cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3711903
title: Dar Es Salaam Very-High-Resolution Land Cover Map

description: This is a very-high-resolution land cover map of Dar es Salaam derived from satellite imagery (Pleiades, 0.5m resolution). The majority of the area is classified from a 2016 (July) image while a small part of it from two images collected in January and March 2018, respectively. The pixel values related to the following legend: 5=tree<br> 8=shadow<br> 3=artificial ground surface<br> 4=low vegetation<br> 2=water<br> 7=bare ground<br> 1=building<br> 113=high elevated buildings<br> 112=medium elevated buildings<br> 111=low elevated buildings The Out of Bag error of the product is 6,38% with the following class errors: Building = 0.035826 Water = 0.049934 Artificial Ground Surface = 0.077108 Low Vegetation = 0.108709 Tall Vegetation = 0.062278 Bare Ground = 0.13803 Shadow = 0.019872 References: [1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” <em>Remote Sensing</em> 9 (4): 358. https://doi.org/10.3390/rs9040358. [2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. https://doi.org/10.1117/12.2278422. [3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>. This dataset was produced in the frame of REACT (http://react.ulb.be), funded by the Belgian Federal Science Policy Office (BELSPO).
DOI: 10.5281/zenodo.5736535
title: Supporting data for manuscript: Beyond Bulk

description: We used soil density fraction data from The International Soil Radiocarbon Database (ISRaD v. 1.1.2 Lawrence et al., 2020; www.soilradiocarbon.org). ISRaD is an online repository for environmental radiocarbon data with a specific emphasis on soils and soil fractions. We utilized a subset of ISRaD data comprising measurements of radiocarbon (persistence), organic C concentration (abundance), or the proportion of organic C in the mineral-associated fraction (distribution) made on soil density fractions for the current analysis. Radiocarbon data are reported in units of Δ<sup>14</sup>C (‰) normalized to account for the year of sampling (Shi et al., 2020) (see below). In studies that employed sequential density separation (isolation of multiple free light, occluded light, and heavy fractions for the same sample), the multiple fractions were combined by taking a mass-weighted average for C abundance and C-weighted average for Δ<sup>14</sup>C values. C distribution among density fractions was normalized to sum to 100%. Overall, our meta-analysis included data from 52 studies. In addition to C measurements, ISRaD compiles ancillary data regarding site and sample characteristics that were either provided directly in the associated published works or provided as supplementary information from manuscript authors. When variables of interest were not available directly through ISRaD, these variables were populated through utilization of geolocated databases (see supplemental materials in associated published manuscript).
description: Contains substantial portions of data from the International Soil Radiocarbon Database.
DOI: 10.5281/zenodo.8323292
title: A global estimate of monthly vegetation and soil fractions from spatio-temporally adaptive spectral mixture analysis during 2001-2022

description: This dataset is a global estimate of monthly vegetation and soil fractions using spatio-temporally adaptive spectral mixture analysis from 2001-2011. The file is a compressed month-by-month GeoTIFF data for each year, according to the grid of longitude 60° and Latitude 50°. Since the dataset for each year includes 216 files, named as “SMA_year_(month-1)_gridid.tif”, like “SMA_2001_0_0.tif”. The dataset from 2012-2022 can be found in another Zenodo (10.5281/zenodo.8331843) The dataset can be also exported from GEE by our code or achieved by request from authors. Here is a schematic of global girds and id <strong>65N</strong> 2 5 8 11 14 17 <strong>15N</strong> 1 4 7 10 13 16 <strong>35S</strong> 0 3 6 9 12 15 <strong>150W</strong> <strong>90W</strong> <strong>30W</strong> <strong>30E</strong> <strong>90E</strong> <strong>150E</strong>
DOI: 10.5281/zenodo.8331843
title: A global estimate of monthly vegetation and soil fractions from spatio-temporally adaptive spectral mixture analysis during 2012-2022

description: This dataset is another global estimate of monthly vegetation and soil fractions using spatio-temporally adaptive spectral mixture analysis from 2012-2022. The file is a compressed month-by-month GeoTIFF data for each year, according to the grid of longitude 60° and Latitude 50°. Since the dataset for each year includes 216 files, named as“SMA_year_(month-1)_gridid.tif”, like“SMA_2012_0_0.tif”. The dataset from 2001-2011 can be found in another Zenodo (10.5281/zenodo.8323292) The dataset can be also exported from GEE by our code or achieved by request from authors. Here is a schematic of global girds and id <strong>65N</strong> 2 5 8 11 14 17 <strong>15N</strong> 1 4 7 10 13 16 <strong>35S</strong> 0 3 6 9 12 15 <strong>150W</strong> <strong>90W</strong> <strong>30W</strong> <strong>30E</strong> <strong>90E</strong> <strong>150E</strong>
DOI: 10.22008/fk2/c24wvn
title: Greenland ice sheet ice and firn temperature reconstruction at 10 m depth, 1950-2022

