cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3576539
title: Genotyping-By-Sequencing and Reference Genome Enabled Variant Discovery in Octoploid Strawberry

description: (P0652) Genotyping-By-Sequencing and Reference Genome Enabled Variant Discovery in Octoploid Strawberry Genotyping-by-sequencing (GBS) approaches have enabled routine high-density genome-wide DNA variant discovery in numerous agriculturally important species. Applications of GBS in octoploid (2n = 8x = 56) strawberry (Fragaria × ananassa) have been hindered by the absence of a reference genome for physically mapping DNA sequences; for discovering variants with sub-genome resolution, or effectively distinguishing homologous from homeologous variation. High-quality reference genome assemblies have recently emerged, supplying the foundation for this study, which focused on demonstrating the utility of GBS for calling sub-genome specific DNA variants in octoploid strawberry. To reduce genomic DNA complexity, double-digest protocols were tested on diverse accessions with two restriction enzyme combinations (PstI-MseI and HindIII-MseI). GBS libraries were sequenced on an Illumina HiSeq 4000 using a 150 bp paired-end protocol. For the purpose of this study, we describe the deployment of a flexible bioinformatic pipeline for GBS-facilitated variant discovery in octoploid strawberry. The percentage of uniquely mapped reads ranged from 51.41% for PstI-MseI to 55.56% for HindIII- MseI resulting in 1,591,764 and 2,362,556 unique locations, respectively. The number of discovered variants was 2.5-fold greater for HindIII-MseI (491,811) than PstI-MseI (199,486). The GBS protocols uncovered a dense genome-wide landscape of DNA variants for high- precision genetic mapping, identification of DNA variants associated with agriculturally important phenotypes, genomic-enabled breeding, and other applications in octoploid strawberry. Poster: PDF of poster and abstract for PAG 2018 (P0652) Figures: PNGs of figures on the poster Scrips: Txt files of SLURM scripts used to generate the follow .vcf files. - 8x_GBS_0_index: Uses BWA to index the reference genome (Edger et al 2019) for later use. - 8x_GBS_1_Demultiplex: Used Sabre to demultiplex fastq.gz files. Demultiplex_key_PE links individuals to their unique barcode. - 8x_GBS_2_Main: adapter removal, sequence alignment, and individual variant calling as a SLURM array. results in a .gvcf file for individuals. - 8x_GBS_3_Variant: population-level variant calling to a final .vcf file VCF: Two VCF files from the two enzyme experiments. H = HindIII-MseI; P = PstI-MseI
DOI: 10.5281/zenodo.3576540
title: Genotyping-By-Sequencing and Reference Genome Enabled Variant Discovery in Octoploid Strawberry

description: (P0652) Genotyping-By-Sequencing and Reference Genome Enabled Variant Discovery in Octoploid Strawberry Genotyping-by-sequencing (GBS) approaches have enabled routine high-density genome-wide DNA variant discovery in numerous agriculturally important species. Applications of GBS in octoploid (2n = 8x = 56) strawberry (Fragaria × ananassa) have been hindered by the absence of a reference genome for physically mapping DNA sequences; for discovering variants with sub-genome resolution, or effectively distinguishing homologous from homeologous variation. High-quality reference genome assemblies have recently emerged, supplying the foundation for this study, which focused on demonstrating the utility of GBS for calling sub-genome specific DNA variants in octoploid strawberry. To reduce genomic DNA complexity, double-digest protocols were tested on diverse accessions with two restriction enzyme combinations (PstI-MseI and HindIII-MseI). GBS libraries were sequenced on an Illumina HiSeq 4000 using a 150 bp paired-end protocol. For the purpose of this study, we describe the deployment of a flexible bioinformatic pipeline for GBS-facilitated variant discovery in octoploid strawberry. The percentage of uniquely mapped reads ranged from 51.41% for PstI-MseI to 55.56% for HindIII- MseI resulting in 1,591,764 and 2,362,556 unique locations, respectively. The number of discovered variants was 2.5-fold greater for HindIII-MseI (491,811) than PstI-MseI (199,486). The GBS protocols uncovered a dense genome-wide landscape of DNA variants for high- precision genetic mapping, identification of DNA variants associated with agriculturally important phenotypes, genomic-enabled breeding, and other applications in octoploid strawberry. Poster: PDF of poster and abstract for PAG 2018 (P0652) Figures: PNGs of figures on the poster Scrips: Txt files of SLURM scripts used to generate the follow .vcf files. - 8x_GBS_0_index: Uses BWA to index the reference genome (Edger et al 2019) for later use. - 8x_GBS_1_Demultiplex: Used Sabre to demultiplex fastq.gz files. Demultiplex_key_PE links individuals to their unique barcode. - 8x_GBS_2_Main: adapter removal, sequence alignment, and individual variant calling as a SLURM array. results in a .gvcf file for individuals. - 8x_GBS_3_Variant: population-level variant calling to a final .vcf file VCF: Two VCF files from the two enzyme experiments. H = HindIII-MseI; P = PstI-MseI
DOI: 10.17632/hfpzc3gydm.2
title: From Trait Anger to Aggressive Violations in Road Traffic

