cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2cyb4t
title: CCDC 2202926: Experimental Crystal Structure Determination

description: Related Article: Atanu Ghosh, Masaaki Akabane-Nakata, Jayanta Kundu, Joel M. Harp, Mimouna Madaoui, Martin Egli, Muthiah Manoharan, Surajit Sinha|2023|Org.Lett.|25|901|doi:10.1021/acs.orglett.2c04067
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cyb1q
title: CCDC 2202923: Experimental Crystal Structure Determination

description: Related Article: Atanu Ghosh, Masaaki Akabane-Nakata, Jayanta Kundu, Joel M. Harp, Mimouna Madaoui, Martin Egli, Muthiah Manoharan, Surajit Sinha|2023|Org.Lett.|25|901|doi:10.1021/acs.orglett.2c04067
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7653741
title: BCdatabaser - large.fungi.none.2023-02-18.zip

description: Parameters: --marker-search-string 'large subunit ribosomal' OR '26S ribosomal' OR 'D1/D2 domain' OR LSU --taxonomic-range Fungi --sequence-length-filter 100:2000 --sequences-per-taxon 9
description: This dataset was automatically created with data from NCBI using the BCdatabaser tool
description: {"references": ["Keller et al. (2019) BCdatabaser: on-the-fly reference database creation for (meta-)barcoding. EcoEvoRxiv (2019); https://doi.org/10.32942/osf.io/cmfu2 - https://github.com/molbiodiv/bcdatabaser", "eutils: Sayers E. E-utilities Quick Start. 2008 Dec 12 [Updated 2018 Oct 24]. In: Entrez Programming Utilities Help [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2010-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK25500/", "NCBI::Taxonomy: F\u00f6rster et al. https://github.com/greatfireball/NCBI-Taxonomy - F\u00f6rster F. greatfireball/NCBI-Taxonomy v0.90. Zenodo. 2018 Oct 15; https://doi.org/10.5281/zenodo.1462861", "SeqFilter: Hackl et al. https://github.com/BioInf-Wuerzburg/SeqFilter", "dispr: Cofield et al. https://github.com/douglasgscofield/dispr", "Krona: Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385."]}
DOI: 10.5281/zenodo.7653740
title: BCdatabaser - large.fungi.none.2023-02-18.zip

description: Parameters: --marker-search-string 'large subunit ribosomal' OR '26S ribosomal' OR 'D1/D2 domain' OR LSU --taxonomic-range Fungi --sequence-length-filter 100:2000 --sequences-per-taxon 9
description: This dataset was automatically created with data from NCBI using the BCdatabaser tool
description: {"references": ["Keller et al. (2019) BCdatabaser: on-the-fly reference database creation for (meta-)barcoding. EcoEvoRxiv (2019); https://doi.org/10.32942/osf.io/cmfu2 - https://github.com/molbiodiv/bcdatabaser", "eutils: Sayers E. E-utilities Quick Start. 2008 Dec 12 [Updated 2018 Oct 24]. In: Entrez Programming Utilities Help [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2010-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK25500/", "NCBI::Taxonomy: F\u00f6rster et al. https://github.com/greatfireball/NCBI-Taxonomy - F\u00f6rster F. greatfireball/NCBI-Taxonomy v0.90. Zenodo. 2018 Oct 15; https://doi.org/10.5281/zenodo.1462861", "SeqFilter: Hackl et al. https://github.com/BioInf-Wuerzburg/SeqFilter", "dispr: Cofield et al. https://github.com/douglasgscofield/dispr", "Krona: Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385."]}
DOI: 10.6084/m9.figshare.21921834
title: RNA-seq of MC3T3-E1 by RPM treatment

description: PTH 1-34 (teriparatide) is approved by FDA for the treatment of postmenopausal osteoporosis. Iron overload is a major contributing factor for bone loss induced by unloading. Whether iron metabolism is involved in the regulation of PTH 1-34 on unloading-induced osteoporosis has not yet been reported. Here, we found that PTH 1-34 attenuated bone loss in unloading mice. PTH 1-34 regulated the disturbance of iron metabolism in unloading mice by activating Nrf2 and further promoting hepcidin expression in the liver. In addition, the Nrf2 inhibitor selectively blocked hepcidin expression in the liver of unloading mice, which neutralized the inhibitory effect of PTH 1-34 on bone loss and the recovery of iron metabolism in unloading mice. Finally, we found that PTH 1-34 promoted the differentiation and inhibited apoptosis of osteoblasts by regulating iron metabolism and maintaining redox balance under unloading conditions. Our results suggested that PTH 1-34 promoted bone formation by regulating iron metabolism under unloading conditions.
DOI: 10.34740/kaggle/dsv/5023398
title: arXiv Dataset
DOI: 10.17909/y1d0-v310
title: High-resolution images of young quasars

