cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3608883
title: Investigating the Effects of Embodiment on Emotional Categorization of Faces and Words in Children and Adults

description: The three data files uploaded here contain the data used for the analyses in experiments 1a, 1b, and 2 as described in the article carrying the same title as this dataset, published in the journal Frontiers in Psychology. All analyses were carried out in SPSS version 22 as described in the published article. Article Abstract: The facial feedback hypothesis (FFH) indicates that besides being involved in the production of facial expressions, the musculature of the face also influences one’s perception of emotional stimuli. Recently, this effect has been the focus of increased scrutiny as efforts to replicate a key study with adult participants supporting this hypothesis, using the so-called “pen-in-the-mouth” task, have not been successful at several labs. Our series of experiments attempted to investigate whether the assumed embodiment effect can be reproduced in a simplified emotional categorization task for emotional faces and words. We also wanted to test whether the embodiment effect can be detected in children because it is assumed that their bodily processes are especially closely linked with their sensory and cognitive processes. Our experiments involved child and adult participants categorizing faces and words as positive or negative as quickly as possible, while inducing a positive or negative facial or bodily state (holding a straw in the mouth such that a smile or a frown was generated, or creating a positive or negative body posture). The positive or negative facial and bodily states could therefore be either congruent or incongruent with the valence of the target face and word stimuli. Our results did not show any significant differences between the congruent and incongruent conditions in either children or adults. This suggests that embodiment effects either do not significantly impact valence-based categorization or are not strong enough to be detected by our approach considering the sample size in the present study.
DOI: 10.5281/zenodo.3608884
title: Investigating the Effects of Embodiment on Emotional Categorization of Faces and Words in Children and Adults

description: The three data files uploaded here contain the data used for the analyses in experiments 1a, 1b, and 2 as described in the article carrying the same title as this dataset, published in the journal Frontiers in Psychology. All analyses were carried out in SPSS version 22 as described in the published article. Article Abstract: The facial feedback hypothesis (FFH) indicates that besides being involved in the production of facial expressions, the musculature of the face also influences one’s perception of emotional stimuli. Recently, this effect has been the focus of increased scrutiny as efforts to replicate a key study with adult participants supporting this hypothesis, using the so-called “pen-in-the-mouth” task, have not been successful at several labs. Our series of experiments attempted to investigate whether the assumed embodiment effect can be reproduced in a simplified emotional categorization task for emotional faces and words. We also wanted to test whether the embodiment effect can be detected in children because it is assumed that their bodily processes are especially closely linked with their sensory and cognitive processes. Our experiments involved child and adult participants categorizing faces and words as positive or negative as quickly as possible, while inducing a positive or negative facial or bodily state (holding a straw in the mouth such that a smile or a frown was generated, or creating a positive or negative body posture). The positive or negative facial and bodily states could therefore be either congruent or incongruent with the valence of the target face and word stimuli. Our results did not show any significant differences between the congruent and incongruent conditions in either children or adults. This suggests that embodiment effects either do not significantly impact valence-based categorization or are not strong enough to be detected by our approach considering the sample size in the present study.
DOI: 10.7910/dvn/gf65e4/bab7u2
title: Unprogrammed Abandonment.R

description: :unav
DOI: 10.7910/dvn/gf65e4/du34cz
title: ReadMe.txt

description: :unav
DOI: 10.7910/dvn/gf65e4/h6o2vk
title: Figure 4.R

description: :unav
DOI: 10.7910/dvn/gf65e4/lfd09o
title: fgmc.tab

description: :unav
DOI: 10.7910/dvn/gf65e4/nltd84
title: DHSProcess.R

description: :unav
DOI: 10.7910/dvn/gf65e4/ozmlrn
title: Figures 1 and 3.R

description: :unav
DOI: 10.7910/dvn/gf65e4/rnir4x
title: FGMC Laws.tab

description: :unav
DOI: 10.7910/dvn/gf65e4/0gek4f
title: fgmcountry.tab

description: :unav
DOI: 10.7910/dvn/gf65e4/5k284k
title: MICSProcess.R

description: :unav
DOI: 10.7910/dvn/gf65e4/kstc4a
title: fgmc-1.tab

description: :unav
DOI: 10.25345/c5cq4p
title: MassIVE MSV000084810 - GNPS Projet RESSAC mode negatif
title: Projet RESSAC : molecular networking about a bactria strain,Toulon , FRANCE
DOI: 10.5281/zenodo.3609353
title: 2020PNAS-Movies

