cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57957/datadeposit.f346c15b-cf18-47f7-8155-2cebd8ccade6
title: GAMP 2D Phase Transitions

description: A fairly large set of numerical results from a number of computational simulation experiments using compressed sensing with generalised approximate message passing reconstruction
DOI: 10.5281/zenodo.8167441
title: The impact of energy releasing B-vitamin intake on indices of obesity and cardiac function: a cross-sectional study

description: Consent form
DOI: 10.5281/zenodo.8177168
title: Slip model of the 2022 Mw 6.6 Luding earthquake from inversion of GNSS and Sentinel-1A satellites

description: 2022年Mw 6.6泸定地震的滑移模型,来自GNSS和Sentinel-1A卫星的反演“2022年Mw 6.6泸定地震滑移模型,GNSS和Sentinel-1A卫星反演”中使用的GNSS数据
DOI: 10.5281/zenodo.8177167
title: Slip model of the 2022 Mw 6.6 Luding earthquake from inversion of GNSS and Sentinel-1A satellites

description: 2022年Mw 6.6泸定地震的滑移模型,来自GNSS和Sentinel-1A卫星的反演“2022年Mw 6.6泸定地震滑移模型,GNSS和Sentinel-1A卫星反演”中使用的GNSS数据
DOI: 10.5281/zenodo.8167422
title: The impact of energy releasing B-vitamin intake on indices of obesity and cardiac function: a cross-sectional study

description: Study Questionnaire
DOI: 10.5281/zenodo.8159116
title: The impact of energy releasing B-vitamin intake on indices of obesity and cardiac function: a cross-sectional study

description: Supplementary file 1
DOI: 10.34740/kaggle/dsv/6186354
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.8177271
title: Continent-scale inventory routing solutions

description: This dataset of inventory routing solutions is a fruit of our partnership between Renault Group and the CERMICS laboratory at Ecole des Ponts. The related instances (also publicly available) are continent-scale with hundreds of customers, 21 days horizon, and 15 depots on average. Routes can last several days (continuous-time), and 30 types of commodities are involved, leading to bin packing problems when filling trucks. In our paper "Solving a Continent-Scale Inventory Routing Problem at Renault" we introduce a new large neighborhood search to solve those instances. This dataset contains the solutions provided both by our algorithm and by a benchmark we implement, as shown in the computational experiments section of our paper. We hope that sharing them publicly will motivate research on real-world and large-scale inventory routing. Environmental and economical impacts at stake are substantial.
DOI: 10.5281/zenodo.8177270
title: Continent-scale inventory routing solutions

description: This dataset of inventory routing solutions is a fruit of our partnership between Renault Group and the CERMICS laboratory at Ecole des Ponts. The related instances (also publicly available) are continent-scale with hundreds of customers, 21 days horizon, and 15 depots on average. Routes can last several days (continuous-time), and 30 types of commodities are involved, leading to bin packing problems when filling trucks. In our paper "Solving a Continent-Scale Inventory Routing Problem at Renault" we introduce a new large neighborhood search to solve those instances. This dataset contains the solutions provided both by our algorithm and by a benchmark we implement, as shown in the computational experiments section of our paper. We hope that sharing them publicly will motivate research on real-world and large-scale inventory routing. Environmental and economical impacts at stake are substantial.
DOI: 10.25345/c5xw4863t
title: MassIVE MSV000092509 - GC-MS Soils bioremediation ext ERG
title: Isabel Cipriani Data Soils bioremediation, ergosterol extraction
DOI: 10.5281/zenodo.8177127
title: Supporting data for publication: The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences.

