cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18716/nmrshiftdb2/74292
title: NMR data for methyl ethanesulfonate
DOI: 10.18716/nmrshiftdb2/74293
title: NMR data for InChI=1S/C5H8O2S/c1-5-2-3-7-8(6)4-5/h2H,3-4H2,1H3
DOI: 10.18716/nmrshiftdb2/74294
title: NMR data for InChI=1S/C23H30O7S/c1-6-22(4,25)20(14-29-18-9-7-17(28-5)8-10-18)30-31(26,27)15-23-12-11-16(13-19(23)24)21(23,2)3/h1,7-10,16,20,25H,11-15H2,2-5H3
DOI: 10.18716/nmrshiftdb2/74296
title: NMR data for N-(2-fluoroethyl)-4-nitrobenzamide
DOI: 10.18716/nmrshiftdb2/74297
title: NMR data for InChI=1S/C9H18FNO2/c1-9(2,3)13-8(12)7(11)5-4-6-10/h7H,4-6,11H2,1-3H3
DOI: 10.18716/nmrshiftdb2/74298
title: NMR data for InChI=1S/C5H10FNO2/c6-3-1-2-4(7)5(8)9/h4H,1-3,7H2,(H,8,9)
DOI: 10.18716/nmrshiftdb2/74299
title: NMR data for 2-fluoro-N,N-dimethylacetamide
DOI: 10.3886/icpsr38306.v1
title: Selected State Substantive and Preemptive Laws 2009-2018, United States
title: Version 1

