cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4091490
title: Topology files used in simulations

description: Here are the two force filed files used during MD simulations.<br>Both molecules (progesterone and asoprinsnil) were calculated using Gaussian09 with HF/6-31G*. After that, RESP was performed to obtain atomic charges. And the bonded and non-bonded parameters were derived from AMBER Gaff force field.Please cite: Zheng L, Lin VC, Mu Y (2016) Exploring Flexibility of Progesterone Receptor Ligand Binding Domain Using Molecular Dynamics. PLoS ONE 11(11): e0165824. doi:10.1371/journal.pone.0165824
DOI: 10.6084/m9.figshare.4091502.v1
title: Probing the interaction of thionine with human serum albumin by multispectroscopic studies and its <i>in vitro</i> cytotoxic activity toward MCF-7 breast cancer cells

description: The studies on protein–dye interactions are important in biological process and it is regarded as vital step in rational drug design. The interaction of thionine (TH) with human serum albumin (HSA) was analyzed using isothermal titration calorimetry (ITC), spectroscopic, and molecular docking technique. The emission spectral titration of HSA with TH revealed the formation of HSA–TH complex <i>via</i> static quenching process. The results obtained from absorption, synchronous emission, circular dichroism, and three-dimensional (3D) emission spectral studies demonstrated that TH induces changes in the microenvironment and secondary structure of HSA. Results from ITC experiments suggested that the binding of TH dye was favored by negative enthalpy and a favorable entropy contribution. Site marker competitive binding experiments revealed that the binding site of TH was located in subdomain IIA (Sudlow site I) of HSA. Molecular docking study further substantiates that TH binds to the hydrophobic cavity of subdomain IIA (Sudlow site I) of HSA. Further, we have studied the cytotoxic activity of TH and TH–HSA complex on breast cancer cell lines (MCF-7) by MTT assay and LDH assay. These studies revealed that TH–HSA complex showed the higher level of cytotoxicity in cancer cells than TH dye-treated MCF-7 cells and the significant adverse effect did not found in the normal HBL-100 cells. Fluorescence microscopy analyses of nuclear fragmentation studies validate the significant reduction of viability of TH–HSA-treated human MCF-7 breast cancer cells through activation of apoptotic-mediated pathways.
DOI: 10.7910/dvn/v1rjpf/up1k6o
title: cbb20170809_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lgnjkx
title: cbb20170414_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4091490.v1
title: Topology files used in simulations

description: Here are the two force filed files used during MD simulations.<br>Both molecules (progesterone and asoprinsnil) were calculated using Gaussian09 with HF/6-31G*. After that, RESP was performed to obtain atomic charges. And the bonded and non-bonded parameters were derived from AMBER Gaff force field.Please cite: Zheng Liangzhen, et al. Exploring flexibility of Progesterone receptor ligand binding domain using molecular dynamics. Plos One, 2016.
DOI: 10.7910/dvn/v1rjpf/bjazu2
title: cbb20171213_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4091325
title: PowerPoint Slides for: Dietary Magnesium and Kidney Function Decline: The Healthy Aging in Neighborhoods of Diversity across the Life Span Study

description: <b><i>Background:</i></b> Prior studies suggest that certain aspects of the diet related to magnesium intake, such as dietary acid load, protein intake and dietary patterns rich in fruits and vegetables, may impact kidney disease risk. We hypothesized that lower dietary magnesium intake would be prospectively associated with more rapid kidney function decline. <b><i>Methods:</i></b> Among participants in the Healthy Aging in Neighborhoods of Diversity across the Life Span study with estimated glomerular filtration rate (eGFR) ≥60 ml/min/1.73 m<sup>2</sup> at baseline (2004-2009), dietary magnesium intake was calculated from two 24-hour dietary recalls. Rapid decline was defined as ≥3% eGFR decline per year. <b><i>Results:</i></b> Median (25th-75th percentile) dietary magnesium intake was 116 (96-356) mg/1,000 kcal. Among 1,252 participants, those with lower dietary magnesium intake were younger, and were more likely to be African-American men. A total of 177 participants (14.1%) experienced rapid eGFR decline over a median follow-up of 5 years. Lower dietary magnesium intake was significantly associated with a greater odds of rapid eGFR decline (OR for tertile 1 vs. 3: 2.02, 95% CI 1.05-3.86, p value for trend across tertiles = 0.02) in analyses adjusted for sociodemographics (age, sex, race, education level, health insurance status, poverty status), kidney disease risk factors (smoking status, diabetes, hemoglobin A1c, hypertension, body mass index), baseline eGFR and dietary factors (total energy intake; diet quality; dietary intake of fiber, sodium, calcium, potassium and phosphorus). <b><i>Conclusions:</i></b> In this urban population, lower dietary magnesium intake was independently associated with greater odds of rapid kidney function decline.
DOI: 10.6084/m9.figshare.4091490.v2
title: Topology files used in simulations

