cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3375747
title: Supporting data: The seasonal origins of streamwater in Switzerland

description: These data support the analysis conducted in "The seasonal origins of streamwater in Switzerland", accepted for publication in Geophysical Research Letters (https://doi.org/10.1029/2019GL084552) by Scott T. Allen, Jana von Freyberg, Markus Weiler, Gregory R. Goldsmith, James W. Kirchner. Information on the study sites, sampling procedures, and analysis can be found in the main text and supporting information files. Here we provide the data used: <strong>Data Table S1.</strong> Annual precipitation and discharge isotope values, precipitation sine fit parameters, and typical isotope ratios of summer and winter precipitation. <strong>Data Table S2.</strong> Streamwater grab sample SOI<sub>q</sub> values with matched daily discharge rates. See the Methods section for further details.
description: {"references": ["Allen ST, von Freyberg J, Weiler M, Goldsmith GR, Kirchner JW. The seasonal origins of streamwater in Switzerland. Geophysical Research Letters (accepted). doi.org/10.1029/2019GL084552."]}
DOI: 10.5281/zenodo.3375748
title: Supporting data: The seasonal origins of streamwater in Switzerland

description: These data support the analysis conducted in "The seasonal origins of streamwater in Switzerland", accepted for publication in Geophysical Research Letters (https://doi.org/10.1029/2019GL084552) by Scott T. Allen, Jana von Freyberg, Markus Weiler, Gregory R. Goldsmith, James W. Kirchner. Information on the study sites, sampling procedures, and analysis can be found in the main text and supporting information files. Here we provide the data used: <strong>Data Table S1.</strong> Annual precipitation and discharge isotope values, precipitation sine fit parameters, and typical isotope ratios of summer and winter precipitation. <strong>Data Table S2.</strong> Streamwater grab sample SOI<sub>q</sub> values with matched daily discharge rates. See the Methods section for further details.
description: {"references": ["Allen ST, von Freyberg J, Weiler M, Goldsmith GR, Kirchner JW. The seasonal origins of streamwater in Switzerland. Geophysical Research Letters (accepted). doi.org/10.1029/2019GL084552."]}
DOI: 10.7910/dvn/4cnelh/7nldye
title: PI_Sub_19_Drive_6.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/aqs3l0
title: PI_Sub_19_Drive_12.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/dclcg2
title: PI_Sub_19_Drive_1.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/dcryyh
title: PI_Sub_19_Drive_5_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/dyt8fs
title: PI_Sub_19_Drive_7_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/hvvmce
title: PI_Sub_19_Drive_7.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/i58kzj
title: PI_Sub_19_Drive_9.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/tikz65
title: PI_Sub_19_Drive_8_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/8a1xo3/3mgmil
title: tomato-angle-0-trial-0.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/3rmuaa
title: strawberry-angle-0-trial-0.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/5jih5d
title: cauliflower-angle-90-trial-5.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/btkvwg
title: cauliflower-angle-0-trial-3.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/hxibys
title: broccoli-angle-0-trial-7.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/l2utnj
title: broccoli-angle-90-trial-7.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/uvyutd
title: README.md

description: Description of folder structure and file types.
DOI: 10.7910/dvn/8a1xo3/vgs56z
title: pepper-angle-0-trial-2.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/w1kjzc
title: spinach-angle-0-trial-0.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/yrcjoa
title: pepper-angle-0-trial-0.zip

description: :unav
DOI: 10.7910/dvn/v3ki2n/zxn6bi
title: 20170528_225855_grid_real.csv

description: :unav
DOI: 10.15468/hlgleb
title: New Odonata from South-China

description: This dataset contains the digitized treatments in Plazi based on the original journal article K. D. P. Wilson (2004): New Odonata from South-China. Odonatologica 33 (4): 423-432, DOI: 10.5281/zenodo.3375618
DOI: 10.17632/4fbr8cwzsg.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample DU145 section 2-1)
DOI: 10.17632/4h48v397zc.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample DU145 section 1)
DOI: 10.17632/jh93tn6785.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample DU145 section 2-2)
DOI: 10.17632/xv329r84hn.1
title: Supplementary data for the Response Surface Modelling of the Biosorption of Zn(II) and Pb(II) onto Micropogonias Undulates Scales