description: The Selected State Preemptive Laws Dataset (SSPLD) was developed to study trends in preemption laws across states from 2009-2018. Researchers selected four domains where states and localities had enacted substantive and preemptive laws: (1) Paid sick days, (2) food, (3) tobacco, and (4) firearms. For three of these domains (sick days, food, and tobacco), state substantive laws are also included in this dataset for each of the policy topics included in the domain.
description: The Selected State Preemptive Laws Dataset (SSPLD) was developed to study trends in preemption laws across states 2009-2018.
description: Researchers sourced many of the statutes analyzed from other public use data sources: Grassroots Change/Preemption Watch Giffords Law Center to Prevent Gun Violence A Better Balance The National Conference of State Legislatures. Paid sick leave preemption statutes were identified from the Economic Policy Institute, American Lung Association, Tobacco Free Kids, CDC STATE online database, (all of which display their analysis of laws to date) and conducted original legal research using Lexis+. For all the sourced statutes, researchers collected all other statutes within the same topical chapter, title, or section, as arranged by the state's legislature, from Lexis+ for the years 2009-2018. Preemption laws are coded first for the presence of the law, then (in a skip pattern) for the type of preemption. While the researchers made every effort to be comprehensive in their retrieval and coding of these laws, some may be missing.
description: Datasets: DS1: Selected State Substantive and Preemptive Laws 2009-2018, United States
description: State laws in the 50 United States from 2009-2018. Smallest Geographic Unit: State
description: record abstractsThe original data file included 5 Excel worksheets of notes pertaining to laws and statues related to topics in the data file. These notes are available as accompanying documentation.Users should note that Diana Silver PhD MPH, Kelley Akiya MPA, and Jose Pagan PhD served as Co-Principal Investigators.For gun laws, state substantive laws can be merged from RAND's dataset: RAND State Firearm Law Database. Version 2.0, October 10, 2019.
DOI: 10.18716/nmrshiftdb2/74300
title: NMR data for InChI=1S/C17H19FN2O4/c18-11-17(12-23-10-13-4-2-1-3-5-13)8-6-15(24-17)20-9-7-14(21)19-16(20)22/h1-5,7,9,15H,6,8,10-12H2,(H,19,21,22)
DOI: 10.18716/nmrshiftdb2/74922
title: NMR data for 1,2,3,4-tetrahydrophenanthrene
DOI: 10.18716/nmrshiftdb2/74924
title: NMR data for trichloromethylbenzene
DOI: 10.18716/nmrshiftdb2/74936
title: NMR data for methyl 3-methoxypropanoate
DOI: 10.18716/nmrshiftdb2/74939
title: NMR data for methyl 2-benzyl-5-methoxy-1,3-oxazole-4-carboxylate
DOI: 10.18716/nmrshiftdb2/74945
title: NMR data for 5-methoxy-2-(3-phenylpropylamino)-1,3-oxazole-4-carbonitrile
DOI: 10.18716/nmrshiftdb2/74951
title: NMR data for InChI=1S/C13H19NO/c1-9-10(2)15-12(14-9)7-11-5-6-13(3,4)8-11/h7H,5-6,8H2,1-4H3/b11-7-
DOI: 10.18716/nmrshiftdb2/74952
title: NMR data for 5-ethoxy-2-(4-phenylsulfanylbutyl)-1,3-oxazole
DOI: 10.18716/nmrshiftdb2/74955
title: NMR data for 5-amino-3-butanoyl-1,3,4-thiadiazol-2-one
DOI: 10.18716/nmrshiftdb2/74958
title: NMR data for 5-amino-3-benzoyl-1,3,4-thiadiazol-2-one
DOI: 10.18716/nmrshiftdb2/74994
title: NMR data for InChI=1S/C14H11Br/c1-9-5-4-8-11-10-6-2-3-7-12(10)14(15)13(9)11/h2-8,14H,1H3
DOI: 10.18716/nmrshiftdb2/74995
title: NMR data for InChI=1S/C7H12O/c1-6-4-2-3-5-7(6)8/h4,7-8H,2-3,5H2,1H3
DOI: 10.18716/nmrshiftdb2/75372
title: NMR data for InChI=1S/C10H6N2OS/c1-2-8-10(12-4-1)13-7-3-5-11-6-9(7)14-8/h1-6H