description: Here are the two force filed files used during MD simulations.<br>Both molecules (progesterone and asoprinsnil) were calculated using Gaussian09 with HF/6-31G*. After that, RESP was performed to obtain atomic charges. And the bonded and non-bonded parameters were derived from AMBER Gaff force field.Please cite: Zheng Liangzhen, et al. Exploring flexibility of Progesterone receptor ligand binding domain using molecular dynamics. Plos One, 2016.
DOI: 10.7910/dvn/v1rjpf/5nqser
title: cbb20170901_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4091490.v4
title: Topology files used in simulations

description: Here are the two force filed files used during MD simulations.<br>Both molecules (progesterone and asoprinsnil) were calculated using Gaussian09 with HF/6-31G*. After that, RESP was performed to obtain atomic charges. And the bonded and non-bonded parameters were derived from AMBER Gaff force field.Please cite: Zheng L, Lin VC, Mu Y (2016) Exploring Flexibility of Progesterone Receptor Ligand Binding Domain Using Molecular Dynamics. PLoS ONE 11(11): e0165824. doi:10.1371/journal.pone.0165824
DOI: 10.6084/m9.figshare.4091490.v3
title: Topology files used in simulations

description: Here are the two force filed files used during MD simulations.<br>Both molecules (progesterone and asoprinsnil) were calculated using Gaussian09 with HF/6-31G*. After that, RESP was performed to obtain atomic charges. And the bonded and non-bonded parameters were derived from AMBER Gaff force field.Please cite: Zheng Liangzhen, et al. Exploring flexibility of Progesterone receptor ligand binding domain using molecular dynamics. Plos One, 2016.
DOI: 10.7910/dvn/v1rjpf/zj8pkw
title: cbb20170304_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4089723
title: Conformations sampled in simulations of apo-form PR LBD

description: This part contains PR LBD conformations sampled from MD, umbrella sampling and metadynamics.<br>Structures from cMD, names apo2ovhL1 to L7, and holo2ovhL1 to L9.Structures from umbrella samping, names usL1, usL2.1 to usL2.10.Structures from metadynamics, names mL1 to mL8.<br>In the result part of the paper, the conformations were mentioned and described. Anything not clear, please refer to the paper (Zheng L, Lin VC, Mu Y (2016) Exploring Flexibility of Progesterone Receptor Ligand Binding Domain Using Molecular Dynamics. PLoS ONE 11(11): e0165824. doi:10.1371/journal.pone.0165824) or email me, or leave a comment here.<br>
DOI: 10.7910/dvn/v1rjpf/aoceyf
title: cbb20171119_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4089441
title: Supplementary Material for: Circulating Progenitor Cells Affect Thrombosis of Dialysis Arteriovenous Fistulas

description: <b><i>Background:</i></b> Arteriovenous fistula (AVF) thrombosis is a relevant cause of morbidity in hemodialysis (HD) patients. The number of circulating endothelial progenitor cells (EPCs) has been identified as a surrogate marker for vascular repair and health. Deficiency of EPCs has been demonstrated in dialysis patients to be associated with vascular events. Nonetheless, their role in thrombosis of AVFs remains unknown. <b><i>Methods:</i></b> From January 2010 to May 2013, 147 HD patients with dysfunctional AVFs were enrolled. Surface makers including CD34, KDR and CD133 were used in combination to determine the number of circulating EPCs. All participants were prospectively followed at 6-month interval until December 2015. The primary outcome was thrombosis of dialysis AVFs. <b><i>Results:</i></b> The median follow-up was 47 months, within which 42 patients experienced at least one episode of AVF thrombosis. Patients with AVF thrombosis had lower CD34<sup>+</sup>KDR<sup>+</sup> cell counts compared with patients without thrombosis (median 5 vs. 13 per 150,000 mononuclear cells, p &lt; 0.001). Kaplan-Meier analysis demonstrated an inverse relationship between CD34<sup>+</sup>KDR<sup>+</sup> cell count tertiles and thrombosis-free patency (59, 69 and 86% for low, middle and high tertiles; p = 0.02). Using Cox regression analysis, AVF thrombosis was predicted by baseline CD34<sup>+</sup>KDR<sup>+</sup> cell counts (hazards ratio (HR) 0.963, 95% CI 0.928-1.000, p = 0.05) and tertiles (high vs. low, HR 3.266, 95% CI 1.380-7.728, p &lt; 0.01). In multivariate analysis, only CD34<sup>+</sup>KDR<sup>+</sup> cell tertiles, C-reactive protein and lesion length remained independent predictors for thrombosis. <b><i>Conclusion:</i></b> Our study demonstrated an independently reverse association between circulating EPCs and thrombosis of dialysis AVFs. Further studies are warranted to ascertain whether EPCs serve as a marker or a therapeutic target for AVF thrombosis.
DOI: 10.7910/dvn/v1rjpf/zfsmda
title: cbb20170913_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4089723.v2
title: Conformations sampled in simulations of apo-form PR LBD

