cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.kgj2ec
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Senecio georgianus DC."
 ]
}

 The dataset includes 46 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041474-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-251947
title: National youth congress, COGIC (12th: 1946), program

description: [description english/roman)] Program book for the 12th national youth congress, Church of God in Christ, Memphis, Tennessee, 1946 June 27 - July 4.
DOI: 10.15468/dl.ytxjqa
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sphoeroides kendalli Meek & Hildebrand, 1928"
 ]
}

 The dataset includes 25 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052357-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rkmjbz
title: Occurrence Download

description: A dataset containing 11103818 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "Year 1602-2018",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 11103818 records from 1430 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006503-180412121330197/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18710/tynihf/9q0nzo
title: 20230201_HOP_REDTEC_glo.txt
DOI: 10.18710/ugnife/jrwtyr
title: PSGO_03.csv
DOI: 10.18710/ugnife/nvdt9z
title: 00_README.txt
DOI: 10.18710/ugnife/soyixe
title: PSG_14.csv
DOI: 10.18710/ugnife/tqkgv5
title: PSGO_12.csv
DOI: 10.18710/vm0wkq/axlcxn
title: Chromosome 11.csv

description: Contents: Gene (name of gene), 
 Gene ID (Ensemble ID of gene)
 Causal Estimate Least Squares (estimate of the causal effect of the gene on CAD using the least squares estimator), 
 Causal Estimate GMM (estimate of the causal effect of the gene on CAD using the Generalized method of moments estimator),
 se Least Squares/GMM (standard error of the least squares estimator and the GMM estimator, because the functional form is same, the standard error is same for both estimators),
 p-value GMM (p-value of the GMM estimator), 
 p-value Least Squares (p-value of the least squares estimator),
 Causal Estimate MVIVW (estimate of the causal effect of the gene on CAD using multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package), 
 se MVIVW (standard error of the estimate for multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package),
 p-value MVIVW (p-value of the estimate for multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package), 
 Causal effect MR (causal effect using univariate Mendelian Randomization), 
 F_stat (conditional F-statistic calculated using the Mendelian Randomization package), 
 Q_stat (cochran's Q-statistic calculated using the Mendelian Randomization package), 
 Q_pval (p-value of the cochran's Q-statistic calculated using the Mendelian Randomization package), 
 lead SNP (the SNP with the lowest p-value in the GWAS summary data for the given locus, hence the most significant SNP), 
 position (position in base pairs of the lead SNP), 
 chromosome (chromosome of the lead SNP), 
 tissue (name of the tissue from where the expression levels of genes are considered in the MVMR causal analysis), 
 No of SNPs (number of SNPs in the MVMR causal analysis), 
 study (GWAS study ebi-a-GCST003116), 
 determinant of LD matrix (the determinant of the Linkage disequilibrium matrix of the SNPs considered in the MVMR causal analysis), 
 determinant of instruments strength matrix (the determinant of the covariance matrix of the instruments and the genes for the SNPs considered in the MVMR causal analysis), 
 LD threshold (maximum allowed LD between SNPs considered as instruments for the causal MVMR analysis).
 
 Purpose: Collection of all results in the MVMR analysis
DOI: 10.18710/vm0wkq/cerkts
title: Chromosome 6.csv

description: Contents: Gene (name of gene), 
 Gene ID (Ensemble ID of gene)
 Causal Estimate Least Squares (estimate of the causal effect of the gene on CAD using the least squares estimator), 
 Causal Estimate GMM (estimate of the causal effect of the gene on CAD using the Generalized method of moments estimator),
 se Least Squares/GMM (standard error of the least squares estimator and the GMM estimator, because the functional form is same, the standard error is same for both estimators),
 p-value GMM (p-value of the GMM estimator), 
 p-value Least Squares (p-value of the least squares estimator),
 Causal Estimate MVIVW (estimate of the causal effect of the gene on CAD using multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package), 
 se MVIVW (standard error of the estimate for multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package),
 p-value MVIVW (p-value of the estimate for multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package), 
 Causal effect MR (causal effect using univariate Mendelian Randomization), 
 F_stat (conditional F-statistic calculated using the Mendelian Randomization package), 
 Q_stat (cochran's Q-statistic calculated using the Mendelian Randomization package), 
 Q_pval (p-value of the cochran's Q-statistic calculated using the Mendelian Randomization package), 
 lead SNP (the SNP with the lowest p-value in the GWAS summary data for the given locus, hence the most significant SNP), 
 position (position in base pairs of the lead SNP), 
 chromosome (chromosome of the lead SNP), 
 tissue (name of the tissue from where the expression levels of genes are considered in the MVMR causal analysis), 
 No of SNPs (number of SNPs in the MVMR causal analysis), 
 study (GWAS study ebi-a-GCST003116), 
 determinant of LD matrix (the determinant of the Linkage disequilibrium matrix of the SNPs considered in the MVMR causal analysis), 
 determinant of instruments strength matrix (the determinant of the covariance matrix of the instruments and the genes for the SNPs considered in the MVMR causal analysis), 
 LD threshold (maximum allowed LD between SNPs considered as instruments for the causal MVMR analysis).
 