description: The file contains the values of the measured variables. Demographic: gender, age, and education. For personality: NEO IPIP (Goldberg et al., 2006), with the Romanian adaptation of IPIP-Ro (Iliescu, Popa, & Dimache, 2016). Variables: the answer to the items, hostility (trait anger) (TA), neuroticism (N), extraversion (E), openness to experience (O), agreeability (A) and consciousness (C). Behavioral, normative and control beliefs (TPB): a questionnaire tailored to the behavioral situation investigated was built Ajzen (2006). Variables: the answer to the items, intention (Int), attitude (Att), subjective norms (SN), descriptive norms (DN), and perceptive behavior control (PBC). Behavioral outcomes: DBQ (Reason, Manstead, Stephen, Baxter, & Campbell, 1990), with the Romanian adaptation (Sârbescu, 2013). Variables: the answer to the items, aggressive violations (AV), ordinary violations (OV), the total number of violations (TNVIO) and errors (Err).
DOI: 10.17632/hfpzc3gydm.3
title: From Trait Anger to Aggressive Violations in Road Traffic

description: The file contains the values of the measured variables. Demographic: gender, age, and education. For personality: NEO IPIP (Goldberg et al., 2006), with the Romanian adaptation of IPIP-Ro (Iliescu, Popa, & Dimache, 2016). Variables: the answer to the items, hostility (trait anger) (TA), neuroticism (N), extraversion (E), openness to experience (O), agreeability (A) and consciousness (C). Behavioral, normative and control beliefs (TPB): a questionnaire tailored to the behavioral situation investigated was built Ajzen (2006). Variables: the answer to the items, intention (Int), attitude (Att), subjective norms (SN), descriptive norms (DN), and perceptive behavior control (PBC). Behavioral outcomes: DBQ (Reason, Manstead, Stephen, Baxter, & Campbell, 1990), with the Romanian adaptation (Sârbescu, 2013). Variables: the answer to the items, aggressive violations (AV), ordinary violations (OV), the total number of violations (TNVIO) and errors (Err).
DOI: 10.17632/hfpzc3gydm.4
title: From Trait Anger to Aggressive Violations in Road Traffic

description: The file contains the values of the measured variables. Demographic: gender, age, and education. For personality: NEO IPIP (Goldberg et al., 2006), with the Romanian adaptation of IPIP-Ro (Iliescu, Popa, & Dimache, 2016). Variables: the answer to the items, hostility (trait anger) (TA), neuroticism (N), extraversion (E), openness to experience (O), agreeability (A) and consciousness (C). Behavioral, normative and control beliefs (TPB): a questionnaire tailored to the behavioral situation investigated was built Ajzen (2006). Variables: the answer to the items, intention (Int), attitude (Att), subjective norms (SN), descriptive norms (DN), and perceptive behavior control (PBC). Behavioral outcomes: DBQ (Reason, Manstead, Stephen, Baxter, & Campbell, 1990), with the Romanian adaptation (Sârbescu, 2013). Variables: the answer to the items, aggressive violations (AV), ordinary violations (OV), the total number of violations (TNVIO) and errors (Err).
DOI: 10.5281/zenodo.3576212
title: Dataset for Mininig Off-by-One Errors

description: The dataset that was made by downloading top 500 starred Java projects from GitHub and then eliminating common projects found with java-large-training and java-large-testing raw datasets*. The resulting dataset consists of 155 GitHub projects. The repositories were downloaded and the code analyzed using code found in the following repository: https://github.com/serg-ml4se-2019/group5-deep-bugs/tree/master (more specifically, the code found in bug_mining folder) * java-large raw dataset can be found at https://github.com/tech-srl/code2seq/blob/master/README.md#datasets
DOI: 10.17632/7ptj637tm3.1
title: Data for: Amide A Band is a Fingerprint for Water Dynamics in Reverse Osmosis Polyamide Membranes

