cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8270364
title: VSDFullBody: The Virtual Skeleton Database Full Body CT Collection

description: Reupload of anonymized postmortem CT scans of the whole body originally published by Michael Kistler through the SICAS Medical Image Repository (smir.ch) as open access Virtual Skeleton Database (VSD). The CT datasets were provided by the forensic institutes of the universities of Bern and Zürich and shared under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license after ethical approval of the Cantonal Ethics Committee Bern. Further information can be found in:

Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R. & Büchler, P. The virtual skeleton database: an open access repository for biomedical research and collaboration. Journal of Medical Internet Research 15(11), e245; 10.2196/jmir.2930 (2013).
Kistler, M. A database framework to incorporate statistical variability in biomechanical simulations. PhD thesis. Faculty of Medicine of the University of Bern; boristheses.unibe.ch/916 (2014).
Due to ongoing difficulties in accessing the SMIR website a mirror of the original VSDFullBody datasets without any alterations is provided.
CAUTION: The VSD contains a few inconsistencies, such as duplicate CT datasets. The uploader is not connected to the SMIR or VSD and, therefore, not responsible for errors in the VSD. However, errors that the uploader recognized during the work with the VSD were logged in an Excel file: VSD_Comments.xlsx
Datasets of the VSD were used for the creation of surface models of the lower body's osseous anatomy. Further information can be found in: 

Fischer, M. C. M. Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans. Scientific Data 10, 763; 10.1038/s41597-023-02669-z (2023).
DOI: 10.5281/zenodo.8270365
title: VSDFullBody: The Virtual Skeleton Database Full Body CT Collection

description: Reupload of anonymized postmortem CT scans of the whole body originally published by Michael Kistler through the SICAS Medical Image Repository (smir.ch) as open access Virtual Skeleton Database (VSD). The CT datasets were provided by the forensic institutes of the universities of Bern and Zürich and shared under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license after ethical approval of the Cantonal Ethics Committee Bern. Further information can be found in:

Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R. & Büchler, P. The virtual skeleton database: an open access repository for biomedical research and collaboration. Journal of Medical Internet Research 15(11), e245; 10.2196/jmir.2930 (2013).
Kistler, M. A database framework to incorporate statistical variability in biomechanical simulations. PhD thesis. Faculty of Medicine of the University of Bern; boristheses.unibe.ch/916 (2014).
Due to ongoing difficulties in accessing the SMIR website a mirror of the original VSDFullBody datasets without any alterations is provided.
CAUTION: The VSD contains a few inconsistencies, such as duplicate CT datasets. The uploader is not connected to the SMIR or VSD and, therefore, not responsible for errors in the VSD. However, errors that the uploader recognized during the work with the VSD were logged in an Excel file: VSD_Comments.xlsx
Datasets of the VSD were used for the creation of surface models of the lower body's osseous anatomy. Further information can be found in: 

Fischer, M. C. M. Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans. Scientific Data 10, 763; 10.1038/s41597-023-02669-z (2023).
DOI: 10.5519/qd.n1yjmjjx
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (14701 records)
DOI: 10.6084/m9.figshare.24049305
title: Source Data

description: source data for each figure are provided
DOI: 10.6084/m9.figshare.24049305.v1
title: Source Data

description: source data for each figure are provided
DOI: 10.5281/zenodo.8279455
title: Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation

description: Summary statistics for "Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation" 
Files are organized in the following directory structure:
G2G/ - Summary statistics of G2G associations (SNPs, HLA, and gene-level analysis).   
HLA/ - Peptide binding prediction results
preS1_haplotypes/ - Resolved intra-host haplotypes of the preS1 binding region. 
DnDs/ - Calculation of intra-host positive selection, within the preS1 binding region.
DOI: 10.5281/zenodo.8279454
title: Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation

description: Summary statistics for "Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation" 
Files are organized in the following directory structure:
G2G/ - Summary statistics of G2G associations (SNPs, HLA, and gene-level analysis).   
HLA/ - Peptide binding prediction results
preS1_haplotypes/ - Resolved intra-host haplotypes of the preS1 binding region. 
DnDs/ - Calculation of intra-host positive selection, within the preS1 binding region.
DOI: 10.3886/e170401v1-142824
title: Code for: Reference Dependence in the Housing Market