description: TEM images of the Movie S1-S8.
DOI: 10.7910/dvn/zlsdwp/avlnz5
title: Figure 1B.dat

description: :unav
DOI: 10.5281/zenodo.3609354
title: 2020PNAS-Movies

description: TEM images of the Movie S1-S8.
DOI: 10.7910/dvn/7nm5bs/ghrips
title: بگفیاو.docx

description: :unav
DOI: 10.18430/m36v8w
title: X-ray diffraction data for the cDYL2 chromodomain in complex with a synthetic peptide (6V8W)
DOI: 10.7910/dvn/xeelvr/km0oak
title: Governance 2020_Social Policy Responsiveness_YPeters DO-file.do

description: Do-file
DOI: 10.7910/dvn/xeelvr/of3frc
title: Governance 2020_Social Policy Responsiveness_YPeters data.tab

description: Data
DOI: 10.14272/cuvcciosrgclax-uhfffaoysa-n.2
title: 2,3,4,6-tetra(carbazol-9-yl)-5-[5-(chloromethyl)-1,3,4-oxadiazol-2-yl]benzonitrile
title: InChI=1S/C58H34ClN7O/c59-33-52-61-62-58(67-52)53-54(63-44-25-9-1-17-35(44)36-18-2-10-26-45(36)63)43(34-60)55(64-46-27-11-3-19-37(46)38-20-4-12-28-47(38)64)57(66-50-31-15-7-23-41(50)42-24-8-16-32-51(42)66)56(53)65-48-29-13-5-21-39(48)40-22-6-14-30-49(40)65/h1-32H,33H2

description: This dataset contains experimental data from a sample (2,3,4,6-tetra(carbazol-9-yl)-5-[5-(chloromethyl)-1,3,4-oxadiazol-2-yl]benzonitrile, InChI=1S/C58H34ClN7O/c59-33-52-61-62-58(67-52)53-54(63-44-25-9-1-17-35(44)36-18-2-10-26-45(36)63)43(34-60)55(64-46-27-11-3-19-37(46)38-20-4-12-28-47(38)64)57(66-50-31-15-7-23-41(50)42-24-8-16-32-51(42)66)56(53)65-48-29-13-5-21-39(48)40-22-6-14-30-49(40)65/h1-32H,33H2). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.14272/cuvcciosrgclax-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (2,3,4,6-tetra(carbazol-9-yl)-5-[5-(chloromethyl)-1,3,4-oxadiazol-2-yl]benzonitrile)
title: InChI=1S/C58H34ClN7O/c59-33-52-61-62-58(67-52)53-54(63-44-25-9-1-17-35(44)36-18-2-10-26-45(36)63)43(34-60)55(64-46-27-11-3-19-37(46)38-20-4-12-28-47(38)64)57(66-50-31-15-7-23-41(50)42-24-8-16-32-51(42)66)56(53)65-48-29-13-5-21-39(48)40-22-6-14-30-49(40)65/h1-32H,33H2

description: dataset for mass spectrometry (MS) HRMS (FAB-MS, 3-NBA, C58H3435ClN7O) Calcd 880.2592 [M+H]+; Found 880.2590 [M+H]+.
DOI: 10.7910/dvn/n8f1fc/tafipd
title: Heirloom rice consumer study dataset (2015).tab

description: Raw data in SPSS
DOI: 10.7910/dvn/n8f1fc/uklkyk
title: Heirloom Questionnaire (2015).pdf

description: Survey questionnaire in English and local language
DOI: 10.14272/cuvcciosrgclax-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (2,3,4,6-tetra(carbazol-9-yl)-5-[5-(chloromethyl)-1,3,4-oxadiazol-2-yl]benzonitrile)
title: InChI=1S/C58H34ClN7O/c59-33-52-61-62-58(67-52)53-54(63-44-25-9-1-17-35(44)36-18-2-10-26-45(36)63)43(34-60)55(64-46-27-11-3-19-37(46)38-20-4-12-28-47(38)64)57(66-50-31-15-7-23-41(50)42-24-8-16-32-51(42)66)56(53)65-48-29-13-5-21-39(48)40-22-6-14-30-49(40)65/h1-32H,33H2