description: Supporting Information
DOI: 10.17632/frkjdh6fpz.1
title: Data for: Investigation of rock fall mechanisms in small-scale experiments using magnetic tracking system
DOI: 10.17632/r8hj6sts3z.1
title: Thermal effects on flow and salinity distribution in coastal confined aquifers

description: This data set contains experimental data and numerical simulation results related to the paper - Li Pu, Pei Xin, Thuy T. M. Nguyen, Xiayang Yu, Ling Li, D. A. Barry, Thermal effects on flow and salinity distribution in coastal confined aquifers, Submitted to Geophysical Research Letters.
DOI: 10.5281/zenodo.3576603
title: alexandru-uta/cloud_network_variability_data: Cloud Network Variability Data

description: # Dataset on Cloud Network Variability This is the dataset obtained while benchmarking public and private clouds for the following article: ### Alexandru Uta, Alexandru Custura, Dmitry Duplyakin, Ivo Jimenez, Jan Rellermeyer, Carlos Maltzahn, Robert Ricci, Alexandru Iosup. Is Big Data Performance Reproducible in Modern Cloud Networks?. In proceedings of 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2020, February 25-27, Santa Clara, USA. The dataset contains bandwidth variability data for:<br> - Amazon EC2<br> - Google Compute Engine<br> - Microsoft Azure (limited data)<br> - Scaleway (limited data)<br> - SURFsara HPCCloud The dataset contains TCP latency (RTT) data for:<br> - Amazon EC2<br> - Google Compute Engine The dataset contains data regarding token bucket sizes (explained in depth in the aforementioned article) for Amazon EC2. Please note that the full archive is over 3.5 GB of data, made up of hundreds of thousands of small files. This is why we decided to archive the data, which we then split into smaller files, to accommodate for the Github max file size of 100 MB. ===== DETAILS ON THE ARCHIVE FORMAT ===== <strong>1. The Bandwidth Variability data:</strong><br> - archived in the bandwidth_variability_data.tar.bz2.parta, .partb, .partc<br> - after unpacking the archive, the data is split in directories per machine type, and experiment type, for example:<br> --- perfvar-aws-m5xlarge-fullspeed: contains iperf3 output files for continuous communication between 2 m5.xlarge VMs in Amazon EC2.<br> --- perfvar-google-4cpu-bursty-5s30s: contains iperf3 output files for bursty communication (5 seconds communication, 30 seconds break; repeat) <strong>2. The Latency Variability data:</strong><br> - archived in the file latency_study.tar.bz2<br> - after unpacking the archive, the directories contain TCP dump RTT data and iperf3 outputs <strong>3. The Token Bucket AWS study:</strong><br> - archived in the file token_bucket_study.tar.bz2<br> - contains files of form INSTANCE_TYPE-REGION-TIMESTAMP.{bw, raw, tb}<br> - files with extension .raw contain raw iperf3 client output<br> - files with extension .bw contain bandwidth samples taken at 1 second intervals from the iperf3 utility<br> - files with extension .tb contain a triple of form &lt;time_to_token_bucket_depletion, high_token_bucket_bandwidth, low_token_bucket_bandwidth&gt;<br> - example of a file name: c5.2xlarge-us-west-1-1567733720.raw
DOI: 10.5281/zenodo.3576604
title: alexandru-uta/cloud_network_variability_data: Cloud Network Variability Data