description: We quantify reference dependence and loss aversion in the housing market using rich Danish administrative data. Our structural model includes loss aversion, reference dependence, financial constraints, and a sale decision, and matches key nonparametric moments, including a ``hockey stick" in listing prices with nominal gains, and bunching at zero realized nominal gains. Households derive substantial utility from gains over the original house purchase price; losses affect households roughly 2.5 times more than gains. The model helps explain the positive correlation between aggregate house prices and turnover, but cannot explain visible attenuation in reference dependence when households are more financially constrained.
DOI: 10.34740/kaggle/dsv/6885196
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.6084/m9.figshare.24500581
title: SRR3414630 (GSE80550) bam and index files

description: /////
DOI: 10.6084/m9.figshare.24500581.v1
title: SRR3414630 (GSE80550) bam and index files

description: /////
DOI: 10.34740/kaggle/dsv/6885425
title: Top Spotify Songs in 73 Countries (Daily Updated)
DOI: 10.34740/kaggle/dsv/6885452
title: Daily Trending Youtube Videos (113 Countries)
DOI: 10.34740/kaggle/dsv/6885498
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.5281/zenodo.10072096
title: Data from: Heuchera tuckasegeensis sp. nov. (Saxifragaceae), a New Species from Western North Carolina

description: A new species of Heuchera (Saxifragaceae) from western North Carolina is described and illustrated herein, including phylogenomic and morphological analyses of its closest allies in the Heuchera parviflora group. Heuchera tuckasegeensis is distinguished from the other Heuchera taxa based on its distinct morphological attributes (style and stamen length, petiole and laminar hair length, and degree of leaf lobing), phylogenetic position, habitat preferences, and edaphic substrate specialization.
DOI: 10.5281/zenodo.10072095
title: Data from: Heuchera tuckasegeensis sp. nov. (Saxifragaceae), a New Species from Western North Carolina

description: A new species of Heuchera (Saxifragaceae) from western North Carolina is described and illustrated herein, including phylogenomic and morphological analyses of its closest allies in the Heuchera parviflora group. Heuchera tuckasegeensis is distinguished from the other Heuchera taxa based on its distinct morphological attributes (style and stamen length, petiole and laminar hair length, and degree of leaf lobing), phylogenetic position, habitat preferences, and edaphic substrate specialization.
DOI: 10.5281/zenodo.8083112
title: Datasets for "Pulsational pair-instability supernovae in gravitational-wave and electromagnetic transients" from Hendriks et al 2023.

description: Data related to the paper "Pulsational pair-instability supernovae in gravitational-wave and electromagnetic transients" by Hendriks et al 2023 https://doi.org/10.1093/mnras/stad2857.

`EVENTS_V2.2.2_SEMI_HIGH_RES*.tar.gz: main PPISN prescription variation simulation results for the GW mergers. These contain configurations for the populations and the convolved merger results which in turn contain merger rates, merger properties and events that preceded the mergers (RLOF episodes, SNe). These results are used in figures 2, 3, 6, and 7.  Figure 8 uses the SFR used in one of these simulations.
`EVENTS_V2.2.2_MID_RES*.tar.gz`: PPISNe prescription variation results for the transient rate evolution. These contain configurations for the populations and the convolved merger results which in turn contain merger rates, merger properties and events that preceded the mergers (RLOF episodes, SNe). These results are used in figure 4.
`grid_single_mass_metallicity_data.tar.gz`: data containing single-star remnant-mass data as a function of initial mass vs. final mass for our fiducial model and three variations: Farmer 2019 PPISN prescription, M_extra_ppisn_ML=10 Msun (i.e. where 10 solarmass of additional mass loss occur for each PPISN), M_co_shift_ppisn=-5 Msun (i.e. the CO core mass range that undergoes PPISN is shifted to lower masses by 5 solarmass). This data is used in figure 5.
`schematic_overview_data.tar.gz`: data containing single-star remnant-mass data as a function of pre-SN core mass for our fiducial models and several variations: Farmer 2019 PPISN prescription, M_extra_ppisn_ML = 5 Msun, M_co_shift_ppisn=-5 Msun, M_co_shift_ppisn=+5 Msun. This data is used in figure 1.
`paper_ppisne_scripts-main.tar.gz`: git-repository that contains the routines to generate the figures. The readme in this script should contain enough information, but  relevant to the data here: the user needs to store the files contained in this zenodo repository in a directory that they point to at with an environment variable called `paper_PPISNe_Hendriks2023_data_dir`. These scripts are also hosted on https://gitlab.com/dhendriks/paper_ppisne_scripts
DOI: 10.5281/zenodo.8083111
title: Datasets for "Pulsational pair-instability supernovae in gravitational-wave and electromagnetic transients" from Hendriks et al 2023.

description: Data related to the paper "Pulsational pair-instability supernovae in gravitational-wave and electromagnetic transients" by Hendriks et al 2023 https://doi.org/10.1093/mnras/stad2857.

