cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21551295
title: Data for Siracusa et al. 2022 PNAS - "Within-individual changes reveal increasing social selectivity with age in rhesus macaques"

description: Data to recreate analyses found in Siracusa et al. 2022 PNAS - "Within-individual changes reveal increasing social selectivity with age in rhesus macaques".
DOI: 10.6084/m9.figshare.21551295.v1
title: Data for Siracusa et al. 2022 PNAS - "Within-individual changes reveal increasing social selectivity with age in rhesus macaques"

description: Data to recreate analyses found in Siracusa et al. 2022 PNAS - "Within-individual changes reveal increasing social selectivity with age in rhesus macaques"".
DOI: 10.34740/kaggle/dsv/4878896
title: OntoUML/UFO Catalog
DOI: 10.6084/m9.figshare.21936459
title: Dataset S3 v2 (Figshare). ATP synthase core subunit, inhibitor, and assembly factor FASTA files.

description: Sequences from solved structures were used to conduct BLAST and HMMER searches into 219 organisms from the EukProt v3 database and tBLASTn queries into the NCBI TSA, WGS, and SRA databases. These hits were verified via reciprocal BLAST, phmmer, HHpred, Phyre2, SWISS-MODEL, and AlphaFold.
DOI: 10.6084/m9.figshare.21936480
title: coexistence data and figures for figshare.xlsx

description: Female body size of Phormia regina and Lucilia sericata as well as survival to adult at different densities
DOI: 10.6084/m9.figshare.21936480.v1
title: coexistence data and figures for figshare.xlsx

description: Female body size of Phormia regina and Lucilia sericata as well as survival to adult at different densities
DOI: 10.6084/m9.figshare.21936498
title: Data SH.xlsx

description: This data contains hormone profile and sex satisfaction on cirrhosis patietns.
DOI: 10.6084/m9.figshare.21936498.v1
title: Data SH.xlsx

description: This data contains hormone profile and sex satisfaction on cirrhosis patietns.
DOI: 10.5281/zenodo.7553005
title: Dataset: An empirical study on self-admitted technical debt in modern code review

description: <pre>This data was used in the IST paper "An Empirical Study on Self-Admitted Technical Debt in Modern Code Review". The program to use this data is published in GitHub (https://github.com/Yutaro-Kashiwa/ReviewSATD_RP) When you use this data in your research, please cite the following papers: ``` @article{Kashiwa:IST:2022:SATD_Review, author = {Yutaro Kashiwa and Ryoma Nishikawa and Yasutaka Kamei and Masanari Kondo and Emad Shihab and Ryosuke Sato and Naoyasu Ubayashi}, title = {An empirical study on self-admitted technical debt in modern code review}, journal = {Information and Software Technology}, volume = {146}, pages = {106855}, year = {2022}, url = {https://doi.org/10.1016/j.infsof.2022.106855}, doi = {10.1016/j.infsof.2022.106855} } ``` </pre>
DOI: 10.5281/zenodo.7553004
title: Dataset: An empirical study on self-admitted technical debt in modern code review

description: <pre>This data was used in the IST paper "An Empirical Study on Self-Admitted Technical Debt in Modern Code Review". The program to use this data is published in GitHub (https://github.com/Yutaro-Kashiwa/ReviewSATD_RP) When you use this data in your research, please cite the following papers: ``` @article{Kashiwa:IST:2022:SATD_Review, author = {Yutaro Kashiwa and Ryoma Nishikawa and Yasutaka Kamei and Masanari Kondo and Emad Shihab and Ryosuke Sato and Naoyasu Ubayashi}, title = {An empirical study on self-admitted technical debt in modern code review}, journal = {Information and Software Technology}, volume = {146}, pages = {106855}, year = {2022}, url = {https://doi.org/10.1016/j.infsof.2022.106855}, doi = {10.1016/j.infsof.2022.106855} } ``` </pre>
DOI: 10.34740/kaggle/dsv/4879046
title: 💥📰 Daily Breaking News Twitter Dataset
DOI: 10.34740/kaggle/dsv/4879068
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.6073/pasta/128eabc592716cc9805decef31e70515
title: GIS50 Coverages Defining the Konza Producer Data Sample Locations (1982-present)