description: The HST imaging of the young quasars, used in the paper "Detecting and Characterizing Young Quasars. III.
The Impact of Gravitational Lensing Magnification" (Yue et al. 2023).
DOI: 10.6084/m9.figshare.21922752.v2
title: Raw and mapped small RNA seq reads post oral delivery of dsRNAs to Psylliodes chrysocephala

description: Raw and trimmed small RNA seq reads obtained from <em>Psylliodes chrysocephala </em>treated with double-stranded (ds) RNAs (dsSec23, dsVatpG, or dsGPF). The".bam" files are the output of Bowtie2, representing the coverage of reads perfectly mapping onto the respective dsRNA treatment. "_reads" files are the raw reads obtained by sequencing on a DNBSEQ-G400 platform and the "_trimmed.fq.gz" files represent the reads cleaned from adaptor sequences as well as reads having quality scores below 30 and lengths below 18 nt and above 25. The bam files could be investigated by, for instance, R package "viRome" which can output the coverages per position on the respective dsRNA sequence or directly using a software such as Tablet for which the dsRNA index files are required and also provided here ("_index" files). Of note the dsRNA sequence in the index file dsVatpG_index is antisense of its target gene whereas for dsSec23_index it is in sense direction. Another important note regarding the directionality is that all the raw reads are in reverse direction, so a siRNA that is antisense to its target in the dataset would be actually sense. <br> Further details regarding the methodology used during the sampling and sequencing is as follows: We performed small RNA sequencing to investigate siRNAs generated from the delivered dsRNAs. The whole bodies of three male and three female pre-aestivation beetles that had received the 200 ng/cm2 <em>Sec23</em>, <em>VatpG</em>, or GFP dsRNA treatment were sampled between 2 DPT and 3 DPT by placing them in liquid nitrogen (n = 3). Specifically, beetles that had consumed at least 50% of the dsRNA-containing leaf disk were selected. The total RNA from the beetles was extracted using the Quick-RNA Tissue/Insect Kit (Zymo Research EUROPE GMBH, Freiburg, Germany) and the RNA integrity was checked via RNA fragment analysis using Agilent 2100 Bioanalyzer (Agilent Technologies). The NEBNext® Multiplex Small RNA Library Prep Set for Illumina® (New England Biolabs GmbH, Frankfurt, Germany) kit was used for the library preparation according to the manufacturers’ instructions and all libraries were pooled based on their concentrations (see Tab. S5 for indexing information). The cDNAs within the range of 141 to 172 bp were size‑selected through gel electrophoresis, and a final concentration of 6.4 ng/µL was obtained. The small RNA sequencing was performed by BGI Tech Solutions Co. Ltd. (Hong Kong) on a DNBSEQ-G400 platform. The number and quality of the raw reads were checked using FastQC (v0.11.9) (Tab. S6). The adapters and low-quality reads were removed from the raw reads using Trim Galore! (v0.6.7), while reads within the range of 18-25 nt were retained. Cleaned and filtered reads were mapped onto the dsRNA sequences via Bowtie2 (v2.4.5) using the very sensitive end‑to-end alignment option and allowing zero mismatches. The small RNA reads from GFP dsRNA treated beetles were mapped onto <em>Sec23</em> and <em>VatpG</em> dsRNA sequences as background control. <br>
DOI: 10.5281/zenodo.7407884
title: TIEGCM simulation for high-latitude zonal winds