 Purpose: Collection of all results in the MVMR analysis
DOI: 10.18710/vm0wkq/zckhxi
title: 0_ReadMe.txt

description: New updated Readme file
DOI: 10.18710/vzbzeo/elkikg
title: 00_Readme.txt
DOI: 10.18710/wrgqam/a4t6ax
title: 8_DIC_Data_EP270-4.zip

description: DIC data of EP270-4
DOI: 10.18710/bsxna1/yqslvv
title: CAPTIV8_diagnostic_data_csv.zip

description: Diagnostic Metadata in csv format
DOI: 10.18710/dqzkmx/3yvwbl
title: 2-ecoli.csv

description: Accuracy tables for the dataset: Ecoli.

Nakai, K. (1996). Ecoli. UCI Machine Learning Repository. https://doi.org/10.24432/C5388M. Licensed under CC BY 4.0.
DOI: 10.18710/e9ctjs/ptgpom
title: 20230813_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/yad9ns
title: 20230913_BJN_REDOBS_gal.txt
DOI: 10.18710/hmf7ee/agulxc
title: 1A-C_explored-states.csv
DOI: 10.18710/j4wlvi/1foevg
title: 20230105_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/47fzds
title: 20230911_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/8e9blb
title: 20230604_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/bcqumr
title: 20230211_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/bxxy1n
title: 20230620_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/cnayw7
title: 20230426_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/conpwf
title: 20230225_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/czvzm7
title: 20230114_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/els8hj
title: 20230409_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/fivncd
title: 20230206_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/gmwwow
title: 20230329_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/jgvd8x
title: 20230310_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/lateid
title: 20230121_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/lb9ok7
title: 20230216_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/luyty3
title: 20230403_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/mxcmva
title: 20230924_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/nc583y
title: 20230110_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/paioxu
title: 20230109_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/pey9qd
title: 20230215_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/psxvdw
title: 20230707_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/pvafse
title: 20230202_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/rzwj1k
title: 20230325_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/vn5ozj
title: 20230119_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/xgwyf2
title: 20230319_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/xseg0d
title: 20230607_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/yhdgvf
title: 20230305_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/yulpag
title: 20230117_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/zbk8wz
title: 20230223_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/zkidqe
title: 20230203_HOP_REDOBS_glo.txt
DOI: 10.18710/l0vpw7/d3ows5
title: SBL042 b_Subt4__57__14-52-13-951.txt
DOI: 10.18710/l0vpw7/dgizbe
title: SBL096_Subt2__3__10-22-20-892.txt
DOI: 10.18710/l0vpw7/euabsq
title: SBL164_B_Subt4__7__13-45-22-041.txt
DOI: 10.18710/l0vpw7/ipv0bx
title: SBL204_Subt2__16__17-06-30-009.txt
DOI: 10.18710/l0vpw7/o3bpd0
title: SBL103_Subt2__10__10-31-10-897.txt
DOI: 10.18710/l0vpw7/osp6n7
title: SBL128_B_Subt4__12__15-05-12-344.txt
DOI: 10.18710/l0vpw7/v0xr76
title: SBL067_Subt4__10__14-09-55-878.txt
DOI: 10.18710/vm0wkq/vp601t
title: Chromosome 4.csv