description: # Dataset on Cloud Network Variability This is the dataset obtained while benchmarking public and private clouds for the following article: ### Alexandru Uta, Alexandru Custura, Dmitry Duplyakin, Ivo Jimenez, Jan Rellermeyer, Carlos Maltzahn, Robert Ricci, Alexandru Iosup. Is Big Data Performance Reproducible in Modern Cloud Networks?. In proceedings of 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2020, February 25-27, Santa Clara, USA. The dataset contains bandwidth variability data for:<br> - Amazon EC2<br> - Google Compute Engine<br> - Microsoft Azure (limited data)<br> - Scaleway (limited data)<br> - SURFsara HPCCloud The dataset contains TCP latency (RTT) data for:<br> - Amazon EC2<br> - Google Compute Engine The dataset contains data regarding token bucket sizes (explained in depth in the aforementioned article) for Amazon EC2. Please note that the full archive is over 3.5 GB of data, made up of hundreds of thousands of small files. This is why we decided to archive the data, which we then split into smaller files, to accommodate for the Github max file size of 100 MB. ===== DETAILS ON THE ARCHIVE FORMAT ===== <strong>1. The Bandwidth Variability data:</strong><br> - archived in the bandwidth_variability_data.tar.bz2.parta, .partb, .partc<br> - after unpacking the archive, the data is split in directories per machine type, and experiment type, for example:<br> --- perfvar-aws-m5xlarge-fullspeed: contains iperf3 output files for continuous communication between 2 m5.xlarge VMs in Amazon EC2.<br> --- perfvar-google-4cpu-bursty-5s30s: contains iperf3 output files for bursty communication (5 seconds communication, 30 seconds break; repeat) <strong>2. The Latency Variability data:</strong><br> - archived in the file latency_study.tar.bz2<br> - after unpacking the archive, the directories contain TCP dump RTT data and iperf3 outputs <strong>3. The Token Bucket AWS study:</strong><br> - archived in the file token_bucket_study.tar.bz2<br> - contains files of form INSTANCE_TYPE-REGION-TIMESTAMP.{bw, raw, tb}<br> - files with extension .raw contain raw iperf3 client output<br> - files with extension .bw contain bandwidth samples taken at 1 second intervals from the iperf3 utility<br> - files with extension .tb contain a triple of form &lt;time_to_token_bucket_depletion, high_token_bucket_bandwidth, low_token_bucket_bandwidth&gt;<br> - example of a file name: c5.2xlarge-us-west-1-1567733720.raw
DOI: 10.26300/v4ad-ct11
title: Analysis Data
DOI: 10.18716/nmrshiftdb2/11178
title: NMR data for Allyl-carbamic acid isobutyl ester
DOI: 10.18716/nmrshiftdb2/11222
title: NMR data for 12,13-Dihydropomolic acid; 3,19-Dihydroxy-ursan-28-oic acid
DOI: 10.5281/zenodo.3576619
title: BCdatabaser - cytb.vertebrata.none.2019-12-15.zip

description: Parameters: --marker-search-string cytb --taxonomic-range Vertebrata --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.3576620
title: BCdatabaser - cytb.vertebrata.none.2019-12-15.zip

description: Parameters: --marker-search-string cytb --taxonomic-range Vertebrata --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: 10.7910/dvn/gy8pxp/tu1xmn
title: Codebook.pdf

description: :unav
DOI: 10.7910/dvn/gy8pxp/twbtgd
title: pew-analysis.R

description: :unav
DOI: 10.7910/dvn/ftpf3z/5dur3b
title: data.xls
DOI: 10.7910/dvn/ftpf3z/ava3gf
title: public_supplementary_material.pdf

description: Experimental material containing the questionnaire, the tutorial, the instructions and tasks shown during the experiment and the visual and textual activity definitions for the tasks used for the experiment reported in our paper.
DOI: 10.7910/dvn/gy8pxp/53vlzt
title: pew-merge.RData

description: :unav
DOI: 10.7910/dvn/gy8pxp/bt5tla
title: nar-df.tab

description: :unav
DOI: 10.7910/dvn/gy8pxp/dj9xdk
title: Figure-S1.R

description: :unav
DOI: 10.7910/dvn/gy8pxp/hsrjgu
title: cmps-merge.RData

description: :unav
DOI: 10.7910/dvn/gy8pxp/mbxdpr
title: README.txt

description: :unav
DOI: 10.7910/dvn/gy8pxp/qawxk7
title: did-vehicle.RData

description: :unav
DOI: 10.7910/dvn/gy8pxp/qyhn6c
title: cmps-analysis.R

description: :unav
DOI: 10.7910/dvn/gy8pxp/svn4th
title: cces-merge-rep.RData

description: :unav
DOI: 10.7910/dvn/gy8pxp/y50hqu
title: price-merge.RData

description: :unav
DOI: 10.7910/dvn/yhjucj/gci6d7
title: 12.Supplement.docx

description: Supplement appendix summarizing all analyses
DOI: 10.7910/dvn/yhjucj/iunogv
title: 10.Pubmed_research_core_analyses.do

description: Code for replicating core analyses of positive framing and forward citations (broader PubMed)
DOI: 10.7910/dvn/ftpf3z/jmydtt
title: screenshot_defintion_process.png

description: :unav
DOI: 10.7910/dvn/gy8pxp/1ayzaw
title: price-analysis.R

description: :unav
DOI: 10.7910/dvn/gy8pxp/jmanks
title: cces-analysis.R

description: :unav
DOI: 10.7910/dvn/gy8pxp/snfkqa
title: foia-aggregate.RData

description: :unav
DOI: 10.7910/dvn/yhjucj/5e66cy
title: 4.Clinical_research_core_analyses.do

description: Code for replicating core analyses of positive framing and forward citations
DOI: 10.7910/dvn/yhjucj/6z560r
title: 11.pubmed_articles_90pct_genderdesig.csv