description: This dataset contains monthly grids of 10 m subsurface temperatures (T10m) for the Greenland ice sheet between 1954 and 2022. These grids have been produced by an Artificial Neural Network (ANN) which take as input ERA5 snowfall and air temperatures and is trained on more than 4500 observations of T10m from multiple sources. For a full description of the observation dataset, ANN training and performance, please see: Vandecrux, B., Fausto, R. S., Box, J., Covi, F., Hock, R., Rennermalm, A., Heilig, A., Abermann, J., van As, D., Bjerre, E., Fettweis, X., Smeets, P.C.J.P., Kuipers Munneke, P., van den Broeke, M., Brils, M., Langen, P.L., Mottram, R., Ahlstrøm, A.: Historical snow and ice temperature compilation documents the recent warming of the Greenland ice sheet, manuscript in development, 2023 The dataset is composed of two netcdf files: - T10m_prediction.nc which contains the ANN's prediction - T10m_uncertainty.nc which contains the ANN's estimated uncertainty Both files have latitude and longitude (WGS84) as coordinate reference system (CRS), a monthly temporal resolution and a spatial resolution of 0.1°x0.1°. The uncertainty is calculated from spatial cross-validation: We devide the ice sheet into 10 geographic regions which each contain between 95 and 1280 observations, meaning from 2% to 28% of the observation dataset. We train 10 ANN models, each of them ignoring one of these regions and therefore not learning from the observations therein. For a given location and month, the standard deviation between these 10 cross-validation models is taken as the ANN uncertainty. The best model, which uses all available observations, is used to produce the T10m_prediction.nc.
DOI: 10.25573/data.10068065.v1
title: SST_San Blas

description: Sea Surface temperature monitoring at or near the former STRI field station in the San Blas Islands.<br><br>Locations: <br>STRI San Blas Station, 9.552572, -78.952256<br>Gulf of San Blas, 9.550428, -78.946669<br>Status: Inactive<br>
DOI: 10.25582/data.2021-09.2339837/2095764
title: Data for EMSL Project 51241 from September 2021
DOI: 10.3886/e116495v1
title: Replication data for: Work of the Past, Work of the Future

description: US cities today are vastly more educated and skill-intensive than they were five decades ago. Yet, urban non-college workers perform substantially less skilled jobs than decades earlier. This deskilling reflects the joint effects of automation and, secondarily, rising international trade, which have eliminated the bulk of non-college production, administrative support, and clerical jobs, yielding a disproportionate polarization of urban labor markets. The unwinding of the urban non-college occupational skill gradient has, I argue, abetted a secular fall in real non-college wages by: (1) shunting non-college workers out of specialized middle-skill occupations into low-wage occupations that require only generic skills; (2) diminishing the set of non-college workers that hold middle-skill jobs in high-wage cities; and (3) attenuating, to a startling degree, the steep urban wage premium for non-college workers that prevailed in earlier decades. Changes in the nature of work—many of which are technological in origin—have been more disruptive and less beneficial for non-college than college workers.
DOI: 10.6084/m9.figshare.22093163
title: Supplementary Material for: Benefit of continuation of low-dose imatinib for gastrointestinal stromal tumors despite adverse events with regular-dose imatinib

description: Tyrosine kinase inhibitors (TKIs) such as imatinib improve the prognosis of patients with gastrointestinal stromal tumors (GISTs). However, treatment options for GISTs are still limited, and the continuation of TKIs is difficult due to adverse events in some cases. The effectiveness of low-dose imatinib is unclear. We report two cases to show effectiveness of low-dose imatinib in patients with adverse events. The first case is a male in his early 60s with a history of intestinal GIST resection was diagnosed with recurrent GIST with peritoneal dissemination. He was started on low-dose imatinib (300 mg) because of a history of subconjunctival hemorrhage after receiving postoperative imatinib. Follow-up contrast-enhanced ultrasonography revealed that the tumors had shrunk in size and number after 2 months of treatment with 300-mg imatinib. He continued this treatment and showed partial response for 8 months. The second case is a female in her late 70s with rectal GIST was treated with imatinib 400 mg. Due to a severe skin lesion, she changed her treatment to sunitinib 2 months after initiation. However, new metastasis in the liver was confirmed after 4 months of administration of sunitinib. She underwent surgical resection of the rectal tumor to reduce the volume. After the surgery, low-dose imatinib (300 mg) with oral steroids was adopted. Follow-up confirmed the absence of recurrence at the rectum and no increase of hepatic tumor size for 18 months. Aggressive treatment with low-dose imatinib instead of discontinuation or alteration of treatment may benefit patients with unresectable and post-operative GISTs with sensible mutation to imatinib.
DOI: 10.17188/1202446
title: Materials Data on Cr2Se3 by Materials Project