description: Contents: Gene (name of gene), 
 Gene ID (Ensemble ID of gene)
 Causal Estimate Least Squares (estimate of the causal effect of the gene on CAD using the least squares estimator), 
 Causal Estimate GMM (estimate of the causal effect of the gene on CAD using the Generalized method of moments estimator),
 se Least Squares/GMM (standard error of the least squares estimator and the GMM estimator, because the functional form is same, the standard error is same for both estimators),
 p-value GMM (p-value of the GMM estimator), 
 p-value Least Squares (p-value of the least squares estimator),
 Causal Estimate MVIVW (estimate of the causal effect of the gene on CAD using multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package), 
 se MVIVW (standard error of the estimate for multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package),
 p-value MVIVW (p-value of the estimate for multivariable Mendelian randomization via the inverse-variance method using the Mendelian Randomization package), 
 Causal effect MR (causal effect using univariate Mendelian Randomization), 
 F_stat (conditional F-statistic calculated using the Mendelian Randomization package), 
 Q_stat (cochran's Q-statistic calculated using the Mendelian Randomization package), 
 Q_pval (p-value of the cochran's Q-statistic calculated using the Mendelian Randomization package), 
 lead SNP (the SNP with the lowest p-value in the GWAS summary data for the given locus, hence the most significant SNP), 
 position (position in base pairs of the lead SNP), 
 chromosome (chromosome of the lead SNP), 
 tissue (name of the tissue from where the expression levels of genes are considered in the MVMR causal analysis), 
 No of SNPs (number of SNPs in the MVMR causal analysis), 
 study (GWAS study ebi-a-GCST003116), 
 determinant of LD matrix (the determinant of the Linkage disequilibrium matrix of the SNPs considered in the MVMR causal analysis), 
 determinant of instruments strength matrix (the determinant of the covariance matrix of the instruments and the genes for the SNPs considered in the MVMR causal analysis), 
 LD threshold (maximum allowed LD between SNPs considered as instruments for the causal MVMR analysis).
 
 Purpose: Collection of all results in the MVMR analysis
DOI: 10.18710/vzbzeo/bneaxl
title: Consent_letter_University_B.pdf
DOI: 10.18710/vzbzeo/hteczg
title: Data_structure_empirical_materials.csv
DOI: 10.18710/1nbww5/5m1vei
title: 00_README.txt
DOI: 10.18710/dqzkmx
title: Replication Data for: Regularized Feature Selection Landscapes: An Empirical Study of Multimodality

description: This dataset contains replication data for the paper Regularized Feature Selection Landscapes: An Empirical Study of Multimodality. It contains accuracy tables of well-known classification datasets from The UCI Machine Learning Repository.
These tables comprise the accuracy for all feature subsets, i.e., all column combinations, obtained by a decision tree classifier, with different levels of regularization.

The decision tree classifier is provided by the BetaML.jl package for the Julia programming language, and uses the default parameters: max_depth=0 (no limits) min_gain=0.0, min_records=2, max_features=0 (consider all features), splitting_criterion=BetaML.Utils.gini, (Gini impurity index), and rng = Random.GLOBAL_RNG (no set seed for random number generation).
description: Julia, 1.9.3
DOI: 10.18710/e9ctjs/68xyrj
title: 20230823_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/7ilt4h
title: 20230728_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/7rhzhu
title: 20230810_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/8r7xp8
title: 20230908_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/a3a1ev
title: 20230912_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/dsmhbb
title: 20230713_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/ewpoaj
title: 20231222_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/hea72o
title: 20230924_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/hx6out
title: 20230913_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/jb4ygg
title: 20230714_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/kqzb7w
title: 20230909_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/p48ejd
title: 20230915_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/qxsdyy
title: 20230717_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/u8bzig
title: 20230930_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/vt9g7c
title: 20230919_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/wycdoz
title: 20230704_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/wziwd3
title: 20230911_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/xof0xz
title: 20230915_BJN_REDOBS_glo.txt
DOI: 10.18710/hmf7ee/dfpmhv
title: 2SN-A-C-02_explored-states.csv
DOI: 10.18710/j4wlvi/3gxyqc
title: 20230812_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/5qw0pq
title: 20230616_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/ans9ny
title: 20230921_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/brbi0u
title: 20230131_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/btzrdk
title: 20230903_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/cesxw1
title: 20230823_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/geqvvi
title: 20230206_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/h5kpmm
title: 20230828_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/jq3ogk
title: 20230816_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/jxei6z
title: 20230331_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/kn41m8
title: 20230717_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/ld9z0v
title: 20230424_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/lgq4sl
title: 20230316_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/n6b5be
title: 20230805_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/rst2r5
title: 20230217_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/tbukuu
title: 20230914_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/v8zoiu
title: 20230903_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/whyu9c
title: 20230311_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/xtobvd
title: 20230328_HOP_REDOBS_gps.txt
DOI: 10.18710/l0vpw7
title: Zooplankton spectral bioluminescence database