description: This repository contains the seismicity catalogues from Cahuilla, Yellowstone and West Bohemia used in the publication Roche et al., 2023 (The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences) and modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016). The catalogues include the hypocentre location, relative time, and magnitude for non-filtered and filtered data. General information on each catalogue, filtering and modifications can be found in the associated publication. Dataset list: Cahuilla Catalogues (modified from Ross et al., 2019): Original data:File name: VR_sup_0021_Cah_All Filtered data: File name: VR_sup_0022_Cah_Filter Bohemia 2008 Catalogues (modified from Haintzl et al., 2016): Original data:File name: VR_sup_0023_Boh_08_All Filtered data: File name: VR_sup_0024_Boh_08_Filter Bohemia 2014 Catalogues (modified from Haintzl et al., 2016): Original data:File name: VR_sup_0025_Boh_14_All Filtered data: File name: VR_sup_0026_Boh_14_Filter Yellowstone Catalogs (modified from Shelly et al., 2013): Original data:File name: VR_sup_0027_Yell_14_All Filtered data: File name: VR_sup_0028_Yell_14_Filter The files are text files tab-delimited, with the following headers: Index: 1 by default Easting(m): hypocenter Easting in meters Northing(m): hypocenter Northing in meters Depth(m): hypocenter depth in meters Mw: magnitude Relative Time(s): date of the origin time in the format If you find these data useful in your research, please cite Roche et al. (2023), as well as the relevant papers Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016).
DOI: 10.21227/w9b3-x241
title: numerical data

description: The focus has been on investors’ hopes for the stock market for a considerable amount of time.Becauseoftheanticipatedhighreturns,thisisthepreferredinvestmentoption.However,dueto the significance of accurate forecasting, such investments are high-risk. In order to analyze stock market forecasts, investors utilize a technical analyst and AI technologies. Predictions and forecasts may be impacted by several obstacles, including analyst bias towards specific firms, investor pro-clivities, the impactof corporate news and economic circumstances, and the reliance on human skills. One LSTM trained specifically on digital data was used to forecast the future share price of the company by analyzing the company’s stock data; its performance was evaluated using the mean squared error metric (MSE), which revealed an accuracy of 98%. The second model is the Support Vector Machine (SVM), which uses a calculation process that gives weight to the number of likes of the tweet, the number of retweets, and the number of followers of the person who tweeted this tweet, in addition to analysis of the text of the tweet, topredictthepositivistornegativistnatureofthetweetbasedonitsweight.Theperformanceofthemodel wasmeasuredbytheaccuracyratio,anditwasfoundthatthetextanalysiswasaccuratebyafactorof99%. Thus,acombinationoftextanddigitaldatawasusedtocreateaninteractivesitewhereuserscanestimate thedailyperfor-manceoftheStocksofselectedfirmsbydraggingTwittertweetsandmarketopeningdata. In the end, our research aids investors in making accurate share price predictions and limiting their losses.
DOI: 10.6084/m9.figshare.23735901
title: Kastovsky_et_al_ 2023_Yellowstone_trichal_cyanobacteria_Diversity_all_ sequences_table.xlsx

description: Table of all sequences used for phylogenetic tree construciton in paper about simple trichal cyanobacteria from Yellowstone National Park.
DOI: 10.6084/m9.figshare.23735901.v1
title: Kastovsky_et_al_ 2023_Yellowstone_trichal_cyanobacteria_Diversity_all_ sequences_table.xlsx

description: Table of all sequences used for phylogenetic tree construciton in paper about simple trichal cyanobacteria from Yellowstone National Park.
DOI: 10.5281/zenodo.8177301
title: UCI and OpenML Data Sets for Ordinal Quantification