description: Description of Data in the "windwall_data.mat": UN: modeled zonal winds (m/s) DUDT_UN: the acceleration due to total forces (cm/s^2); FD_UN: the acceleration due to ion drag (cm/s^2); ZL: the acceleration due to pressure gradient (cm/s^2); VIS_VERT_UN: the acceleration due to viscosity (cm/s^2); COR_UN: the acceleration due to Coriolis (cm/s^2); CENT_UN: the acceleration due to centrifugal (cm/s^2); NE: ionospheric electron density (cm^{-3}) UI: zonal ExB (m/s) "TIEGCM_modeled_UN_windwall_fig2.rar" is modeled wind data in Figure 2 "clt_*.mat" and "cmlat_*.mat" are geomagnetic local time and latitudes,
DOI: 10.5281/zenodo.4387672
title: TIEGCM simulation for high-latitude zonal winds

description: Description of Data in the "windwall_data.mat": UN: modeled zonal winds (m/s) DUDT_UN: the acceleration due to total forces (cm/s^2); FD_UN: the acceleration due to ion drag (cm/s^2); ZL: the acceleration due to pressure gradient (cm/s^2); VIS_VERT_UN: the acceleration due to viscosity (cm/s^2); COR_UN: the acceleration due to Coriolis (cm/s^2); CENT_UN: the acceleration due to centrifugal (cm/s^2); NE: ionospheric electron density (cm^{-3}) UI: zonal ExB (m/s) "TIEGCM_modeled_UN_windwall_fig2.rar" is modeled wind data in Figure 2 "clt_*.mat" and "cmlat_*.mat" are geomagnetic local time and latitudes,
DOI: 10.5519/qd.cm9tnp0j
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5414147 records)
DOI: 10.34740/kaggle/dsv/5023747
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.6084/m9.figshare.22114163
title: ERA5 extractions for 4km sampling points

description: four csv files in total: two for precipitation and the rests are for temperature. <br> Daily temperature and 11-day running precipitation from 1951-1990 were averaged as 'norm', to comparing with 2021 records.
DOI: 10.6084/m9.figshare.22114163.v1
title: ERA5 extractions for 4km sampling points

description: four csv files in total: two for precipitation and the rests are for temperature. <br> Daily temperature and 11-day running precipitation from 1951-1990 were averaged as 'norm', to comparing with 2021 records.
DOI: 10.6084/m9.figshare.22122212
title: Appendix Tables

description: Appendix tables
DOI: 10.6084/m9.figshare.22122212.v1
title: Appendix Tables

description: Appendix tables
DOI: 10.6084/m9.figshare.22110779
title: gridded points: EVI values changes due to heat dome

description: 4-km points across effected regions during 2021 heat dome event. EVI records shortly after the heat dome were organized, and were compared with the average EVI values in normal years.
DOI: 10.6084/m9.figshare.22110779.v1
title: gridded points: EVI values changes due to heat dome

description: 4-km points across effected regions during 2021 heat dome event. EVI records shortly after the heat dome were organized, and were compared with the average EVI values in normal years.
DOI: 10.6084/m9.figshare.22110767
title: gridded points attributes values

description: 4-km gridded points across effected regions in 2021 heat dome. Including coordinates, elevations, vegetation (percentage of deciduous), and historical monthly climate variables.
DOI: 10.6084/m9.figshare.22110767.v1
title: gridded points attributes values

description: 4-km gridded points across effected regions in 2021 heat dome. Including coordinates, elevations, vegetation (percentage of deciduous), and historical monthly climate variables.
DOI: 10.6084/m9.figshare.22110749
title: ERA5: mean temperature

description: This folder contains 2 files: one is ERA5 daily temperature in year 2021, and another one is the average daily temperatures between 1951-1990.
DOI: 10.6084/m9.figshare.22110689
title: ERA5: running average precipitation

description: 11-day running average precipitation records; This file contains 2 files: one is for 2021 daily records, and another is 1951-1990 mean daily running precipitation values.
DOI: 10.6084/m9.figshare.22110689.v1
title: ERA5: running average precipitation

description: 11-day running average precipitation records; This file contains 2 files: one is for 2021 daily records, and another is 1951-1990 mean daily running precipitation values.
DOI: 10.6084/m9.figshare.21936024.v6
title: Religion and Diplomacy

description: The dataset includes signatory information on 385 multilateral treaties signed between 1750 to 1900. This is a part of the MATRS project, led by Robert A. Denemark and Matthew J. Hoffmann.
DOI: 10.25582/data.2019-08.1830591/1927187
title: Data for EMSL Project 49960 from August 2019
DOI: 10.6084/m9.figshare.21936024
title: Religion and Diplomacy