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Chloroform-d, ppm) δ = 8.11 (dt, J = 7.8, 0.8 Hz, 2H), 7.73–7.66 (m, 2H), 7.66–7.60 (m, 2H), 7.52 (ddd, J = 8.3, 7.2, 1.2 Hz, 2H), 7.43 (d, J = 8.1 Hz, 2H), 7.40–7.32 (m, 4H), 7.23–7.17 (m, 2H), 7.16–7.09 (m, 2H), 7.09–7.02 (m, 4H), 7.02–6.90 (m, 6H), 6.82 (td, J = 7.5, 0.9 Hz, 2H), 6.66 (ddd, J = 8.4, 7.2, 1.2 Hz, 2H), 3.77 (s, 2H).
DOI: 10.14272/cuvcciosrgclax-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (2,3,4,6-tetra(carbazol-9-yl)-5-[5-(chloromethyl)-1,3,4-oxadiazol-2-yl]benzonitrile)
title: InChI=1S/C58H34ClN7O/c59-33-52-61-62-58(67-52)53-54(63-44-25-9-1-17-35(44)36-18-2-10-26-45(36)63)43(34-60)55(64-46-27-11-3-19-37(46)38-20-4-12-28-47(38)64)57(66-50-31-15-7-23-41(50)42-24-8-16-32-51(42)66)56(53)65-48-29-13-5-21-39(48)40-22-6-14-30-49(40)65/h1-32H,33H2

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, CDCl3, ppm) δ = 162.4, 158.4, 144.2, 143.2, 142.5, 141.0 (2C), 138.9 (2C), 138.5 (2C), 137.6 (2C), 136.7, 127.6, 126.9 (2C), 125.9 (2C), 125.7 (2C), 124.8 (2C), 124.5 (2C), 124.5 (2C), 124.2 (2C), 123.9 (2C), 121.8 (2C), 121.7 (2C), 121.4 (2C), 121.0 (2C), 120.9 (2C), 120.4 (2C), 120.3 (2C), 119.7 (2C), 117.6, 112.1, 110.2 (2C), 109.9 (2C), 109.5 (2C), 109.5 (2C), 31.7.
DOI: 10.14272/cuvcciosrgclax-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (2,3,4,6-tetra(carbazol-9-yl)-5-[5-(chloromethyl)-1,3,4-oxadiazol-2-yl]benzonitrile)
title: InChI=1S/C58H34ClN7O/c59-33-52-61-62-58(67-52)53-54(63-44-25-9-1-17-35(44)36-18-2-10-26-45(36)63)43(34-60)55(64-46-27-11-3-19-37(46)38-20-4-12-28-47(38)64)57(66-50-31-15-7-23-41(50)42-24-8-16-32-51(42)66)56(53)65-48-29-13-5-21-39(48)40-22-6-14-30-49(40)65/h1-32H,33H2

description: dataset for infrared absorption spectroscopy (IR) IR (ATR) ṽ [cm-1] = 1597 (vw), 1544 (vw), 1490 (w), 1478 (w), 1443 (m), 1333 (w), 1309(w), 1292 (w), 1221 (w), 1175 (w), 1149 (w), 1119 (w), 1027 (w), 984 (w), 929 (vw), 741(m), 720 (m), 617 (w), 595 (w), 548 (w), 527 (w), 486 (vw), 426 (vw), 422 (w).
DOI: 10.21227/bf3v-5m89
title: Robot_calibration_experiment_data

description: The data is used to identify the deviation of kinematic parameters,train GPR model,record the geometric error and non-geometric error compensation result.
DOI: 10.21227/yxdw-jp45
title: Robot_calibration_experiment_data

description: The data is used to identify the deviation of kinematic parameters,train GPR model,record the geometric error and non-geometric error compensation result.
DOI: 10.5281/zenodo.3609284
title: MSR Licenses

description: For the purposes of replicating the license study on free software JavaScript ecosystem projects, all results generated in the study, including the script code used to perform the experiments and data analysis, as well as the data generated by the tool used are available here. .
DOI: 10.5281/zenodo.3609502
title: Elucidation of the Atroposelectivity in the Synthesis of Axially Chiral Thiohydantoin Derivatives