description: These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data. With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label. We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files <em>app_val_indices.csv</em>, <em>app_tst_indices.csv</em>, <em>app-oq_val_indices.csv</em>, and <em>app-oq_tst_indices.csv</em> represents one sample. Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed. <strong>Usage</strong> You can extract four CSV files through the provided script <em>extract-oq.jl</em>, which is conveniently wrapped in a <em>Makefile</em>. The <em>Project.toml</em> and <em>Manifest.toml</em> specify the Julia package dependencies, similar to a requirements file in Python. <strong>Preliminaries:</strong> You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments. <strong>Data Extraction:</strong> In your terminal, you can call either <pre><code>make</code></pre> (recommended), or <pre><code>julia --project="." --eval "using Pkg; Pkg.instantiate()" julia --project="." extract-oq.jl</code></pre> <strong>Outcome: </strong>The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class. <strong>Further Reading</strong> Implementation of our experiments: https://github.com/mirkobunse/regularized-oq
DOI: 10.5281/zenodo.8177302
title: UCI and OpenML Data Sets for Ordinal Quantification

description: These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data. With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label. We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files <em>app_val_indices.csv</em>, <em>app_tst_indices.csv</em>, <em>app-oq_val_indices.csv</em>, and <em>app-oq_tst_indices.csv</em> represents one sample. Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed. <strong>Usage</strong> You can extract four CSV files through the provided script <em>extract-oq.jl</em>, which is conveniently wrapped in a <em>Makefile</em>. The <em>Project.toml</em> and <em>Manifest.toml</em> specify the Julia package dependencies, similar to a requirements file in Python. <strong>Preliminaries:</strong> You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments. <strong>Data Extraction:</strong> In your terminal, you can call either <pre><code>make</code></pre> (recommended), or <pre><code>julia --project="." --eval "using Pkg; Pkg.instantiate()" julia --project="." extract-oq.jl</code></pre> <strong>Outcome: </strong>The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class. <strong>Further Reading</strong> Implementation of our experiments: https://github.com/mirkobunse/regularized-oq
DOI: 10.34740/kaggle/dsv/6186547
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.8177267
title: Steady VLM correction exemplar

description: This dataset includes an example MATLAB script that implements corrections to steady Vortex Lattice Method (VLM) results. Corrections include: Viscous lift curve slope. Lifting surface viscous drag (including lift-dependent viscous drag). Fuselage profile drag. Fuselage contribution to pitching moment. Tailplane contribution to pitching moment. The example includes experimental wind tunnel data from a model of an English Electric Canberra, along with software models of this aircraft in three popular steady VLM solvers: AVL, XFLR5 and OpenVSP/VSPAERO.
DOI: 10.5281/zenodo.8177266
title: Steady VLM correction exemplar

description: This dataset includes an example MATLAB script that implements corrections to steady Vortex Lattice Method (VLM) results. Corrections include: Viscous lift curve slope. Lifting surface viscous drag (including lift-dependent viscous drag). Fuselage profile drag. Fuselage contribution to pitching moment. Tailplane contribution to pitching moment. The example includes experimental wind tunnel data from a model of an English Electric Canberra, along with software models of this aircraft in three popular steady VLM solvers: AVL, XFLR5 and OpenVSP/VSPAERO.
DOI: 10.11588/data/dsdjud/p7jm9j
title: Fig._S3-1.opju

description: :unav
DOI: 10.11588/data/dsdjud/lrm8id
title: Fig._2ab_and_Fig_S2-1.opju

description: :unav
DOI: 10.11588/data/dsdjud/kdlrwi
title: Chiral_Layered_Perovskites_Figures.docx

description: :unav
DOI: 10.11588/data/dsdjud/1gnnz7
title: Figure_S4-1a-e.opju

description: :unav
DOI: 10.11588/data/dsdjud/pxmk8r
title: Figure_S3-2b.opju

description: :unav
DOI: 10.11588/data/dsdjud/ufii9x
title: Figure_S3-4.opju

description: :unav
DOI: 10.11588/data/dsdjud/fhjl7a
title: Figure_S3-2a.opju

description: :unav
DOI: 10.11588/data/dsdjud/8gmqed
title: Rac_3BrMBA2PbI4.cif

description: :unav
DOI: 10.11588/data/dsdjud/wjspob
title: Figure_4a-c__S4-1f__S4-2__S4-3__S4-4.opju