description: The dataset includes signatory information on 385 multilateral treaties signed between 1750 to 1900. This is a part of the MATRS project, led by Robert A. Denemark and Matthew J. Hoffmann.
DOI: 10.6084/m9.figshare.21936024.v7
title: Religion and Diplomacy

description: The dataset includes signatory information on 385 multilateral treaties signed between 1750 to 1900. This is a part of the MATRS project, led by Robert A. Denemark and Matthew J. Hoffmann.
DOI: 10.6084/m9.figshare.22122227.v1
title: 新冠自我康复手册中文版（WHO）(1).pdf

description: materials
DOI: 10.6084/m9.figshare.22122227
title: 新冠自我康复手册中文版（WHO）(1).pdf

description: materials
DOI: 10.6084/m9.figshare.16823143.v1
title: DocWarn Replication Package

description: A replication package of "Towards Reliable Agile Iterative Planning via Predicting Documentation Changes of Work Items" published at the 19th International Conference on Mining Software Repositories – Technical track (MSR 2022 Technical track). Replication code DocWarn-T: /code/model/DocWarn_T.py DocWarn-H: /code/model/DocWarn_H.py DocWarn-C: /code/Rscript/DocWarn_C.R Analysis code for RQ1 can be found at /code/Rscript/ RQ1_performance_measure.R must be run first to measure the performance of the model. Then, run RQ1_performance_stattest.R to perform statistical test on the measured performance. RQ3_rank_features.R is used to find a statistical distinct rank for each features in DocWarn-C. Results and dataset can be found at /data (only available on on Figshare version: https://figshare.com/s/88547b3c197b21b60f7c) /data/data_reverted_cleaned stores dataset that the work items were reverted to sprint assignment time. /data/trainingData stores the dataset for each cross-validation round. /data/features stores the metrics extracted from each work items in the dataset. /data/modelResult stores the DocWarn-C R models (/models/...), performance of each DocWarn variations, and the result of features ranking. Manual classification /rq2_manual_validation.csv is the result of RQ2's manual classification to validate DocWarn-C. /rq2_manual_validation_external.csv is the manual classification that were done by the external coder (to measure the inter-rater agreement). code 0 = others, 1 = changing scope, 2 = defining scope, 3 = adding additional detail, 4 = adding implementation detail DistilRoberta for DocWarn-T and DocWarn-H can be found at /distilroberta-base-jira (only available on on Figshare version: https://figshare.com/s/88547b3c197b21b60f7c) This is the fine-tuned version of distilroberta-base with 110k JIRA issues.
DOI: 10.13140/rg.2.2.20134.19529
title: Anaemia in Pregnancy Related Calculators
DOI: 10.13140/rg.2.2.33555.96802
title: Anaemia in Pregnancy Related Calculators
DOI: 10.6084/m9.figshare.16823143
title: DocWarn

description: A replication package of "Towards Reliable Agile Iterative Planning via Predicting Documentation Changes of Work Items" published at the 19th International Conference on Mining Software Repositories – Technical track (MSR 2022 Technical track). Replication code DocWarn-T: /code/model/DocWarn_T.py DocWarn-H: /code/model/DocWarn_H.py DocWarn-C: /code/Rscript/DocWarn_C.R Analysis code for RQ1 can be found at /code/Rscript/ RQ1_performance_measure.R must be run first to measure the performance of the model. Then, run RQ1_performance_stattest.R to perform statistical test on the measured performance. RQ3_rank_features.R is used to find a statistical distinct rank for each features in DocWarn-C. Results and dataset can be found at /data (only available on on Figshare version: https://figshare.com/s/88547b3c197b21b60f7c) /data/data_reverted_cleaned stores dataset that the work items were reverted to sprint assignment time. /data/trainingData stores the dataset for each cross-validation round. /data/features stores the metrics extracted from each work items in the dataset. /data/modelResult stores the DocWarn-C R models (/models/...), performance of each DocWarn variations, and the result of features ranking. Manual classification /rq2_manual_validation.csv is the result of RQ2's manual classification to validate DocWarn-C. /rq2_manual_validation_external.csv is the manual classification that were done by the external coder (to measure the inter-rater agreement). code 0 = others, 1 = changing scope, 2 = defining scope, 3 = adding additional detail, 4 = adding implementation detail DistilRoberta for DocWarn-T and DocWarn-H can be found at /distilroberta-base-jira (only available on on Figshare version: https://figshare.com/s/88547b3c197b21b60f7c) This is the fine-tuned version of distilroberta-base with 110k JIRA issues.
DOI: 10.6084/m9.figshare.16823143.v2
title: DocWarn