description: This part contains PR LBD conformations sampled from MD, umbrella sampling and metadynamics.<br>Structures from cMD, names apo2ovhL1 to L7, and holo2ovhL1 to L9.Structures from umbrella samping, names usL1, usL2.1 to usL2.10.Structures from metadynamics, names mL1 to mL8.<br>In the result part of the paper, the conformations were mentioned and described. Anything not clear, please refer to the paper (Zheng L, Lin VC, Mu Y (2016) Exploring Flexibility of Progesterone Receptor Ligand Binding Domain Using Molecular Dynamics. PLoS ONE 11(11): e0165824. doi:10.1371/journal.pone.0165824) or email me, or leave a comment here.<br>
DOI: 10.6084/m9.figshare.4089441.v1
title: Supplementary Material for: Circulating Progenitor Cells Affect Thrombosis of Dialysis Arteriovenous Fistulas

description: <b><i>Background:</i></b> Arteriovenous fistula (AVF) thrombosis is a relevant cause of morbidity in hemodialysis (HD) patients. The number of circulating endothelial progenitor cells (EPCs) has been identified as a surrogate marker for vascular repair and health. Deficiency of EPCs has been demonstrated in dialysis patients to be associated with vascular events. Nonetheless, their role in thrombosis of AVFs remains unknown. <b><i>Methods:</i></b> From January 2010 to May 2013, 147 HD patients with dysfunctional AVFs were enrolled. Surface makers including CD34, KDR and CD133 were used in combination to determine the number of circulating EPCs. All participants were prospectively followed at 6-month interval until December 2015. The primary outcome was thrombosis of dialysis AVFs. <b><i>Results:</i></b> The median follow-up was 47 months, within which 42 patients experienced at least one episode of AVF thrombosis. Patients with AVF thrombosis had lower CD34<sup>+</sup>KDR<sup>+</sup> cell counts compared with patients without thrombosis (median 5 vs. 13 per 150,000 mononuclear cells, p &lt; 0.001). Kaplan-Meier analysis demonstrated an inverse relationship between CD34<sup>+</sup>KDR<sup>+</sup> cell count tertiles and thrombosis-free patency (59, 69 and 86% for low, middle and high tertiles; p = 0.02). Using Cox regression analysis, AVF thrombosis was predicted by baseline CD34<sup>+</sup>KDR<sup>+</sup> cell counts (hazards ratio (HR) 0.963, 95% CI 0.928-1.000, p = 0.05) and tertiles (high vs. low, HR 3.266, 95% CI 1.380-7.728, p &lt; 0.01). In multivariate analysis, only CD34<sup>+</sup>KDR<sup>+</sup> cell tertiles, C-reactive protein and lesion length remained independent predictors for thrombosis. <b><i>Conclusion:</i></b> Our study demonstrated an independently reverse association between circulating EPCs and thrombosis of dialysis AVFs. Further studies are warranted to ascertain whether EPCs serve as a marker or a therapeutic target for AVF thrombosis.
DOI: 10.6084/m9.figshare.4089723.v1
title: Conformations sampled in simulations for apo-form PR LBD