description: These data show the sample locations for datasets pertaining to primary production at Konza Prairie. These data reference various treatments across Konza including varying burn frequencies, belowground plots, patch burn, exclosures, etc.Record type one (GIS500) contains sample locations for estimated standing crop biomass in various burning-grazing treatments (PABXX). Record type six (GIS505) contains sample locations for peak foliage biomass measured at the belowground plot experiments (PBBXX).Record type 11 and 12 contain the transect (GIS510) and plot (GIS511) locations for plots in the patch-burn experiments (PBGXX). Record type 16 (GIS515) contains the locations of exclosures used to sample primary productivity in bison grazed watersheds (PEB01). Record type 21 (GIS520) contains the locations of exclosures used to sample primary productivity in cattle grazed watersheds (PEB01X). Record type 26 (GIS525) contains the locations of sample sites for litterfall (PGLXX) collectors in the gallery forest. Record type 31 (GIS530) contains species composition transects, and (GIS531) provides locations for species transect plots in the patch-burn experiments for Konza Prairie. These data may be used in conjunction species composition (PVC01 and PVC02), primary production in grazing exclosures (PEB01, PEB01_X), soil chemistry and bulk density (NSC01) and primary production (PAB01). These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).
DOI: 10.34740/kaggle/ds/2829163
title: Bellabeat Capstone
DOI: 10.6073/pasta/e36743573fe8f56cdd453518a9d117b2
title: GIS55 GIS Coverages Defining the Konza HQ Irrigation System (1982-present)

description: These data show the components of the irrigation system near Konza Prairie HQ. Record types 1, 2, 3 and 4 demarcate the locations of the study plots heads (GIS550), transect lines (GIS551), irrigation lines (GIS552), and irrigation line joints (GIS553). Record types 4 and 5 describe the location of the storage piles (GIS554) and the irrigation reservoir (GIS555). This data may be used in conjunction with the Irrigation Transect Studies (WATXX) data. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).
DOI: 10.3886/e184169
title: Factors of parental investment in the home language environment in peri-urban China: A mixed methods study

description: This mixed methods study describes the home language environment and early child language skills among households in a low-socioeconomic-status, peri-urban district of Chengdu, China, and identifies factors influencing parental investment in the home language environment. And the data here is the quantitative raw data.
DOI: 10.3886/e184169v1
title: Factors of parental investment in the home language environment in peri-urban China: A mixed methods study

description: This mixed methods study describes the home language environment and early child language skills among households in a low-socioeconomic-status, peri-urban district of Chengdu, China, and identifies factors influencing parental investment in the home language environment. And the data here is the quantitative raw data.
DOI: 10.6073/pasta/4f11107cba0af18dc7b55f29f932f5ab
title: GIS60 GIS Coverages Defining Other Konza Sample and Research Areas (1982-present)

description: These data show locations of samples and research areas at Konza that do not fit under our standard classifications. GIS 600 contains the locations of the Hulbert plots on Konza Prairie. GIS605 contains locations for rainfall shelters, ramps, experimental streams, restoration plots, the weather station, grasshopper cages, the climate extremes project. Currently no associated LTER datasets exist for these locations. GIS 610 provides a record of the historic Konza gridded location system. Older datasets may reference these locations with a column letter and row number. GIS615 contains the location for the Clean Air Status and Trends Network (CASTNET) site on Konza Prairie. For more information, visit the following link: http://www.epa.gov/castnet/javaweb/site_pages/KNZ184.html. GIS620 contains the location for the USGS gauging station. These data may be used in conjunction with the Stream Discharge for Kings Creek Measured at USGS Gauging Station (ASD01) dataset. For more information, visit the following link: http://waterdata.usgs.gov/nwis/nwisman/?site_no=06879650. GIS630) and GIS635 contain the location and treatment information for two bison grant grazing experiments. Currently, no associated LTER datasets exist for these data. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).
DOI: 10.6073/pasta/9c966580f7cd330f9476f1e096f04b83
title: GIS60 GIS Coverages Defining Other Konza Sample and Research Areas (1982-present)