description: The dataset consists of spectral bioluminescence measurements of various zooplankton taxa collected from Mid-Norway and Svalbard. It is collected as a part of the RCN project 315728 "Light as a Cue for Life in the Arctic and Northern Seas (LightLife)". The dataset is based on the measurements for the MSc thesis of Hedda Førde "Glow and Know – Evaluating the Potential of Spectral Bioluminescence as a Tool for Zooplankton Identification in situ" (NTNU, 2024) and contains raw data, analyzing code as well as extracted parameters.
DOI: 10.18710/l0vpw7/3i8upp
title: SBL126_Subt2__70__14-33-41-668.txt
DOI: 10.18710/l0vpw7/7oyd7n
title: SBL104_Subt2__9__18-12-56-785.txt
DOI: 10.18710/l0vpw7/bpi0u1
title: SBL121_Subt2__8__18-32-00-794.txt
DOI: 10.18710/l0vpw7/cokadx
title: SBL093_Subt2__12__10-17-58-889.txt
DOI: 10.18710/l0vpw7/hu0jsj
title: SBL234_A_Subt2__3__10-31-23-255.txt
DOI: 10.18710/l0vpw7/izpcpj
title: SBL272_C_Subt2__2__21-21-01-648.txt
DOI: 10.18710/l0vpw7/lva4lo
title: SBL172_B_Subt2__1__15-30-45-604.txt
DOI: 10.18710/l0vpw7/lznaoq
title: SBL143_B_Subt8__10__17-26-20-489.txt
DOI: 10.18710/l0vpw7/tclmcr
title: SBL248_A_Subt2__15__16-19-12-390.txt
DOI: 10.18710/l0vpw7/wsdbyb
title: SBL115_Subt2__9__18-25-54-790.txt
DOI: 10.18710/l0vpw7/xdvnbw
title: SBL052_Subt4__13__17-12-10-093.txt
DOI: 10.18710/r5jlfr
title: Replication Data for: On the role of ecological validity in language and speech research

description: &lt;p&gt;&lt;b&gt;Dataset abstract&lt;/b&gt;&lt;/p&gt;
&lt;p&gt;This dataset contains the results from 40 language and speech researchers, who completed a survey. In the first part of the survey, respondents were asked to complete a demographic (e.g., age, gender, first language) and professional background questionnaire (e.g., current academic position, research interests). In addition, they were asked several open-ended questions about their familiarity with and understanding of the term ‘ecological validity’ (e.g., which words come to mind when you hear this term, how to measure the ecological validity of a study, how does ecological validity apply to your area of research). In the second part of the survey, respondents were presented with 24 short speech excerpts, representing 12 different stimulus types. They were asked to rate each speech excerpt on its degree of casualness (i.e. spontaneity) and naturalness, and how likely they are to encounter each excerpt in everyday listening situations.&lt;/p&gt;
description: &lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;Article abstract&lt;/b&gt;&lt;/p&gt;
&lt;p&gt;This paper explores how researchers in the field of language and speech sciences understand and apply the concept of ecological validity. It also assesses the ecological validity of various stimulus materials, ranging from isolated word productions to sentences taken from authentic interviews. Forty researchers participated in a survey, which contained (i) a demographic and professional background questionnaire with open-ended questions about the definition, feasibility and desirability of ecological validity, and (ii) a speech rating task. In the rating task, respondents evaluated 24 speech excerpts, representing 12 types of stimulus materials, on their casualness, naturalness, and likelihood of occurrence in real-life contexts. The results showed that while most researchers acknowledge the importance of ecological validity, defining the necessary and sufficient criteria for evaluating or achieving it remains challenging. Regarding stimulus types, unscripted sentences from interviews and Map Task dialogues were rated as the most casual and natural. In contrast, carefully read sentences and digitally modified stimuli were viewed as the least casual and natural, although individual differences in rating were noticeable. Similarly, ratings for the likelihood of occurrence in everyday listening situations were highest for various types of extemporaneous speech. The survey responses not only enhance our theoretical understanding of ecological validity, but also raise awareness about the implications of methodological choices, such as the selection of tasks and stimulus materials, on the ecological validity of a study.
&lt;/p&gt;
description: R Studio, 2024.04.1+748
description: R, 4.4.0
description: MS Excel, Version 16.86
description: Praat, 6.3.14
description: Qualtrics
DOI: 10.18710/w5b9qd/rso7p2
title: Experimental_protocol.txt