description: Cr2Se3 is Corundum-like structured and crystallizes in the trigonal R-3 space group. The structure is three-dimensional. there are three inequivalent Cr3+ sites. In the first Cr3+ site, Cr3+ is bonded to six equivalent Se2- atoms to form a mixture of edge and corner-sharing CrSe6 octahedra. The corner-sharing octahedral tilt angles are 45°. All Cr–Se bond lengths are 2.54 Å. In the second Cr3+ site, Cr3+ is bonded to six equivalent Se2- atoms to form a mixture of face and corner-sharing CrSe6 octahedra. The corner-sharing octahedra tilt angles range from 45–50°. All Cr–Se bond lengths are 2.56 Å. In the third Cr3+ site, Cr3+ is bonded to six equivalent Se2- atoms to form a mixture of face, edge, and corner-sharing CrSe6 octahedra. The corner-sharing octahedral tilt angles are 50°. There are three shorter (2.52 Å) and three longer (2.57 Å) Cr–Se bond lengths. Se2- is bonded in a rectangular see-saw-like geometry to four Cr3+ atoms.
DOI: 10.25582/data.2021-09.2340566/2095765
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340917/2095766
title: Data for EMSL Project 51241 from September 2021
DOI: 10.14272/prmvcfzslsdnrn-uhfffaoysa-n/chmo0000596
title: distortionless enhancement with polarization transfer (DEPT) ()
title: InChI=1S/C14H19NO2S/c1-4-5-6-7-10-13-11-8-9-12-14(13)15(2)18(3,16)17/h8-9,11-12H,4-6H2,1-3H3

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.14272/prmvcfzslsdnrn-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C14H19NO2S/c1-4-5-6-7-10-13-11-8-9-12-14(13)15(2)18(3,16)17/h8-9,11-12H,4-6H2,1-3H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2956 (w), 2931 (w), 2871 (w), 2861 (w), 2232 (vw), 1595 (w), 1486 (s), 1465 (w), 1448 (m), 1428 (w), 1380 (w), 1337 (vs), 1288 (m), 1264 (w), 1235 (w), 1177 (m), 1146 (vs), 1108 (m), 1069 (s), 1037 (m), 1010 (w), 994 (w), 960 (vs), 892 (s), 870 (m), 856 (m), 802 (w), 769 (vs), 748 (vs), 704 (w), 662 (w), 633 (w), 618 (w), 611 (w), 605 (w), 578 (w), 557 (s), 516 (vs), 475 (w), 465 (w), 449 (w), 419 (w), 402 (w), 395 (w), 381 (w) cm–1.
DOI: 10.4225/08/50eb54b6f1042
title: Global Bemisia dataset release version 31 Dec 2012