description: Recently, Sarigul and Dogan have synthesized a number of enantiomerically enriched axially chiral atropoisomeric 2-thiohydantoins by the reaction of L-amino acid ester salts and <em>o</em>-aryl isothiocyanates in the presence of triethyl amine (TEA) in dichloromethane. The nonaxially chiral derivative 5-methyl-3-phenyl-2-thiohydantoin gave a racemic product whereas the axially chiral 5-methyl-3-<em>o</em>-bromophenyl-2-thiohydantoin was less prone to racemize at C<sub>5</sub> of the heterocyclic ring. In this study, we present a computational study (M06-2X/6-311+G(d,p) for C, H, O, N and S; M06-2X/6-311++G(3df,3pd) for Br) in order to propose plausible mechanisms for the racemization and cyclization steps for 2-thiohydantoin derivatives. The study includes rationalization based on steric as well as the electrostatic effects to elucidate the epimerization differences at C<sub>5</sub>.
DOI: 10.5281/zenodo.3609503
title: Elucidation of the Atroposelectivity in the Synthesis of Axially Chiral Thiohydantoin Derivatives

description: Recently, Sarigul and Dogan have synthesized a number of enantiomerically enriched axially chiral atropoisomeric 2-thiohydantoins by the reaction of L-amino acid ester salts and <em>o</em>-aryl isothiocyanates in the presence of triethyl amine (TEA) in dichloromethane. The nonaxially chiral derivative 5-methyl-3-phenyl-2-thiohydantoin gave a racemic product whereas the axially chiral 5-methyl-3-<em>o</em>-bromophenyl-2-thiohydantoin was less prone to racemize at C<sub>5</sub> of the heterocyclic ring. In this study, we present a computational study (M06-2X/6-311+G(d,p) for C, H, O, N and S; M06-2X/6-311++G(3df,3pd) for Br) in order to propose plausible mechanisms for the racemization and cyclization steps for 2-thiohydantoin derivatives. The study includes rationalization based on steric as well as the electrostatic effects to elucidate the epimerization differences at C<sub>5</sub>.
DOI: 10.5281/zenodo.3608952
title: Breast Cancer

description: Breast cancer dataset
DOI: 10.5281/zenodo.3608953
title: Breast Cancer

description: Breast cancer dataset
DOI: 10.18112/openneuro.ds002397.v1.0.0
title: test dataset
DOI: 10.5281/zenodo.3609223
title: PLAsTiCC rolling mix 10 yr Models

description: PLAsTiCC models simulated with LSST rolling mix 10yrs opsim cadence
DOI: 10.5281/zenodo.3609224
title: PLAsTiCC rolling mix 10 yr Models

description: PLAsTiCC models simulated with LSST rolling mix 10yrs opsim cadence
DOI: 10.5281/zenodo.3609288
title: Neutron diffraction data for 5M aqueous imidazole solution

description: The zipped file has the raw neutron diffraction data and processed (Gudrun) data for 5M aqueous imidazole solutions as well as the data files generated from the emperical potential structure refinement (EPSR) simulation
DOI: 10.7910/dvn/8bnxes/8lb5pj
title: BigFourFashionWeeks-InstagramPosts-2018.tab

description: Instagram Posts. Schema: ['Post ID', 'Likes Count', 'Time_In', 'Time_Other', 'Time_None', 'Comments Count', 'Caption Length', 'Hashtags Count', 'Event_Milan', 'Event_Paris', 'Event_London', 'Event_NewYork', 'Brand_Gucci', 'Brand_Chanel', 'Brand_Dior', 'Brand_Fendi', 'Brand_Burberry', 'Brand_D&G', 'Brand_Balenciaga', 'Brand_Versace', 'Brand_Prada', 'Brand_Louisvuitton', 'Brand_Tommy', 'Brand_Nike', 'Brand_Valentino', 'Brand_Adidas', 'Brand_Zara', 'Brand_CalvinKlein', 'Brand_VictoriaSecret', 'Brand_Miumiu', 'Brand_Bvlgari', 'Brand_H&M', 'Brand_Armani', 'User's PK']
DOI: 10.7910/dvn/8bnxes/qmcl1m
title: BigFourFashionWeeks-InstagramUsers-2018.tab