`EVENTS_V2.2.2_SEMI_HIGH_RES*.tar.gz: main PPISN prescription variation simulation results for the GW mergers. These contain configurations for the populations and the convolved merger results which in turn contain merger rates, merger properties and events that preceded the mergers (RLOF episodes, SNe). These results are used in figures 2, 3, 6, and 7.  Figure 8 uses the SFR used in one of these simulations.
`EVENTS_V2.2.2_MID_RES*.tar.gz`: PPISNe prescription variation results for the transient rate evolution. These contain configurations for the populations and the convolved merger results which in turn contain merger rates, merger properties and events that preceded the mergers (RLOF episodes, SNe). These results are used in figure 4.
`grid_single_mass_metallicity_data.tar.gz`: data containing single-star remnant-mass data as a function of initial mass vs. final mass for our fiducial model and three variations: Farmer 2019 PPISN prescription, M_extra_ppisn_ML=10 Msun (i.e. where 10 solarmass of additional mass loss occur for each PPISN), M_co_shift_ppisn=-5 Msun (i.e. the CO core mass range that undergoes PPISN is shifted to lower masses by 5 solarmass). This data is used in figure 5.
`schematic_overview_data.tar.gz`: data containing single-star remnant-mass data as a function of pre-SN core mass for our fiducial models and several variations: Farmer 2019 PPISN prescription, M_extra_ppisn_ML = 5 Msun, M_co_shift_ppisn=-5 Msun, M_co_shift_ppisn=+5 Msun. This data is used in figure 1.
`paper_ppisne_scripts-main.tar.gz`: git-repository that contains the routines to generate the figures. The readme in this script should contain enough information, but  relevant to the data here: the user needs to store the files contained in this zenodo repository in a directory that they point to at with an environment variable called `paper_PPISNe_Hendriks2023_data_dir`. These scripts are also hosted on https://gitlab.com/dhendriks/paper_ppisne_scripts
DOI: 10.34740/kaggle/dsv/6885572
title: arXiv Dataset
DOI: 10.6084/m9.figshare.24083247.v7
title: Biodiversity changes in Mesoamerican biopshere reserves

description: Data on biodiversity changes documented in several biosphere reserves in Mesoamerica. The data comes from a study documenting the underlying and proximate drivers of biodiversity changes in Mexican protected areas.The study was submitted to PNAS in 2023.
DOI: 10.34740/kaggle/dsv/6885766
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.5281/zenodo.10072125
title: Remote sensing image classification dataset
DOI: 10.5281/zenodo.10072124
title: Remote sensing image classification dataset
DOI: 10.34740/kaggle/dsv/6885791
title: BBC News
DOI: 10.5281/zenodo.10072104
title: Edge Mode Percolation and Equilibration in the Topological Insulator Cadmium Arsenide
DOI: 10.5281/zenodo.10072103
title: Edge Mode Percolation and Equilibration in the Topological Insulator Cadmium Arsenide
DOI: 10.34740/kaggle/dsv/6886031
title: WallStreetBets 2022
DOI: 10.15121/2006871
title: Assessing Low-Temperature Geothermal Play Types: Relevant Data and Play Fairway Analysis Methods