description: This part contains PR LBD conformations sampled from MD, umbrella sampling and metadynamics.Structures from cMD, names apo2ovhL1 to L7, and holo2ovhL1 to L9.Structures from umbrella samping, names usL1, usL2.1 to usL2.10.Structures from metadynamics, names mL1 to mL8.<br><br>
DOI: 10.7910/dvn/v1rjpf/vvh4mz
title: cbb20170504_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/eratz9
title: cbb20170115_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/3xbaq1
title: cbb20171209_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/9zceb9
title: cbb20170517_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/x0ybgl
title: cbb20170922_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4083378
title: Xanthomonas gene sequences.rar

description: A database of 16S rRNA,<i> cpn60</i>, <i>gyrB</i>, and <i>avrBs2</i> gene sequences obtained from 327 <i>Xanthomonas</i> strains.
DOI: 10.7910/dvn/v1rjpf/dgipwg
title: cbb20170704_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4083378.v1
title: Xanthomonas gene sequences.rar

description: A database of 16S rRNA,<i> cpn60</i>, <i>gyrB</i>, and <i>avrBs2</i> gene sequences obtained from 327 <i>Xanthomonas</i> strains.
DOI: 10.7910/dvn/v1rjpf/homxtj
title: cbb20171013_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fpp6w8
title: cbb20170803_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4083348
title: YorkU.nearpond.impermeable.OCT26-2016

description: Meta Data in Word Document attached. Lab on October 26th 2016 - BIOL 2050 F
DOI: 10.6084/m9.figshare.4083348.v1
title: YorkU.nearpond.impermeable.OCT26-2016

description: Meta Data in Word Document attached. Lab on October 26th 2016 - BIOL 2050 F
DOI: 10.7910/dvn/v1rjpf/yotikt
title: cbb20170402_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/z7udw8
title: cbb20170716_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/3iyrpn
title: cbb20170421_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vqtz9v
title: cbb20170225_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/kp8ecu
title: cbb20170630_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/cepu9t
title: cbb20170319_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vlkeuc
title: cbb20171007_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ymqsch
title: cbb20171231_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rep6la
title: cbb20171014_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lki4dy
title: cbb20171125_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8vzlvj
title: cbb20170622_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fuhtil
title: cbb20170701_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lpizyc
title: cbb20171223_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4083276
title: Metadata

description: Metadata for the images taken at landcare research
DOI: 10.7910/dvn/v1rjpf/ko7ev2
title: cbb20170430_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4083276.v2
title: Metadata

description: Metadata for the images taken at landcare research
DOI: 10.7910/dvn/v1rjpf/6fqqsm
title: cbb20170306_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4083276.v1
title: New draft item

description: This is an example set of data for generating an error in the morphometricManova R code<br>
DOI: 10.6084/m9.figshare.4083264
title: chromosome number polymorphism

description: Dataset of the chromosome number polymorphism in Asteraceae family.
DOI: 10.6084/m9.figshare.4083267
title: chromosome number monomorphism

description: Dataset of monomorphic chromosome number of Asteraceae family.
DOI: 10.7910/dvn/v1rjpf/mm7oo8
title: cbb20170802_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4083267.v1
title: chromosome number monomorphism

description: Dataset of monomorphic chromosome number of Asteraceae family.
DOI: 10.6084/m9.figshare.4083264.v1
title: chromosome number polymorphism

description: Dataset of the chromosome number polymorphism in Asteraceae family.
DOI: 10.7910/dvn/v1rjpf/fy54h2
title: cbb20171220_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ndrjhw
title: cbb20170531_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4083198
title: Academic Reputation Building Mechanisms in Online Learning Environments

description: A presentation made at the 9th Annual Iranian Conference on e-Learning (ICeLeT), 11-13 March 2015, Kharazmi University, Tehran, IRAN. <br>Abstract: Currently academic reputation building is mainly based on research activities and publications. Online reputation building platforms such as ResearchGate, ImpactStory, GrowKudos, Academia and alike all mainly focus on research. Some of these also take into account interactions among peers and knowledge exchange activities; however, teaching and education related activities are totally missing in current reputation building activities. This paper studies a range of online reputation building platforms such as Altmetrics platforms, academic social networking services, and online learning environments to find out about any data and measure of reputation related to teaching and learning activities. Then, considering the types of data potentially available through different types of learning platforms, teaching related reputation building mechanisms and measures are proposed.
DOI: 10.7910/dvn/v1rjpf/fqw4os
title: cbb20170607_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4083198.v1
title: Academic Reputation Building Mechanisms in Online Learning Environments