description: Instagram Users. ['User's PK', 'Event Posts Count', 'Event Geolocated Percent', 'Event Likes Sum', 'Event Likes Avg', 'Event Likes Med', 'Event Highest Like', 'Event Comment Sum', 'Event Comment Avg', 'Event Comment Med', 'Event Highest Comment']
DOI: 10.7910/dvn/8bnxes/rbjx4o
title: Dataset Description.pdf

description: :unav
DOI: 10.3886/e109142
title: Varieties of Irreligion: Stereotypes, intergroup biases and characteristics of members of nonreligious groups

description: In the present research we focus on different categories of nonreligious individuals, including Atheists, Agnostics and a group we term “Unaffiliated Believers,” (UBs) and compare members of these categories with Christians, as well as each other. We examine (1) how Christian, Atheist, Agnostic, and UB participants rate potential Atheist, Agnostic, and Christian roommates on trustworthiness, competence, warmth, and likeability; (2) how Christian, Atheist, Agnostic, and UB participants rank potential Atheist, Agnostic, and Christian roommates (1st, 2nd, 3rd choice); (3) the streotype content Atheist, Agnostic, Christian, and UB participants attribute to Atheists, Agnostics, and Christians; (4) the (non)religious diversity of Atheist, Agnostic, Christian, and UB participant family and friendship networks; and (5) Atheist, Agnostic, Christian, and UB participant scores on measures assessing personal need for structure (PNS), right-wing authoritarianism (RWA), and social dominance orientation (SDO).
DOI: 10.3886/e109142v1-21841
title: Varieties of Irreligion: Stereotypes, intergroup biases and characteristics of members of nonreligious groups

description: In the present research we focus on different categories of nonreligious individuals, including Atheists, Agnostics and a group we term “Unaffiliated Believers,” (UBs) and compare members of these categories with Christians, as well as each other. We examine (1) how Christian, Atheist, Agnostic, and UB participants rate potential Atheist, Agnostic, and Christian roommates on trustworthiness, competence, warmth, and likeability; (2) how Christian, Atheist, Agnostic, and UB participants rank potential Atheist, Agnostic, and Christian roommates (1st, 2nd, 3rd choice); (3) the streotype content Atheist, Agnostic, Christian, and UB participants attribute to Atheists, Agnostics, and Christians; (4) the (non)religious diversity of Atheist, Agnostic, Christian, and UB participant family and friendship networks; and (5) Atheist, Agnostic, Christian, and UB participant scores on measures assessing personal need for structure (PNS), right-wing authoritarianism (RWA), and social dominance orientation (SDO).
DOI: 10.5281/zenodo.3607236
title: data_jgr

description: The data are the <em>in situ</em> observations data and the LICOM eddy-related products used in the study.
DOI: 10.5281/zenodo.3607237
title: data_jgr

description: The data are the <em>in situ</em> observations data and the LICOM eddy-related products used in the study.
DOI: 10.5281/zenodo.3609276
title: Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance inTwitter Conversations

description: Automated ways to extract stance (denying vs. supporting opinions) from conversations on social media are essential to advance opinion mining research. Recently, there is a renewed excitement in the field as we see new models attempting to improve the state-of-the-art. However, for training and evaluating the models, the datasets used are often small. Additionally, these small datasets have uneven class distributions, i.e., only a tiny fraction of the examples in the dataset have favoring and denying stance, and most other examples have no clear stance. Moreover, the existing datasets do not distinguish between the different types of conversations on social media (e.g., replying vs. quoting on Twitter). Because of this, models trained on one event do not generalize to other events. In the presented work, we create a new dataset by labeling stance in responses to posts on Twitter (both replies and quotes) on controversial issues. To the best of our knowledge, this is currently the largest human-labeled stance dataset for Twitter conversations with over 5200 stance labels. More importantly, we designed a tweet collection methodology that favours the selection of denial-type responses. This class is expected to be more useful in the identification of rumours and determining antagonistic relationships between users.
DOI: 10.5281/zenodo.3609277
title: Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance inTwitter Conversations