description: This data catalog contains information on low temperature geothermal play types. The U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO) supports the Geothermal Heating and Cooling Geospatial Datasets and Analysis project, conducted by the National Renewable Energy Laboratory (NREL). This project is part of a broader effort to demonstrate the multifaceted value of integrating geothermal power and geothermal heating and cooling technologies into national decarbonization strategies and community energy plans. There is a need to establish baseline low-temperature geothermal resource data sets and evaluate methods for deploying these technologies. This project aims to reduce exploration risk of low temperature geothermal systems by collecting baseline datasets that can be used for Play Fairway Analysis methodologies. This data catalog contains links to publicly available datasets from different sources that can be relevant for the low temperature geothermal systems. This submission contains data catalogs for Alaska, Hawaii, and the Conterminous United States, as well as a technical report on the methods used to classify and asses the geothermal play types.
DOI: 10.25582/data.2020-09.2046554/2172254
title: Data for EMSL Project 51301 from September 2020
DOI: 10.25582/data.2020-08.2043547/2172255
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2033292/2172261
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2036392/2172262
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2021-04.2195812/2172265
title: Data for EMSL Project 51301 from April 2021
DOI: 10.25582/data.2020-08.2033350/2172267
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-12.2107935/2172268
title: Data for EMSL Project 51301 from December 2020
DOI: 10.25582/data.2020-11.2091122/2172272
title: Data for EMSL Project 51301 from November 2020
DOI: 10.25582/data.2020-08.2036403/2172273
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-02.1948938/2172274
title: Data for EMSL Project 51301 from February 2020
DOI: 10.25582/data.2020-11.2091059/2172275
title: Data for EMSL Project 51301 from November 2020
DOI: 10.25582/data.2020-08.2032975/2172276
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2021-03.2187437/2172282
title: Data for EMSL Project 51301 from March 2021
DOI: 10.25582/data.2020-08.2033338/2172283
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2033290/2172284
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2033132/2172287
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2044971/2172288
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-02.1949022/2172290
title: Data for EMSL Project 51301 from February 2020
DOI: 10.25582/data.2020-09.2046544/2172291
title: Data for EMSL Project 51301 from September 2020
DOI: 10.25582/data.2020-08.2032893/2172299
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2043705/2172303
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-09.2046604/2172315
title: Data for EMSL Project 51301 from September 2020
DOI: 10.25582/data.2020-08.2045090/2172319
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-11.2091148/2172325
title: Data for EMSL Project 51301 from November 2020
DOI: 10.25582/data.2020-08.2036454/2172334
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2033384/2172339
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-09.2046614/2172352
title: Data for EMSL Project 51301 from September 2020
DOI: 10.25582/data.2020-12.2107631/2172355
title: Data for EMSL Project 51301 from December 2020
DOI: 10.25582/data.2020-08.2033438/2172358
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2032848/2172359
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2036413/2172360
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-02.1949002/2172364
title: Data for EMSL Project 51301 from February 2020
DOI: 10.25582/data.2020-08.2033306/2172365
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2032919/2172368
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-11.2091070/2172369
title: Data for EMSL Project 51301 from November 2020
DOI: 10.25582/data.2020-11.2091076/2172371
title: Data for EMSL Project 51301 from November 2020
DOI: 10.25582/data.2020-08.2043966/2172388
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2033436/2172389
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2033321/2172401
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-02.1948498/2172404
title: Data for EMSL Project 51301 from February 2020
DOI: 10.25582/data.2020-12.2107618/2172406
title: Data for EMSL Project 51301 from December 2020
DOI: 10.25582/data.2020-08.2036460/2172418
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2032808/2172419
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2033219/2172421
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2033278/2172431
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2044585/2172432
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2021-03.2187467/2172435
title: Data for EMSL Project 51301 from March 2021
DOI: 10.25582/data.2020-08.2033313/2172436
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2043685/2172437
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-11.2091022/2172438
title: Data for EMSL Project 51301 from November 2020
DOI: 10.25582/data.2020-08.2043595/2172439
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2032840/2172441
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-11.2092458/2172447
title: Data for EMSL Project 51301 from November 2020
DOI: 10.25582/data.2020-08.2033335/2172449
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2032816/2172450
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-09.2046613/2172452
title: Data for EMSL Project 51301 from September 2020
DOI: 10.25582/data.2020-08.2045013/2172453
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2033460/2172456
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2032844/2172459
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2044764/2172467
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2036455/2172472
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2043506/2172475
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-09.2046555/2172477
title: Data for EMSL Project 51301 from September 2020
DOI: 10.25582/data.2020-12.2107903/2172482
title: Data for EMSL Project 51301 from December 2020
DOI: 10.25582/data.2020-08.2033029/2172483
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2033408/2172484
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2045024/2172485
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2032950/2172486
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2045075/2172500
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-08.2043599/2172504
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-12.2107701/2172507
title: Data for EMSL Project 51301 from December 2020
DOI: 10.25582/data.2020-02.1948859/2172509
title: Data for EMSL Project 51301 from February 2020
DOI: 10.25582/data.2020-08.2033433/2172511
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-11.2090996/2172516
title: Data for EMSL Project 51301 from November 2020
DOI: 10.25582/data.2020-08.2036395/2172529
title: Data for EMSL Project 51301 from August 2020
DOI: 10.25582/data.2020-01.1916993/2172542
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1920491/2172547
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1916729/2172548
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919042/2172553
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919062/2172567
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1932961/2172576
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919217/2172579
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919220/2172602
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919151/2172608
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919188/2172619
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919192/2172621
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919224/2172622
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919193/2172624
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919236/2172646
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1918832/2172670
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1920576/2172677
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919185/2172678
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1918941/2172679
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1932922/2172681
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919201/2172682
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919207/2172685
title: Data for EMSL Project 51287 from January 2020
DOI: 10.25582/data.2020-01.1919252/2172690
title: Data for EMSL Project 51287 from January 2020