description: This is the dataset associated with the research manuscript "Response Surface Modelling of the Biosorption of Zn(II) and Pb(II) onto Micropogonias Undulates Scales: Box-Behken Experimental Approach"
DOI: 10.17632/ypryjkmjx9.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D023 section 2)
DOI: 10.5281/zenodo.3361417
title: Descriptor dataset for predicting interfacial thermal resistance

description: We present details of the two datasets for the ITR model training and prediction, one is the "ITR dataset" and the other one is the "descriptor dataset" of various materials. The former dataset shows the ITR values of various interfaces with the measuring temperature, synthesized method, thermal measurement method, sample pre-treatment and its original references. The latter dataset shows the physical, chemical and process descriptors of 298 different materials, which are single element or binary compounds. These materials can construct over 80,000 pair-material systems (e.g. Bi/Si) for ITR prediction. Additionally, the total energy for the binding energy can also be found in the "atom_energy_vasp" file. Further, the training data for the ITR machine-learning model are furnished under the file name“training dataset for ITR prediction” and can be directly used as training data for ITR predictions. Descriptor dataset for predicting interfacial thermal resistance is licensed under a Creative Commons Attribution 4.0 International License.
description: {"references": ["Yen-Ju Wu, Lei Fang, Yibin Xu, npj Computational Materialsvolume 5, Article number: 56 (2019)", "Wu, Y., Zhan, T., Hou, Z. et al. Physical and chemical descriptors for predicting interfacial thermal resistance. Sci Data 7, 36 (2020)."]}
DOI: 10.5281/zenodo.3361418
title: Descriptor dataset for predicting interfacial thermal resistance

description: We present details of the two datasets for the ITR model training and prediction, one is the "ITR dataset" and the other one is the "descriptor dataset" of various materials. The former dataset shows the ITR values of various interfaces with the measuring temperature, synthesized method, thermal measurement method, sample pre-treatment and its original references. The latter dataset shows the physical, chemical and process descriptors of 298 different materials, which are single element or binary compounds. These materials can construct over 80,000 pair-material systems (e.g. Bi/Si) for ITR prediction. Additionally, the total energy for the binding energy can also be found in the "atom_energy_vasp" file. Further, the training data for the ITR machine-learning model are furnished under the file name“training dataset for ITR prediction” and can be directly used as training data for ITR predictions. Descriptor dataset for predicting interfacial thermal resistance is licensed under a Creative Commons Attribution 4.0 International License.<br> Based on a work at https://doi.org/10.5281/zenodo.3361418.
description: {"references": ["Yen-Ju Wu, Lei Fang, Yibin Xu, npj Computational Materialsvolume 5, Article number: 56 (2019)"]}
DOI: 10.5281/zenodo.3376168
title: StEmp-Tool ABW Dataset