description: :unav
DOI: 10.11588/data/dsdjud/7qexm0
title: Fig._3c__f.opju

description: :unav
DOI: 10.11588/data/dsdjud/ybchdy
title: Chiral_Layered_Perovskites_Supplemetary_Materials.docx

description: :unav
DOI: 10.11588/data/dsdjud/wo3pns
title: Fig_S3-5a.opju

description: :unav
DOI: 10.11588/data/dsdjud/xu4ftl
title: Figure_S2-3.opju

description: :unav
DOI: 10.11588/data/dsdjud/uvevkk
title: Fig_3abde.opju

description: :unav
DOI: 10.11588/data/dsdjud/7xxo2k
title: Chiral_Layered_Perovskites_Manunscirpt.docx

description: :unav
DOI: 10.11588/data/dsdjud/yij2zg
title: S_3BrMBA2PbI4.cif

description: :unav
DOI: 10.11588/data/dsdjud/2omrej
title: Fig._2d-f_CPL.opju

description: :unav
DOI: 10.11588/data/dsdjud/w0bdxe
title: Figure_4d.opju

description: :unav
DOI: 10.11588/data/dsdjud/azbgzm
title: Fig_S3-5b.opju

description: :unav
DOI: 10.11588/data/dsdjud/eiz0ta
title: Fig_S3-3_TCSPC_Intensity.opju

description: :unav
DOI: 10.11588/data/dsdjud/vencrn
title: Fig_2c.opju

description: :unav
DOI: 10.11588/data/dsdjud/caz7fy
title: Fig._1d_and_Fig_S1-1.opju

description: :unav
DOI: 10.11588/data/dsdjud
title: Bright Circularly-Polarized Photoluminescence in Chiral Layered Lead-Halide Perovskites [Research Data]

description: Hybrid semiconductor materials are predicted to lock chirality into place and encode asymmetry into their electronic states, while the softness of their crystal lattice accommodates lattice strain and maintains high crystal quality with the low defect densities necessary for high luminescence yields. The realization of chiral bulk emitters with bright circularly-polarized luminescence from such materials is desired for the design of chiroptical photonic and opto-spintronic applications. Here, we report photoluminescence quantum efficiencies (PLQE) as high as 39%, and degrees of circularly polarized photoluminescence of up to 52%, at room temperature, in the chiral layered hybrid lead-halide perovskites (R/S/Rac)-3BrMBA2PbI4 (3BrMBA = 1-(3-Bromphenyl)-ethylamine). Using x-ray diffraction and density-functional theory we elucidate the detailed chirality transfer mechanism from chiral crystal structures to spin-orbit-split band structures. Using state-of-the-art transient chiroptical spectroscopy, we rationalize the excellent photoluminescence yields from suppression of non-radiative loss channels and very high rates of radiative recombination. We further find that photo-excitations sustain polarization lifetimes that exceed the timescales of radiative decays, which rationalize the high degrees of polarized luminescence. We postulate that the superior optoelectronic properties of the layered hybrid perovskites arise from their special tolerance to crystal structure chirality, which we carefully designed by cation engineering. Our findings pave the way towards high-performance solution-processed photonic systems for chiroptical applications and chiral-spintronic logic at room temperature.
DOI: 10.5281/zenodo.8177381
title: Allometric parameters and linear breaking strength of intestinal segments

description: The dataset reports bodyweight, crown-rump-length, and the linear breaking strengths of small and large intestinal segments in German Landrace pigs.
DOI: 10.5281/zenodo.8177380
title: Allometric parameters and linear breaking strength of intestinal segments

description: The dataset reports bodyweight, crown-rump-length, and the linear breaking strengths of small and large intestinal segments in German Landrace pigs.
DOI: 10.25625/imz2nu/eyy8xn
title: allyl_tfm_220411_1706.out