description: These data show locations of samples and research areas at Konza that do not fit under our standard classifications. GIS 600 contains the locations of the Hulbert plots on Konza Prairie. GIS605 contains locations for rainfall shelters, ramps, experimental streams, restoration plots, the weather station, grasshopper cages, the climate extremes project. Currently no associated LTER datasets exist for these locations. GIS 610 provides a record of the historic Konza gridded location system. Older datasets may reference these locations with a column letter and row number. GIS615 contains the location for the Clean Air Status and Trends Network (CASTNET) site on Konza Prairie. For more information, visit the following link: http://www.epa.gov/castnet/javaweb/site_pages/KNZ184.html. GIS620 contains the location for the USGS gauging station. These data may be used in conjunction with the Stream Discharge for Kings Creek Measured at USGS Gauging Station (ASD01) dataset. For more information, visit the following link: http://waterdata.usgs.gov/nwis/nwisman/?site_no=06879650. GIS630) and GIS635 contain the location and treatment information for two bison grant grazing experiments. Currently, no associated LTER datasets exist for these data. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).
DOI: 10.25582/data.2020-09.2064585/1909919
title: Data from EMSL Project 51595
DOI: 10.25582/data.2020-09.2064531/1909924
title: Data from EMSL Project 51595
DOI: 10.25582/data.2020-09.2059144/1910053
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059165/1910054
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059201/1910055
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063791/1910056
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059182/1910057
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059146/1910058
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064002/1910059
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064166/1910060
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059135/1910061
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063855/1910062
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059189/1910063
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064138/1910064
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064146/1910065
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064052/1910066
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064062/1910067
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059163/1910068
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059175/1910069
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059168/1910070
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059184/1910071
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059197/1910072
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063819/1910073
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059164/1910074
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059195/1910075
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059183/1910076
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064129/1910077
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064176/1910078
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059145/1910079
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059194/1910080
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063837/1910081
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059134/1910082
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059185/1910083
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064250/1910084
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064413/1910085
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063951/1910086
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064069/1910087
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063899/1910088
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063800/1910089
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063976/1910090
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059196/1910091
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063970/1910092
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064215/1910093
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063844/1910094
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063959/1910095
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064151/1910096
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064078/1910097
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059203/1910098
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063769/1910099
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063738/1910100
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063986/1910101
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059132/1910102
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059147/1910103
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064335/1910104
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064185/1910105
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059170/1910106
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063883/1910107
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059173/1910108
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064104/1910109
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063758/1910110
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064263/1910111
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063868/1910112
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059169/1910113
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064046/1910114
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064233/1910115
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064372/1910116
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059162/1910117
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064224/1910118
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064089/1910119
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063994/1910120
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064110/1910125
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059171/1910126
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064201/1910127
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059158/1910128
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063784/1910129
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059179/1910130
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064059/1910137
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059159/1910138
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059160/1910139
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063954/1910140
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059202/1910141
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063891/1910142
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059174/1910143
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059190/1910144
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059178/1910145
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064340/1910146
title: Data for EMSL Project 51595 from September 2020
DOI: 10.6073/pasta/2b2e9902c00373db2aa276832c47510c
title: GIS68 GIS Coverages of Konza Prairie Research Experiments in 2020

description: These data show locations for some experiments at Konza Prairie including: Chronic Addition of Nitrogen Gradient Experiment (ChANGE), Ghost Fire, Shrub Rainfall Manipulation Plots (ShRaMPs), sampling locations for ingrowth cores collected as part of the ShRaMPs experiment, Climate Extremes Experiment, Drought-Net, the Experimental Streams Experiment, the Nutrient Network Experiment, Phosphorous Plots experiment, the Vert-Invert experiment, and restoration areas.GIS680 defines the locations where the ChANGE experiment occurs on Konza Prairie. These data are to be used in conjunction with the NGE01 dataset.GIS681 defines the locations where the Ghost Fire experiment occurs on Konza Prairie. These data are to be used in conjunction with the GFE01.GIS682 defines locations where the ShRaMPs shelters occur on Konza Prairie.GIS683 defines locations where ingrowth cores were installed as part of the ShRaMPs experiment.GIS684 defines the locations where the Climate Extremes experiment occurs on Konza Prairie. These data are to be used in conjunction with the CEE01 dataset.GIS685 defines the locations where the Drought-Net experiment occurs on Konza Prairie.GIS686 defines the site where the Experimental Streams experiments occur on Konza Prairie.GIS687 defines the locations where the Nutrient Network experiment occurs on Konza Prairie. These data are to be used in conjunction with the NUT01 dataset.GIS688 defines the locations where the Phosphorous Plots experiment occurs on Konza Prairie. These data are to be used in conjunction with the PPL01 dataset.GIS689 defines the locations where the Vert-Invert experiment occurs on Konza Prairie. These data are to be used in conjunction with the VIR01 dataset.GIS690 contains locations of restoration areas. These data are to be used in conjunction with the HRE01, SPR01, and PRP01 datasets. These data are available to download as zipped shapefiles (.zip), and compressed Google Earth KML layers (.kmz).
DOI: 10.25582/data.2020-09.2063944/1910147
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064019/1910148
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064324/1910149
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059176/1910150
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2063746/1910151
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2064096/1910152
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2021-12.2408779/1910175
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408730/1910178
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408711/1910180
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408715/1910183
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2406173/1910184
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408565/1910189
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408802/1910195
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408700/1910201
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408547/1910206
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408704/1910208
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2406290/1910211
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408710/1910215
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408566/1910217
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408787/1910228
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2021-12.2408558/1910238
title: Data for EMSL Project 51134 from December 2021
DOI: 10.7910/dvn/sbhjyg
title: Replication Data for: Mitigating agricultural fires with carrot or stick? Evidence from China

description: Replication data for Mitigating agricultural fires with carrot or stick? Evidence from China
DOI: 10.25582/data.2021-12.2408564/1910245
title: Data for EMSL Project 51134 from December 2021
DOI: 10.25582/data.2020-09.2059109/1910250
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059131/1910251
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2051827/1910252
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059113/1910253
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059099/1910254
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059098/1910255
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059110/1910256
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059116/1910257
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2051821/1910258
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059100/1910259
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2051814/1910260
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059115/1910261
title: Data for EMSL Project 51595 from September 2020
DOI: 10.25582/data.2020-09.2059123/1910262
title: Data for EMSL Project 51595 from September 2020