predator swarm model show that BioMASS significantly outperforms the continuous (Repast toolkit) and hybrid models (Mason toolkit) in terms of execution time, allowing for higher-resolution modeling and simulation of complex systems.
DOI: 10.25582/data.2021-10.2372695/2139425
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373569/2139426
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2372268/2139427
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373595/2139428
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373579/2139429
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2374449/2139430
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373582/2139431
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373610/2139432
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2371342/2139433
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373589/2139434
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373641/2139435
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2371563/2139436
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373456/2139437
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373414/2139438
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2371319/2139439
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373412/2139440
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2372253/2139441
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-10.2372582/2139442
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373368/2139443
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2372493/2139444
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373289/2139445
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373332/2139446
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2371324/2139447
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2374620/2139448
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373353/2139449
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2371564/2139450
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2374446/2139451
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2371436/2139452
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373447/2139453
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373430/2139454
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373520/2139455
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373369/2139456
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2374710/2139457
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373493/2139458
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373338/2139459
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2371348/2139460
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373466/2139461
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2371326/2139462
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373608/2139463
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373620/2139464
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373553/2139465
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373436/2139466
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373435/2139467
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2374447/2139468
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2372570/2139469
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373432/2139470
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373572/2139471
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373357/2139472
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373559/2139473
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2372380/2139474
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373364/2139475
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373570/2139476
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373564/2139477
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2371451/2139478
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373555/2139479
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373348/2139480
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373658/2139481
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-10.2371311/2139482
title: Data for EMSL Project 51287 from October 2021
DOI: 10.25582/data.2021-11.2373590/2139483
title: Data for EMSL Project 51287 from November 2021
DOI: 10.25582/data.2021-11.2373408/2139484
title: Data for EMSL Project 51287 from November 2021