description: Data for replicating core analysis based on a gender designation threshold of 90%
DOI: 10.7910/dvn/yhjucj/cowzog
title: 5.Clinical_research_ind_words.do

description: Code for replicating analyses of individual positive words
DOI: 10.7910/dvn/yhjucj/cz4xjr
title: 6.clinical_articles_90pct_genderdesig.tab

description: Data for replicating core analysis based on a gender designation threshold of 90%
DOI: 10.7910/dvn/yhjucj/fhqvsp
title: 2.Genderize_Rcode_unlimited_batch_query.R

description: Code for batch querying the Genderize database
DOI: 10.7910/dvn/yhjucj/hxb31e
title: 7.clinical_articles_50pct_genderdesig.tab

description: Data for replicating core analysis based on a gender designation threshold of 50%
DOI: 10.7910/dvn/yhjucj/k3nupe
title: 1.Positive_word_identification.do

description: Generic code for identifying positive word use in articles’ titles and abstracts
DOI: 10.7910/dvn/yhjucj/v33sna
title: 3.NLP_Nouns.py

description: Code for determining nouns immediately following positive adjectives
DOI: 10.17632/fktwyfj6b8.1
title: Data for: Impact of brine reinjection on the geothermal fluids of Los Azufres, Mexico, as determined by water stable isotopes

description: Data tables of stable isotopic composition and chloride content
DOI: 10.5519/qd.nf0riah9
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.7910/dvn/yhjucj/hrk3qz
title: Overview_key_variables.pdf

description: Overview of key variables across datasets
DOI: 10.7910/dvn/yhjucj/mdkkcj
title: 9.clinical_articles_95pct_genderdesig.tab

description: Data for replicating core analysis based on a gender designation threshold of 95%
DOI: 10.7910/dvn/yhjucj/mylnjd
title: README.rtf

description: Overview of dataverse
DOI: 10.7910/dvn/yhjucj/qdjxkx
title: 8.clinical_articles_80pct_genderdesig.tab

description: Data for replicating core analysis based on a gender designation threshold of 80%
DOI: 10.18716/nmrshiftdb2/11224
title: NMR data for trans,trans-()-3'-(4-Methylphenyl)-2-phenylspiro[2H-1-benzothiopyran-3(4H),2'-oxiran]-4-one 1,1-Dioxide
DOI: 10.17632/jsfk4swkxf.1
title: Data for: Soil respiration negatively correlated with volume gains by a young Pinus radiata clone over five months

description: Soil respiration, tree growth and soil moisture data.
DOI: 10.18716/nmrshiftdb2/11220
title: NMR data for 5,12-Dihydro-cycloocta[1,2-b;5,6-b']diindole-6,13-dicarboxylic acid dimethyl ester; Caulerpin
DOI: 10.5281/zenodo.3577202
title: Ancient Conical Sundial Measurements

description: **ConicalSundialsMeasurements.json** describes the dimensions of the corpus and cone of ancient conical sundials with 3D-models which are cataloged in the repository of the Berlin Ancient Sundials Project (http://repository.edition-topoi.org/collection/BSDP). The dimensions were measured from the 3D-models in that collection. Besides, the database lists distances and angles of construction points (i.e., the shortest distance between the cone apex and the day curves). They were relevant to validate the geographical location for which the sundial was crafted, and derived values for the ratio between gnomon size and shadow length (g,s).
DOI: 10.5281/zenodo.3577203
title: Ancient Conical Sundial Measurements

description: **ConicalSundialsMeasurements.json** describes the dimensions of the corpus and cone of ancient conical sundials with 3D-models which are cataloged in the repository of the Berlin Ancient Sundials Project (http://repository.edition-topoi.org/collection/BSDP). The dimensions were measured from the 3D-models in that collection. Besides, the database lists distances and angles of construction points (i.e., the shortest distance between the cone apex and the day curves). They were relevant to validate the geographical location for which the sundial was crafted, and derived values for the ratio between gnomon size and shadow length (g,s).
DOI: 10.7910/dvn/sxkbap/lq0i7v
title: Video 1 ‐ Made with Clipchamp (1) (1).mp4

description: RECONSTRUÇÃO DO MEMBRO INFERIOR COM RETALHO FASCIOCUTÂNEO DE PEDÍCULO DISTAL – TÉCNICA DE MONTEIRO MODIFICADA
DOI: 10.17632/c5k8k595mw.1
title: Data for: Variation of Kukoamine A (KuA) and B (KuB) contents and related eteorological factors for Cortex Lycii Radicis of different areas