description: A presentation made at the 9th Annual Iranian Conference on e-Learning (ICeLeT), 11-13 March 2015, Kharazmi University, Tehran, IRAN. <br>Abstract: Currently academic reputation building is mainly based on research activities and publications. Online reputation building platforms such as ResearchGate, ImpactStory, GrowKudos, Academia and alike all mainly focus on research. Some of these also take into account interactions among peers and knowledge exchange activities; however, teaching and education related activities are totally missing in current reputation building activities. This paper studies a range of online reputation building platforms such as Altmetrics platforms, academic social networking services, and online learning environments to find out about any data and measure of reputation related to teaching and learning activities. Then, considering the types of data potentially available through different types of learning platforms, teaching related reputation building mechanisms and measures are proposed.
DOI: 10.7910/dvn/v1rjpf/bwx2uh
title: cbb20171121_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4083159
title: York University Campus BIOL 2050 Ecology Network: Observing plants, vertebrates and invertebrates near a pond and impermeable area

description: The data was collected on the west side of York University’s Keele Campus on October 26th, 2016, for approximately 1.5 hours in total. Data was collected in two areas: near a pond and in an impermeable area, the latter of which was a baseball diamond. The weather was cloudy, 5 °C with a moderate breeze (a 4 on the Beaufort scale). The data was collected by Lauren Cunningham, Samina Munawar, Erin O’Connor, and Fatima Zia.Please see attached word document for meta-data
DOI: 10.7910/dvn/v1rjpf/dkymug
title: cbb20170412_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qgzt8w
title: cbb20170906_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jac21m
title: cbb20170222_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/sek2nl
title: cbb20170804_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/umzk2i
title: cbb20170305_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/aj2spj
title: cbb20170323_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/paqnum
title: cbb20170712_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zptuh9
title: cbb20170718_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/edjv5e
title: cbb20170127_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4082400
title: Dataset for "Collective properties of cellular identity: a computational approach"

description: Dataset to the paper "Collective properties of cellular identity: a computational approach". arXiv:1302:0826 [q-bio.GN, q-bio.QM] (2013). <br>arXiv location: https://arxiv.org/abs/1302.0826Github repository: https://github.com/balicea/collective-properties-cellular-identity<br>Dataset has 14 parts:1) calculated-kernels2) kernels-only3) separable-fuzzy-classifier4) separable-only-fuzzy-results5) introgressions6) All FIBS-kernels7) FIB kernels8) All STEM-kernels9) STEM kernels10) All FIBS to STEM kernels11) All STEM to FIB kernels12) non-separable13) separable14) across-all-pairwise
DOI: 10.7910/dvn/v1rjpf/nks0gx
title: cbb20170626_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4082400.v1
title: Dataset for "Collective properties of cellular identity: a computational approach"

description: Dataset to the paper "Collective properties of cellular identity: a computational approach". arXiv:1302:0826 [q-bio.GN, q-bio.QM] (2013). <br>arXiv location: https://arxiv.org/abs/1302.0826Github repository: https://github.com/balicea/collective-properties-cellular-identity<br>Dataset has 14 parts:1) calculated-kernels2) kernels-only3) separable-fuzzy-classifier4) separable-only-fuzzy-results5) introgressions6) All FIBS-kernels7) FIB kernels8) All STEM-kernels9) STEM kernels10) All FIBS to STEM kernels11) All STEM to FIB kernels12) non-separable13) separable14) across-all-pairwise
DOI: 10.7910/dvn/v1rjpf/0cznck
title: cbb20171111_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4080585
title: Sobral et al 2016 EcoLetters: .RData and script

description: Data set and R-script used in Sobral et al. 2016 Ecology Letters (doi: 10.1111/ele.12646)
DOI: 10.6084/m9.figshare.4082370
title: phylogenetic supertree of Asteraceae

description: Dataset of phylogenetic supertree of Asteraceae family
DOI: 10.7910/dvn/v1rjpf/fs397b
title: cbb20171002_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4080585.v1
title: Sobral et al 2016 EcoLetters: .RData and script

description: Data set and R-script used in Sobral et al. 2016 Ecology Letters (doi: 10.1111/ele.12646)
DOI: 10.7910/dvn/v1rjpf/ypejdp
title: cbb20170130_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jy2k3a
title: cbb20170817_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ec1th5
title: cbb20171126_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/v9pznk
title: cbb20170220_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/z1zbqc
title: cbb20170224_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/udyr5g
title: cbb20170503_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xc6rgy
title: cbb20170317_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ktfvah
title: cbb20171031_03.txt.gz

description: :unav