description: Automated ways to extract stance (denying vs. supporting opinions) from conversations on social media are essential to advance opinion mining research. Recently, there is a renewed excitement in the field as we see new models attempting to improve the state-of-the-art. However, for training and evaluating the models, the datasets used are often small. Additionally, these small datasets have uneven class distributions, i.e., only a tiny fraction of the examples in the dataset have favoring and denying stance, and most other examples have no clear stance. Moreover, the existing datasets do not distinguish between the different types of conversations on social media (e.g., replying vs. quoting on Twitter). Because of this, models trained on one event do not generalize to other events. In the presented work, we create a new dataset by labeling stance in responses to posts on Twitter (both replies and quotes) on controversial issues. To the best of our knowledge, this is currently the largest human-labeled stance dataset for Twitter conversations with over 5200 stance labels. More importantly, we designed a tweet collection methodology that favours the selection of denial-type responses. This class is expected to be more useful in the identification of rumours and determining antagonistic relationships between users.
DOI: 10.5281/zenodo.3609287
title: Neutron diffraction data for 5M aqueous imidazole solution

description: The zipped file has the raw neutron diffraction data and processed (Gudrun) data for 5M aqueous imidazole solutions as well as the data files generated from the emperical potential structure refinement (EPSR) simulation
DOI: 10.7910/dvn/kp4xrp/b6cqrf
title: tweets_stance_sentiment_1outof4.csv

description: :unav
DOI: 10.7910/dvn/kp4xrp/oc0yup
title: tweets_stance_sentiment_4outof4.csv

description: :unav
DOI: 10.7910/dvn/kp4xrp/ziypdw
title: users_stance_sentiment_botscore_tweetcounts.tab

description: :unav
DOI: 10.7910/dvn/kp4xrp/0haazc
title: tweets_stance_sentiment_3outof4.csv

description: :unav
DOI: 10.7910/dvn/kp4xrp/hwqhsy
title: tweets_stance_sentiment_2outof4.csv

description: :unav
DOI: 10.7910/dvn/kp4xrp/nnxc0a
title: Dataset Description - Brexit.docx

description: :unav
DOI: 10.14469/hpc/6710
title: Benzene 1A1g CCSD(T)/Def2-TZVPP Kekule B2u mode = 1328 cm-1

description: Gaussian Calculation
DOI: 10.7910/dvn/dxyccf/wqzg2m
title: Data JBPA Ingrams et al. 2019.tab

description: :unav
DOI: 10.5281/zenodo.3609246
title: icwsm20d-sub1095-i5

description: Dataset paper under revision - ICWSM 2020 (https://www.icwsm.org/2020/index.html). Available access only for conference' reviews.
DOI: 10.5281/zenodo.3609419
title: Data for the paper "New Vary-Chap scale height profile retrieved from COSMIC radio occultation data"

description: This dataset works specifically for the paper Wu et al., (2020), New Vary-Chap scale height profile retrieved from COSMIC radio occultation data: Fig1_data.txt: Data for Figure 1 Fig2_data.txt: Data for Figure 2 Fig3_data.zip: Data for Figure 3 Fig4_5_10_11_13_14_15_16_data.zip: Data for Figure 4, Figure 5, Figure 10, Figure 11, Figure 13, Figure 14, Figure 15, Figure 16. This dataset includes many columns to provide the materials for above figures. Fig6_data.zip: Data for Figure 6 Fig7_data.zip: Data for Figure 7 Fig8_data.zip: Data for Figure 8 Fig9_data.zip: Data for Figure 9 Fig12_data.zip: Data for Figure 12
DOI: 10.5281/zenodo.3609420
title: Data for the paper "New Vary-Chap scale height profile retrieved from COSMIC radio occultation data"

description: This dataset works specifically for the paper Wu et al., (2020), New Vary-Chap scale height profile retrieved from COSMIC radio occultation data: Fig1_data.txt: Data for Figure 1 Fig2_data.txt: Data for Figure 2 Fig3_data.zip: Data for Figure 3 Fig4_5_10_11_13_14_15_16_data.zip: Data for Figure 4, Figure 5, Figure 10, Figure 11, Figure 13, Figure 14, Figure 15, Figure 16. This dataset includes many columns to provide the materials for above figures. Fig6_data.zip: Data for Figure 6 Fig7_data.zip: Data for Figure 7 Fig8_data.zip: Data for Figure 8 Fig9_data.zip: Data for Figure 9 Fig12_data.zip: Data for Figure 12
DOI: 10.18112/openneuro.ds002398.v1.0.0
title: test dataset