DOI: 10.18716/nmrshiftdb2/75373
title: NMR data for 4H-1,4-thiazine 1,1-dioxide
DOI: 10.18716/nmrshiftdb2/75374
title: NMR data for 5-nitro-1H-pyridin-2-one
DOI: 10.18716/nmrshiftdb2/74925
title: NMR data for acridine
DOI: 10.18716/nmrshiftdb2/74993
title: NMR data for 9-bromo-9H-fluorene
DOI: 10.18716/nmrshiftdb2/74941
title: NMR data for N,2,4-trimethyl-N-phenyl-1,3-oxazol-5-amine
DOI: 10.18716/nmrshiftdb2/74978
title: NMR data for 5-(4-nitrophenyl)-3-prop-2-ynyl-1,3,4-thiadiazol-2-one
DOI: 10.18716/nmrshiftdb2/75368
title: NMR data for 4,5-dihydrobenzo[e][1,3]benzodithiole-2-selone
DOI: 10.18716/nmrshiftdb2/74946
title: NMR data for 2-(benzylamino)-5-methoxy-1,3-oxazole-4-carbonitrile
DOI: 10.18716/nmrshiftdb2/74949
title: NMR data for methyl 5-methoxy-2-propyl-1,3-oxazole-4-carboxylate
DOI: 10.18716/nmrshiftdb2/74986
title: NMR data for 1-cyano-N-phenylmethanethioamide
DOI: 10.18716/nmrshiftdb2/75370
title: NMR data for InChI=1S/C10H6N2OS/c1-3-7-9(11-5-1)14-10-8(13-7)4-2-6-12-10/h1-6H
DOI: 10.18716/nmrshiftdb2/74923
title: NMR data for 1-(3-hydroxy-8,8-dimethyl-6,7-dihydro-5H-phenanthren-2-yl)ethanone
DOI: 10.18716/nmrshiftdb2/74982
title: NMR data for 2-(2-chlorophenyl)acetonitrile
DOI: 10.18716/nmrshiftdb2/74953
title: NMR data for methyl 5-methoxy-2-prop-2-enyl-1,3-oxazole-4-carboxylate
DOI: 10.18716/nmrshiftdb2/74954
title: NMR data for 3-acetyl-5-amino-1,3,4-thiadiazol-2-one
DOI: 10.18716/nmrshiftdb2/74942
title: NMR data for ethyl 5-methyl-2-methylsulfanyl-1,3-oxazole-4-carboxylate
DOI: 10.18716/nmrshiftdb2/75375
title: NMR data for 1-oxidopyridin-1-ium-3-ol
DOI: 10.18716/nmrshiftdb2/75376
title: NMR data for 1-hydroxypyrazole
DOI: 10.18716/nmrshiftdb2/75377
title: NMR data for 1-oxidopyridin-1-ium-4-carbonitrile
DOI: 10.18716/nmrshiftdb2/75378
title: NMR data for 1-oxidopyridin-1-ium-4-carbaldehyde
DOI: 10.18716/nmrshiftdb2/75379
title: NMR data for piperazine-1,4-diamine
DOI: 10.18716/nmrshiftdb2/75380
title: NMR data for 4,5-dichloro-1H-pyridazin-6-one
DOI: 10.18716/nmrshiftdb2/75381
title: NMR data for (4S,6R)-6-[(4-bromo-1H-pyrrole-2-carbonyl)oxymethyl]-6-methyl-4,5-dihydro-1H-pyrimidine-4-carboxylic acid
DOI: 10.18716/nmrshiftdb2/75383
title: NMR data for N,N-dimethyl-1H-indole-6-sulfonamide
DOI: 10.18716/nmrshiftdb2/75408
title: NMR data for methyl 2-(N-(2-methoxy-2-oxoethyl)anilino)acetate
DOI: 10.18716/nmrshiftdb2/75382
title: NMR data for (4S,5S,6S)-6-[(4-bromo-1H-pyrrole-2-carbonyl)oxymethyl]-5-hydroxy-6-methyl-4,5-dihydro-1H-pyrimidine-4-carboxylic acid
DOI: 10.18716/nmrshiftdb2/75752
title: NMR data for InChI=1S/C16H20N2O5/c1-22-15(20)13-8-5-9-18(13)14(19)10-17-16(21)23-11-12-6-3-2-4-7-12/h2-4,6-7,13H,5,8-11H2,1H3,(H,17,21)
DOI: 10.18716/nmrshiftdb2/75756
title: NMR data for InChI=1S/C6H8N2O5S/c1-5-3-2-4-7(8(9)10)6(5)14(11,12)13/h2-4,6H,1H3,(H,11,12,13)
DOI: 10.18716/nmrshiftdb2/75758
title: NMR data for InChI=1S/C7H12N2O8S2/c1-6(9(10)11)3-4-7(2,19(15,16)17)8-5(6)18(12,13)14/h3-5,8H,1-2H3,(H,12,13,14)(H,15,16,17)
DOI: 10.18716/nmrshiftdb2/75760
title: NMR data for [(1R,2S,5R)-5-methyl-2-propan-2-ylcyclohexyl] N-[[(2S)-5-oxopyrrolidin-2-yl]methyl]carbamate
DOI: 10.18716/nmrshiftdb2/75762
title: NMR data for InChI=1S/C7H13NO2/c1-10-7(9)5-6-3-2-4-8-6/h6,8H,2-5H2,1H3
DOI: 10.18716/nmrshiftdb2/75765
title: NMR data for InChI=1S/C9H17NO2/c1-9(2,8(11)12-3)7-5-4-6-10-7/h7,10H,4-6H2,1-3H3
DOI: 10.18716/nmrshiftdb2/75766
title: NMR data for (2R,4R)-1-benzyl-4-chloro-2-methylpyrrolidine
DOI: 10.15468/32gs5d
title: Cerambycidae collection of Karaganda Buketov University Museum of Nature