description: :unav
DOI: 10.25625/imz2nu/meplyz
title: BZ220325_-_S200_CuCF3+Allyl_BD02_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/hdgocs
title: BZ210308_S200_bpyCu(CF3)3_sBuLi_BD03_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/w98wlp
title: imc_cu_tfm2-tfm2_211213_1344.out

description: :unav
DOI: 10.25625/imz2nu/a93wvr
title: BZ20210217_CuMe(CF3)3_BD14.xy

description: :unav
DOI: 10.25625/imz2nu/p2zx1p
title: BZ210928_-_Cu(CF3)4_BD17_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/rnevco
title: BZ20210218_CuPhCl(CF3)3_BD01.xy

description: :unav
DOI: 10.25625/imz2nu/ubdlsf
title: BZ210219_bpyCu(CF3)3+MePhMgBr_BD05.xy

description: :unav
DOI: 10.25625/imz2nu/2ajtjs
title: BZ201208_bpyCu(CF3)3+nBuMgCl_BD_06.xy

description: :unav
DOI: 10.25625/imz2nu/3t1jh9
title: BZ201008_Cu(CF3)3_MS3CID_269.9.xy

description: :unav
DOI: 10.25625/imz2nu/fzakzw
title: BZ210308_S200_bpyCu(CF3)3_sBuLi_BD24_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/lrcknu
title: BZ20210217_CuPhF(CF3)3_BD31.xy

description: :unav
DOI: 10.25625/imz2nu/hktlwk
title: BZ201208_bpyCu(CF3)3+nBuMgCl_BD_40.xy

description: :unav
DOI: 10.25625/imz2nu/ogen30
title: imc_cc_cu_tfm2-et_tfm_220413_1137.out

description: :unav
DOI: 10.25625/imz2nu/ewgewk
title: BZ220325_-_S200_CuCF3+Allyl_BD112_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/j9wcmj
title: BZ201208_bpyCu(CF3)3+nBuMgCl_BD_37.xy

description: :unav
DOI: 10.25625/imz2nu/fcu33c
title: BZ20210217_CuPh(CF3)3_BD08.xy

description: :unav
DOI: 10.25625/imz2nu/bjyqk0
title: BZ201208_bpyCu(CF3)3+EtMgBr+THF_BD_17.xy

description: :unav
DOI: 10.25625/imz2nu/8hfmjb
title: BZ20210218_CuPhCl(CF3)3_BD20.xy

description: :unav
DOI: 10.25625/imz2nu/xeofvo
title: BZ20210217_CuPhF(CF3)3_BD03.xy

description: :unav
DOI: 10.25625/imz2nu/vmbnfr
title: BZ210308_S200_bpyCu(CF3)3_sBuLi_BD20_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/rncbdw
title: BZ210928_-_Cu(CF3)4_BD08_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/3cxgme
title: BZ20210217_CuMe(CF3)3_BD07.xy

description: :unav
DOI: 10.25625/imz2nu/jnzu5x
title: BZ201208_bpyCu(CF3)3+EtMgBr+THF_BD_37.xy

description: :unav
DOI: 10.25625/imz2nu/koit09
title: BZ220325_-_S200_CuCF3+Allyl_BD10_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/dka6zl
title: BZ20210217_CuPh(CF3)3_BD06.xy

description: :unav
DOI: 10.25625/imz2nu/6vpg0s
title: BZ20210218_CuPhCl(CF3)3_BD18.xy

description: :unav
DOI: 10.25625/imz2nu/yezofu
title: BZ210223_bpyCu(CF3)3+CF3-PhMgBr_BD16_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/wkbg2q
title: BZ220325_-_S200_CuCF3+Allyl_BD105_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/hwsmb7
title: BZ201208_bpyCu(CF3)3+EtMgBr+THF_BD_02.xy

description: :unav
DOI: 10.25625/imz2nu/vi0ou2
title: BZ201208_bpyCu(CF3)3+EtMgBr+THF_BD_15.xy