description: A replication package of "Towards Reliable Agile Iterative Planning via Predicting Documentation Changes of Work Items" published at the 19th International Conference on Mining Software Repositories – Technical track (MSR 2022 Technical track). Replication code DocWarn-T: /code/model/DocWarn_T.py DocWarn-H: /code/model/DocWarn_H.py DocWarn-C: /code/Rscript/DocWarn_C.R Analysis code for RQ1 can be found at /code/Rscript/ RQ1_performance_measure.R must be run first to measure the performance of the model. Then, run RQ1_performance_stattest.R to perform statistical test on the measured performance. RQ3_rank_features.R is used to find a statistical distinct rank for each features in DocWarn-C. Results and dataset can be found at /data (only available on on Figshare version: https://figshare.com/s/88547b3c197b21b60f7c) /data/data_reverted_cleaned stores dataset that the work items were reverted to sprint assignment time. /data/trainingData stores the dataset for each cross-validation round. /data/features stores the metrics extracted from each work items in the dataset. /data/modelResult stores the DocWarn-C R models (/models/...), performance of each DocWarn variations, and the result of features ranking. Manual classification /rq2_manual_validation.csv is the result of RQ2's manual classification to validate DocWarn-C. /rq2_manual_validation_external.csv is the manual classification that were done by the external coder (to measure the inter-rater agreement). code 0 = others, 1 = changing scope, 2 = defining scope, 3 = adding additional detail, 4 = adding implementation detail DistilRoberta for DocWarn-T and DocWarn-H can be found at /distilroberta-base-jira (only available on on Figshare version: https://figshare.com/s/88547b3c197b21b60f7c) This is the fine-tuned version of distilroberta-base with 110k JIRA issues.
DOI: 10.13140/rg.2.2.18456.47362
title: Anaemia in Pregnancy Related Calculators
DOI: 10.25582/data.2021-10.2370681/1928368
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-10.2370637/1928369
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-10.2370686/1928370
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-10.2370629/1928371
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-09.2326781/1928372
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-09.2327004/1928373
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-10.2370655/1928374
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-10.2370682/1928375
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-09.2325793/1928376
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-09.2326368/1928377
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-09.2326865/1928378
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-10.2370632/1928379
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-09.2325866/1928380
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-09.2326816/1928381
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-10.2370675/1928382
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-10.2370663/1928383
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-09.2326631/1928384
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-09.2326584/1928385
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-09.2326874/1928386
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-09.2327042/1928387
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-09.2326593/1928388
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2021-10.2370674/1928389
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-10.2370646/1928390
title: Data for EMSL Project 60160 from October 2021
DOI: 10.25582/data.2021-09.2326756/1928391
title: Data for EMSL Project 60160 from September 2021
DOI: 10.25582/data.2019-09.1847438/1928392
title: Data for EMSL Project 50298 from September 2019
DOI: 10.25582/data.2019-09.1847626/1928394
title: Data for EMSL Project 50298 from September 2019
DOI: 10.25582/data.2020-05.1984670/1928402
title: Data for EMSL Project 50386 from May 2020
DOI: 10.25582/data.2020-10.2076661/1928528
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2077122/1928529
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2077109/1928530
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2075050/1928531
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2076997/1928532
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2077139/1928533
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2077339/1928534
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2076656/1928535
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2076209/1928536
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2076277/1928537
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2077085/1928538
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2077138/1928539
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2076130/1928540
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2077334/1928541
title: Data for EMSL Project 51600 from October 2020
DOI: 10.25582/data.2020-10.2077349/1928542
title: Data for EMSL Project 51600 from October 2020
DOI: 10.6084/m9.figshare.22122182
title: Vsh and Radial Anisotropy of Tongzhou

description: A Vsh and Radial anisotropy model of Tonghzou, Beijing obtained from Surface waves dispersion inversion.