description: Entomological Cerambycidae collection of the KarU Nature Museum were supplemented for 50 years by materials of students summer field practices. The collection work began under the guidance of professor N.P. Shlykov, and later professor N.G. Skopin and professor B.N. Mukhachev. Some of the species were obtained as a result of the exchange of collection samples with other entomologists. At the present here are 80 species of longhorn beetles in collection , among which there are pest species, species of aesthetic significance, endemics of Central Kazakhstan and rare species from other regions of the planet.
DOI: 10.5281/zenodo.7846474
title: Data accompanying "Diurnal variability of the upper ocean simulated by a climate model"

description: Data used for creating figures in the draft article "Diurnal variability of the upper ocean simulated by a climate model". This includes: Multi-year, monthly mean diurnal cycle metrics at all model grid points. Monthly mean diurnal cycle data for individual years at selected locations. Code used to create these data files, and to create the plots, is in a Github repository (https://github.com/JackReevesEyre/cfs-analysis-gaea/). The repository is also archived on Zenodo (https://doi.org/10.5281/zenodo.7846095).
DOI: 10.57841/casdc.0000518
title: 磁性离子束及其电流驱动动力学--原始数据及数据处理文件
DOI: 10.57841/casdc.0000519
title: 强耦合kagome超导体中的Roton对密度波--原始数据及数据处理文件
DOI: 10.57841/casdc.0000520
title: 手性磁体中斯格明子的电操作--原始数据及数据处理文件
DOI: 10.57841/casdc.0000521
title: 拓扑半金属TaAs2和NbAs2的尖端诱导超导电性--原始数据及数据处理文件
DOI: 10.57841/casdc.0000522
title: 两种不同的电荷密度波阶数和高压超导现象--原始数据及数据处理文件
DOI: 10.57841/casdc.0000523
title: Mo-P化合物在压力下和双weyl半金属Hex-MoP2中的超导性--原始数据及数据处理文件
DOI: 10.57841/casdc.0000524
title: 温度控制二元表面活性剂体系对近锯齿形单手性碳纳米管的亚毫克级分离--原始数据及数据处理文件
DOI: 10.6084/m9.figshare.22589587
title: tmQM data

description: tmQM data from https://github.com/bbskjelstad/tmqm <br> license: CC BY-NC 4.0 Creative Commons Attribution-NonCommercial 4.0 International License
DOI: 10.6084/m9.figshare.22589587.v3
title: tmQM data

description: tmQM data from https://github.com/bbskjelstad/tmqm <br> license: CC BY-NC 4.0 Creative Commons Attribution-NonCommercial 4.0 International License
DOI: 10.6084/m9.figshare.22589587.v2
title: tmQM data

description: tmQM data from https://github.com/bbskjelstad/tmqm <br> license: CC BY-NC 4.0 Creative Commons Attribution-NonCommercial 4.0 International License
DOI: 10.13012/b2idb-3000894_v1
title: Assembly of wood-inhabiting archaeal, bacterial and fungal communities along a salinity gradient: common taxa are broadly distributed but locally abundant in preferred habitats

description: Supplemental data sets for the Manuscript entitled " Assembly of wood-inhabiting archaeal, bacterial and fungal communities along a salinity gradient: common taxa are broadly distributed but locally abundant in preferred habitats"
DOI: 10.5281/zenodo.7834061
title: Raw data for the article Donor-Acceptor Aminocyclobutane Monoesters: Synthesis and Silylium-Catalyzed (4+2) Annulation with Indoles

description: Raw computational, NMR, IR and MS data for the article "Donor-Acceptor Aminocyclobutane Monoesters: Synthesis and Silylium-Catalyzed (4+2) Annulation with Indoles " published in Angew. Chem., Int. Ed., DOI: https://doi.org/10.1002/anie.202302420 The number of the folders either correspond to compounds numbers in the article or the name of the folder is self-describing. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.
DOI: 10.5281/zenodo.7834060
title: Raw data for the article Donor-Acceptor Aminocyclobutane Monoesters: Synthesis and Silylium-Catalyzed (4+2) Annulation with Indoles