description: ERRATA: PLEASE USE THE FILES IN THE MORE RECENT VERSION BY CLICKING THE LINK ABOVE. THERE ARE ERRORS IN THE FILES FOR THIS VERSION. SUPPORTING FILES BELOW CONTAINS INFORMATION ABOUT THE CHANGES, see Errata_Readme.txt. The original dataset housed in the CSIRO data portal was accumulated over a seven year period and uploaded in 31 Dec 2012. During this time multiple researchers and students contributed sequences and worked on the dataset. Many were entered into the dataset before going into GenBank and the accession details were added later once the contributor had uploaded the sequences into GenBank. This has meant that some of the sequences do not now reflect what is in GenBank which is source of confusion. We have also become aware that over this period various errors have crept into the dataset. We have therefore decided to rebuild the dataset using the same accessions, but using the data as it now appears in GenBank. The original dataset had 558 sequences. This included one sequence that had not been uploaded into GenBank and 2 duplicates. These have been removed leaving 555 sequences. Of these 11 contained either stop codons or indels that caused frameshifts and these have been removed leaving 544 sequences. You can read the file note to see which sequences were removed. There are now two files. 
1)       FINAL Unaligned 544 sequences dataset 14 May 2017 unedited.fas. This is the original source data as it appears in GenBank. The only change is that the 11 error sequences in the original 555 sequence downloaded from GenBank have been removed.
2)       FINAL Aligned 544 sequences 14 May 2017.fas. The downloaded sequences have been trimmed to 657 b length. There are no other edits.
Mitochondrial cytochrome oxidase subunit one gene, partial cds, sequence data. The dataset was compiled to help researchers wanting to undertake a phylogenetic analysis of the Bemisia tabaci species complex; to assign unknowns to already identified members of the Bemisia tabaci species complex or to identify putatuve new species belonging to the complex.  All sequences are available publicly through GenBank and all accession numbers are included. The dataset contains 558 unique Bemisi tabaci plus outgroup sequences . The sequences are 657 bases in length starting with GAAAATTAGAGGT (using Middle East-Asia Minor 1 DQ174535 as the alignment template) and were aligned using ClustalX (ver. 1.81) according to Thompson et al. (1997). All sequences had no gaps or pseudogenes and  ambiguous bases were &lt;0.8%.
DOI: 10.6084/m9.figshare.23815677
title: Supplementary Material for: Efficacy of Blue LED Phototherapy on Sleep Quality, Behavioral and Psychological Symptoms of Dementia: A Double-blind Randomized Controlled Trial

description: Introduction: People with dementia often experience behavioral and psychological symptoms of dementia (BPSD), which are a major cause of caregiver burden and institutionalization. Therefore, we conducted a double-blind, parallel-group randomized controlled trial (RCT) to examine the efficacy of blue-enriched light therapy for BPSD in institutionalized older adults with dementia. Methods: Participants were enrolled and randomly allocated into blue-enriched light therapy (N = 30) or the conventional light group (N = 30) for 60 minutes in 10 weeks with five sessions per week. The primary outcome was sleep quality measured by actigraphy and Pittsburgh Sleep Quality Index (PSQI). The secondary outcome was overall BPSD severity (Cohen-Mansfield Agitation Inventory (CMAI) and Neuropsychiatric Inventory (NPI-NH). The outcome indicators were assessed at baseline, mid-test, immediate posttest, 1-month, 3-month, and 6-month follow-up. The effects of the blue-enriched light therapy were examined by the generalized estimating equations (GEE) model. Results: Blue-enriched light therapy revealed significant differences in the objective sleep parameters (sleep efficiency: β = 5.81, Waldχ2 = 32.60, CI: 3.82; 7.80; sleep latency: β = -19.82, Waldχ2 = 38.38, CI:-26.09; -13.55), subjective sleep quality (PSQI: β = -2.07, Waldχ2 = 45.94, CI: -2.66; -1.47), and overall BPSD severity (CMAI: β = -0.90, Waldχ2 = 14.38, CI: -1.37; -0.44 ) (NPI-NH: β = -1.67, Waldχ2 = 30.61, CI: -2.26; -1.08) compared to conventional phototherapy immediate posttest, 1-month, 3-month, and 6-month follow-up. Furthermore, the effects for sleep efficiency and sleep latency lasted for up to six months. In the subscale analysis, the differences of the behavioral symptoms changed significantly between the groups in physical/non-aggressive (CI: -1.01; -0.26) and verbal/non-aggressive (CI: -0.97; -0.29). Conclusions: Blue-enriched light therapy is a feasible low-cost intervention that could be integrated as a comprehensive therapy program for BPSD among older adults with dementia.
DOI: 10.6084/m9.figshare.24088002
title: Supplementary Material for: Regulatory qualification of a cross-disease digital measure: benefits and challenges from the perspective of IMI consortium IDEA-FAST

description: Background: Innovative Medicines Initiative (IMI) consortium IDEA-FAST is developing novel digital measures of fatigue, sleep quality and impact of sleep disturbances for neurodegenerative diseases and immune-mediated inflammatory diseases. In 2022, the consortium met with the European Medicines Agency (EMA) to receive advice on its plans for regulatory qualification of the measures. This viewpoint reviews the IDEA-FAST perspective on developing digital measures for multiple diseases and the advice provided by the EMA. Summary: The EMA considered a cross-disease measure an interesting and arguably feasible concept. Developers should account for the need for a strong rationale that the clinical features to be measured are similar across diseases. In addition, they may expect increased complexity of study design, challenges when managing differences within and between disease populations and the need for validation in both heterogeneous and homogeneous populations. Key messages: EMA highlighted the challenges teams may encounter when developing a cross-disease measure though benefits potentially include reduced resources for technology developer and health authority, faster access to innovation across different therapeutic fields and feasibility of cross-disease comparisons. The insights included here can be used by project teams to guide them in the development of cross-disease digital measures intended for regulatory qualification.
DOI: 10.14272/prmvcfzslsdnrn-uhfffaoysa-n.1
title: N-(2-(hex-1-yn-1-yl)phenyl)-N-methylmethanesulfonamide
title: InChI=1S/C14H19NO2S/c1-4-5-6-7-10-13-11-8-9-12-14(13)15(2)18(3,16)17/h8-9,11-12H,4-6H2,1-3H3