description: :unav
DOI: 10.25625/imz2nu/a4d6ue
title: BZ201208_bpyCu(CF3)3+nBuMgCl_BD_03.xy

description: :unav
DOI: 10.25625/imz2nu/z3kbm8
title: bu_tfm_220411_1705.out

description: :unav
DOI: 10.25625/imz2nu/xukfcj
title: BZ20210210_Cu(sBu)(CF3)3_Spectra.xy

description: :unav
DOI: 10.25625/imz2nu/qpp2hl
title: BZ210223_bpyCu(CF3)3+CF3-PhMgBr_BD21_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/wfdhbw
title: BZ20210217_CuMe(CF3)3_BD18.xy

description: :unav
DOI: 10.25625/imz2nu/ececuz
title: BZ20210218_CuPhCl(CF3)3_BD31.xy

description: :unav
DOI: 10.25625/imz2nu/jnp9x7
title: BZ201208_bpyCu(CF3)3+nBuMgCl_BD_14.xy

description: :unav
DOI: 10.25625/imz2nu/bwm2wz
title: BZ220325_-_S200_CuCF3+Allyl_BD113_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/nmvh4v
title: imc_cc_cu_tfm2-allyl_tfm_220413_1553.out

description: :unav
DOI: 10.25625/imz2nu/x9r4tc
title: BZ210928_-_Cu(CF3)4_BD19_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/9de3qn
title: BZ210928_-_Cu(CF3)4_BD21_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/mujffd
title: cu_et_tfm3_211211_1839.out

description: :unav
DOI: 10.25625/imz2nu/dakqse
title: ph_rad_220412_1622.out

description: :unav
DOI: 10.25625/imz2nu/z0sfjx
title: BZ220325_-_S200_CuCF3+Allyl_BD25_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/gvecfh
title: BZ210308_S200_bpyCu(CF3)3_sBuLi_BD27_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/3zryxt
title: BZ210414_Cu(ArOMe)(CF3)3_BD01_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/mefyou
title: BZ20210217_CuMe(CF3)3_BD24.xy

description: :unav
DOI: 10.25625/imz2nu/l6auix
title: BZ201208_bpyCu(CF3)3+nBuMgCl_BD_43.xy

description: :unav
DOI: 10.25625/imz2nu/xmfmet
title: BZ210223_bpyCu(CF3)3+CF3-PhMgBr_BD22_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/qqatie
title: BZ20210217_CuPhF(CF3)3_BD15.xy

description: :unav
DOI: 10.25625/imz2nu/o6bthc
title: BZ220325_-_S200_CuCF3+Allyl_BD04_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/axnnsh
title: BZ210308_S200_bpyCu(CF3)3_sBuLi_BD18_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/ga7g7u
title: BZ20210217_CuPh(CF3)3_BD02.xy

description: :unav
DOI: 10.25625/imz2nu/s0c2ns
title: BZ210414_Cu(ArOMe)(CF3)3_BD02_spectrum.xy

description: :unav
DOI: 10.25625/imz2nu/n27el4
title: BZ20210218_CuPhCl(CF3)3_BD29.xy

description: :unav
DOI: 10.25625/imz2nu/nyagmc
title: BZ20210217_CuPh(CF3)3_BD14.xy

description: :unav
DOI: 10.25625/imz2nu/rdxqtd
title: BZ20210217_CuPhF(CF3)3_BD05.xy

description: :unav
DOI: 10.25625/imz2nu/gbqvcv
title: ts_cc_cu_sbu_tfm3_220412_1106.out

description: :unav
DOI: 10.25625/imz2nu/qcnczg
title: BZ20210217_CuPh(CF3)3_BD09.xy

description: :unav
DOI: 10.25625/imz2nu/ehpaza
title: bu_rad_220411_1603.out

description: :unav
DOI: 10.25625/imz2nu/cf0dt7
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