description: Raw computational, NMR, IR and MS data for the article "Donor-Acceptor Aminocyclobutane Monoesters: Synthesis and Silylium-Catalyzed (4+2) Annulation with Indoles " published in Angew. Chem., Int. Ed., DOI: https://doi.org/10.1002/anie.202302420 The number of the folders either correspond to compounds numbers in the article or the name of the folder is self-describing. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.
DOI: 10.26050/wdcc/ar6.ipcc-ddc_ar6_sup_gsatpr
title: IPCC AR6 WGI assessed GSAT Projections

description: Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: These data sets contain the assessed Global Surface Air Temperature (GSAT) projections and all input data and instructions necessary to reproduce the assessed GSAT projections in the Intergovernmental Panel on Climate Change Sixth Assessment Report (Figure 4.11, IPCC AR6 WGI). The constrained CMIP6 projections are based on the methods from three publications calculating the global mean near surface air temperature relative to the average over the period 1995–2014. They are described in box 4.1. The Effective Radiative Forcing (ERF) time series are reproduced from IPCC WGI chapter 7 and included to facilitate reproduction of the analysis. They are uncoupled to any GSAT change. ERF quantifies the energy gained or lost by the earth system following an imposed perturbation (for instance in green house gases, aerosols or solar irradiance). As such it is a fundamental driver of changes in the earth’s TOA energy budget. ERF is determined by the change in the net downward radiative flux at the top of atmosphere (box 7.1) after the system has adjusted to the perturbation but excluding the radiative response to changes in surface temperature (Figure 7.3, IPCC AR6 WGI). For a detailed description of the ERF time series, please refer to chapter 7 (https://github.com/IPCC-WG1/Chapter-7). Disclaimer: The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law. Required Acknowledgements and Citation: In order to document the impact of assessed Global Surface Air Temperature, users of the data are obligated to cite chapter 4 of WGI contribution to the IPCC AR6.
DOI: 10.26050/wdcc/ar6.ipcc-ddc_ar6_sup_dist
title: IPCC AR6 WGI Relative Sea Level Projection Distributions

description: Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projection distributions for all the workflows described in AR6 WGI 9.6.3.2, as well as distributions for the components contributing to relative sea level change. These data may be of use for users who want to substitute their own estimates. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.
DOI: 10.26050/wdcc/ar6.ipcc-ddc_ar6_sup_distbc
title: IPCC AR6 WGI Relative Sea Level Projection Distributions without Background Component

description: Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.
DOI: 10.26050/wdcc/ar6.ipcc-ddc_ar6_sup_pbox
title: IPCC AR6 WGI Relative Sea Level Projection P-Boxes

description: Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements of the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections for all of the p-boxes described in AR6 WGI 9.6.3 (see sister entries 'IPCC-DDC_AR6_Sup_PBox_PP' and 'IPCC-DDC_AR6_Sup_PBox_PPr'), as well as a variant excluding the AR6 estimates of background sea level change (see sister entries 'IPCC-DDC_AR6_Sup_PBox_woBC' and 'IPCC-DDC_AR6_Sup_PBox_woBCr'). Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.
DOI: 10.26050/wdcc/ar6.ipcc-ddc_ar6_sup_slpr_supp
title: IPCC AR6 WGI Sea Level Projections Chapter 7 Support