description: File containing the experimental protocol
DOI: 10.18710/e9ctjs/ftasro
title: 20230707_BJN_REDOBS_gps.txt
DOI: 10.18710/hmf7ee/o2pjr5
title: 2SN-B_scenarios-found.csv
DOI: 10.18710/j4wlvi/a8z3aj
title: 20230507_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/ktunfz
title: 20230611_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/twztcz
title: 20230528_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/uthprk
title: 20230517_HOP_REDOBS_glo.txt
DOI: 10.18710/l0vpw7/2svtgf
title: SBL273_B_Subt2__4__21-26-59-650.txt
DOI: 10.18710/l0vpw7/bw31oq
title: SBL184_A_Subt2__18__13-48-52-469.txt
DOI: 10.18710/l0vpw7/jtgvru
title: QEPro_data_processing_V1_3.txt

description: Matlab code used to process the data as a text file.
DOI: 10.18710/l0vpw7/mqvmeg
title: SBL073_Subt4__21__14-30-33-888.txt
DOI: 10.18710/mtuavq/38dmlr
title: 20230731_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/8ppf5d
title: 20230904_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/agazij
title: 20230723_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/cbgu0s
title: 20230622_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/doh49c
title: 20230712_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/hxe7br
title: 20230704_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/kcjd3s
title: 20230804_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/mfmo6k
title: 20230520_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/ryfrgj
title: 20230519_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/v2jyrf
title: 20230506_BJN_REDTEC_gal.txt
DOI: 10.18710/pwuqbp/u9bv5a
title: 00_README.txt
DOI: 10.18710/ivsil3/0wxb3r
title: 20230329_KHO_REDOBS_gal.txt
DOI: 10.18710/ivsil3/mh8vzr
title: 20230310_KHO_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/uashch
title: 20231114_HOP_REDOBS_glo.txt
DOI: 10.18710/mtuavq/2hkso5
title: 20230918_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/3httsr
title: 20230714_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/4ecrpv
title: 20230719_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/6ah25u
title: 20230926_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/ahod6n
title: 20230721_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/facm5n
title: 20230903_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/i9mldh
title: 20230902_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/jquf6h
title: 20230810_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/l2lxxr
title: 20230802_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/ljzyax
title: 20230929_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/nj02sh
title: 20230801_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/sj9jaf
title: 20230925_BJN_REDTEC_gps.txt
DOI: 10.18710/mtuavq/skxkdb
title: 20230921_BJN_REDTEC_gal.txt
DOI: 10.18710/mtuavq/uqnhia
title: 20230725_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/wexact
title: 20230703_BJN_REDTEC_glo.txt
DOI: 10.18710/mtuavq/wwbowj
title: 20230829_BJN_REDTEC_gps.txt
DOI: 10.18710/tynihf/ht6okj
title: 20230316_HOP_REDTEC_gps.txt
DOI: 10.18710/ivsil3/1e63q4
title: 20230208_KHO_REDOBS_glo.txt
DOI: 10.18710/ivsil3/5m3jcl
title: 20230328_KHO_REDOBS_glo.txt
DOI: 10.18710/ivsil3/76ioct
title: 20230217_KHO_REDOBS_gps.txt
DOI: 10.18710/ivsil3/9myrg2
title: 20230318_KHO_REDOBS_gal.txt
DOI: 10.18710/ivsil3/nx0eyq
title: 20230224_KHO_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/04vp7z
title: 20231124_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/nq0grm
title: 20231227_HOP_REDOBS_gal.txt
DOI: 10.18710/j4wlvi/ssr8cv
title: 20231119_HOP_REDOBS_glo.txt
DOI: 10.18710/j4wlvi/t7miis
title: 20231212_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/w0urex
title: 20231106_HOP_REDOBS_gps.txt
DOI: 10.18710/j4wlvi/xjc3su
title: 20231101_HOP_REDOBS_gal.txt
DOI: 10.18710/l0vpw7/6byfuo
title: SBL273_A_Subt2__3__21-24-57-650.txt
DOI: 10.18710/e9ctjs/7nizs5
title: 20230906_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/7wmvoh
title: 20231108_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/9orhfp
title: 20230909_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/ao3qvu
title: 20230728_BJN_REDOBS_gps.txt
DOI: 10.18710/e9ctjs/ej6azn
title: 20230714_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/qqepoz
title: 20231026_BJN_REDOBS_gal.txt
DOI: 10.18710/e9ctjs/qqpfhm
title: 20231113_BJN_REDOBS_glo.txt
DOI: 10.18710/e9ctjs/rwruv3
title: 20230822_BJN_REDOBS_gps.txt