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367]. The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]: InChI descriptor[cheminf_000113]: InChI=1S/C14H19NO2S/c1-4-5-6-7-10-13-11-8-9-12-14(13)15(2)18(3,16)17/h8-9,11-12H,4-6H2,1-3H3, and canonical SMILES descriptor[cheminf_000007]: CCCCC#Cc1ccccc1N(S(=O)(=O)C)C, and by the IUPAC name[cheminf_000107]: . The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]: The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-12801 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]: Melting point descriptor[CHEMINF_000256]: Boiling point descriptor[CHEMINF_000257]: Refractive index descriptor[CHEMINF_000253]: The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]: CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR) CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR) CHMO:0000596 | distortionless enhancement with polarization transfer (DEPT) CHMO:0000596 | distortionless enhancement with polarization transfer (DEPT) CHMO:0000470 | mass spectrometry (MS) CHMO:0000630 | infrared absorption spectroscopy (IR) The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: Used ontologies: CHEBI - Chemical Entities of Biological Interest CHEMINF - chemical information ontology (information entities about chemical entities) CHMO - Chemical Methods Ontology OBI - Ontology for Biomedical Investigations
DOI: 10.14272/prmvcfzslsdnrn-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C14H19NO2S/c1-4-5-6-7-10-13-11-8-9-12-14(13)15(2)18(3,16)17/h8-9,11-12H,4-6H2,1-3H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Chloroform-d [7.26 ppm], ppm) δ = 7.45 (dd, J = 1.8 Hz, J = 7.4 Hz, 1H), 7.42–7.40 (m, 1H), 7.33–7.25 (m, 2H), 3.37 (s, 3H), 3.00 (s, 3H), 2.45 (t, J = 7.1 Hz, 2H), 1.62–1.57 (m, 2H), 1.52–1.45 (m, 2H), 0.95 (t, J = 7.3 Hz, 3H).
DOI: 10.25582/data.2021-09.2339751/2095767
title: Data for EMSL Project 51241 from September 2021
DOI: 10.14272/prmvcfzslsdnrn-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C14H19NO2S/c1-4-5-6-7-10-13-11-8-9-12-14(13)15(2)18(3,16)17/h8-9,11-12H,4-6H2,1-3H3

description: dataset for mass spectrometry (MS) EI (m/z, 70 eV, 30 °C): 266 (13) [M+1]+, 265 (69) [M]+, 186 (53), 157 (16), 156 (16), 145 (12), 144 (100), 143 (37), 142 (12), 131 (16), 130 (97), 118 (12), 117 (12), 115 (22), 91 (10), 77 (11), 69 (12), 58 (34), 57 (14), 55 (10). HRMS (C14H19NO2S): Calcd 265.1132, Found 265.1131.
DOI: 10.14272/prmvcfzslsdnrn-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C14H19NO2S/c1-4-5-6-7-10-13-11-8-9-12-14(13)15(2)18(3,16)17/h8-9,11-12H,4-6H2,1-3H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, CDCl3 [77.16 ppm], ppm) δ = 141.8, 134.0 (+, CH), 131.5 (+, CH), 128.9 (+, CH), 128.4 (+, CH), 123.4, 96.4, 77.9, 39.2 (+, CH3), 38.1 (+, CH3), 30.8 (–, CH2), 22.3 (–, CH2), 19.4 (–, CH2), 13.7 (+, CH3).
DOI: 10.14272/prmvcfzslsdnrn-uhfffaoysa-n/chmo0000596.1
title: distortionless enhancement with polarization transfer (DEPT) ()
title: InChI=1S/C14H19NO2S/c1-4-5-6-7-10-13-11-8-9-12-14(13)15(2)18(3,16)17/h8-9,11-12H,4-6H2,1-3H3

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.25582/data.2021-09.2340261/2095768
title: Data for EMSL Project 51241 from September 2021
DOI: 10.25582/data.2021-09.2340761/2095769
title: Data for EMSL Project 51241 from September 2021