description: Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: If there is a need to regenerate the sea level rise projections from primary inputs, outputs from the chapter 7 two-layer model emulator have to use to drive all the surface temperature-sensitive parts of the sea level projections and to project thermal expansion (see Github repository https://github.com/chrisroadmap/ar6). The specific notebook that generates this data is https://github.com/chrisroadmap/ar6/blob/main/notebooks/245_chapter9_projections_SSPs.ipynb Used outputs for https://www.wdc-climate.de/ui/entry?acronym=IPCC-DDC_AR6_Sup_SLPr are available via https://www.wdc-climate.de/ui/entry?acronym=IPCC-DDC_AR6_Sup_SLPr_Supp_td
DOI: 10.25345/c5xw4860c
title: MassIVE MSV000091745 - Structural basis for unfolded protein recognition by the ER stress-sensor IRE1alpha
title: ERN1/IRE1alpha is an endoplasmic reticulum sensor that recognizes misfolded proteins to activate the unfolded protein response. We used cholera toxin (CTx), which activates IRE1alpha in cells, to understand how this occurs. In vitro, the A1 subunit of CTx (CTxA1) bound the IRE1alpha lumenal domain (IRE1alphaLD). Global unfolding was not required. Instead, IRE1alphaLD recognized a 7-residue motif within a metastable region of CTxA1 that was also found in other microbial and host proteins involved in IRE1alpha activation. Binding mapped to a pocket on IRE1alphaLD normally occupied by a segment of the C-terminal flexible loop implicated in IRE1alpha regulation. Mutation of the recognition motif blocked CTx-induced IRE1alpha activation in live cells. These findings describe a mechanism for substrate recognition by IRE1alpha that induces the unfolded protein response.
DOI: 10.57967/hf/0550
title: CIS6930_DAAGR_Empathetic_Dialogues
DOI: 10.6084/m9.figshare.22659418
title: Ordonez-Flores_etal_review_database

description: We conducted a literature review to understand the state of the art of knowledge of the advertisement calls of the anurans distributed in Mexican territory. We also did a bibliometric analysis to determine the publication trends for this topic in Mexico. For this, we followed the method presented by Guerra et al. (2018) with some modifications. For the literature review, we did a Boolean search for descriptions of the advertisement calls of Mexican anurans on some of the main scientific literature databases: Google Scholar (www.scholar.google.com), Scopus (https://www.scopus.com/), Web of Science (https://www.webofknowledge.com), and Scielo (https://scielo.org/es/).
DOI: 10.25345/c5t43jc7d
title: MassIVE MSV000091746 - DEIMoS Example LC-IMS-MS/MS Files
title: Example LC-IMS-MS/MS data files for use with the data extraction for multidimensional spectrometry (DEIMoS) user guide.
DOI: 10.17909/rnp9-gj41
title: HST Jupiter Observations acquired on July 28th, 2022 as part of program 16913 (PI: M. H. Wong)
DOI: 10.6084/m9.figshare.22659502
title: CGE RNA and DNA methylation Luo 2017, RDS dgCMatrix

description: RDS files contain "dgCMatrix" class objects for sparse raw counts of the downsampled datasets: GSE97179 (Luo et al, Science, 2017) <br> RDS files contain named "factor" class objects for cluster assignment of each dataset, obtained from the original study. <br> These datasets are prepared primarily for the use of presenting LIGER integration method.
DOI: 10.6084/m9.figshare.22659502.v1
title: CGE RNA and DNA methylation Luo 2017, RDS dgCMatrix

description: RDS files contain "dgCMatrix" class objects for sparse raw counts of the downsampled datasets: GSE97179 (Luo et al, Science, 2017) <br> RDS files contain named "factor" class objects for cluster assignment of each dataset, obtained from the original study. <br> These datasets are prepared primarily for the use of presenting LIGER integration method.
DOI: 10.5519/qd.6fu75gf4
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (50 records)
DOI: 10.5281/zenodo.7846616
title: IODP Expedition 385 Per-fluorocarbon tracers (PFT)

description: Perfluorocarbon tracers (PFT) can be used to quantify the amount of core contamination due to drilling fluid. PFT is continuously pumped into the stream of drilling fluid to maintain a consistent concentration. Core subsamples are then measured in triplicate using µECD gas chromatography. Measured levels of PFT of the core outer surface, the outer core surface after undergoing a decontamination procedure, and the inner core are then compared to determine the degree of contamination.
DOI: 10.5281/zenodo.7846615
title: IODP Expedition 385 Per-fluorocarbon tracers (PFT)

description: Perfluorocarbon tracers (PFT) can be used to quantify the amount of core contamination due to drilling fluid. PFT is continuously pumped into the stream of drilling fluid to maintain a consistent concentration. Core subsamples are then measured in triplicate using µECD gas chromatography. Measured levels of PFT of the core outer surface, the outer core surface after undergoing a decontamination procedure, and the inner core are then compared to determine the degree of contamination.