description: Dataset used in the Stakeholder Empowerment (StEmp) Tool for Anhalt-Bitterfeld-Wittenberg (ABW), developed by the Reiner Lemoine Institute. The tool lives on github The docs can be found on ReadTheDocs
DOI: 10.7910/dvn/4cnelh/5t87ug
title: PI_Sub_19_Drive_11_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/ereah8
title: PI_Sub_19_Drive_1_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/mqv1br
title: PI_Sub_19_Drive_4.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/nhrhbs
title: PI_Sub_19_Drive_10_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/ocat1s
title: PI_Sub_19_Drive_265.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/ujzfw1
title: PI_Sub_19_Drive_10.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/va5kbi
title: PI_Sub_19_Drive_00_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/vyjfha
title: PI_Sub_19_Drive_3.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/yyfbs4
title: PI_Sub_19_Drive_4_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/8a1xo3/17uhor
title: strawberry-angle-0-trial-13.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/1v981t
title: apple-angle-0-trial-9.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/2rnxtp
title: pineapple-angle-0-trial-8.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/a6g69v
title: honeydew-angle-0-trial-2.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/c0hpel
title: kiwi-angle-0-trial-8.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/gpffvf
title: spinach-angle-0-trial-5.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/gpyham
title: broccoli-angle-0-trial-4.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/k2rxs9
title: celery-angle-90-trial-4.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/n4d3fy
title: honeydew-angle-90-trial-1.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/pukwgx
title: apple-angle-90-trial-1.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/qliwwa
title: strawberry-angle-90-trial-3.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/zdpvml
title: strawberry-angle-0-trial-7.zip

description: :unav
DOI: 10.17632/228sv9m46x.1
title: Data for: Vacuum Bagged Composite Laminate Forming Processes: Predicting Thickness Deviation in Complex Shapes

description: This is the experimental data set that goes with the article. As well as the matlab script used to produce the design charts.
DOI: 10.17632/66cfx3k9px.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D021 section 1)
DOI: 10.17632/f6g6crsprr.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D014 section 2-2 & 3)
DOI: 10.17632/hhtybt9t3j.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D021 section 2)
DOI: 10.17632/hr3hhhnvdv.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D023 section 1)
DOI: 10.17632/pcznh6kzwx.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D022 section 1 & 2)
DOI: 10.17632/rgdg47rzzj.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D016 section 2-1)
DOI: 10.17632/vpxvw6xpjz.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D016 section 1)
DOI: 10.17632/xzjp6y4pz3.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D016 section 2-2)
DOI: 10.5281/zenodo.3375294
title: Measured BSDF and data-driven model of a laser cut panel (LCP001)

description: Model generated from the measured Bidirectional Scattering Distribution Function (BSDF) of a LCP. The RADIANCE tool-chain pabopto2bsdf, bsdf2ttree was employed. Initial tensor resolution set to 128x128 incident, and equal number of outgoing, scattered directions (corresponding to approx. 1.4 degree). Subsequent adaptive data reduction by approx. 98%. Photometric BSDF. The measured data is included as Differential Scattering Function (DSF): DSF = BSDF x cos(theta_s), where theta_s is the off-normal angle of the outgoing, scattered direction of light (the first column in the measured data). The data was measured on a scanning gonio-photometer (pab advanced technologies pgII) at the optical laboratory of CC Building Envelopes. A halogen lamp was employed, focud on the detector plane for maximum resolution, with a hot-mirror installed in the illuminator to block near infrared emission. The Si photocell of the detector was equipped with a weighing filter to match photometric response v(lambda). The profiles through the unobstructed beam are given for the phi=0°,180°, and phi=90°,270° planes. The angular coordinates theta=90, phi=0 correspond to the intended up direction when installed vertically, e.g. in a window. When publishing any work making use of this data-set, please reference either this data-set, including its Digital Object Identifier (DOI:10.5281/zenodo.3375294), or (preferred) by this article which describes sample, model and its exemplary application: Lars Oliver Grobe. Photon mapping in image-based visual comfort assessments with BSDF models of high resolution. Journal of Building Performance Simulation. DOI:10.1080/19401493.2019.1653994
description: This research was financially supported by the Swiss Innovation Agency Innosuisse and is part of the Swiss Competence Center for Energy Research SCCER FEEB&D.
description: {"references": ["Lars Oliver Grobe. Photon mapping in image-based visual comfort assessments with BSDF models of high resolution. Journal of Building Performance Simulation. DOI:10.1080/19401493.2019.1653994"]}
DOI: 10.5281/zenodo.3375295
title: Measured BSDF and data-driven model of a laser cut panel (LCP001)

description: Model generated from the measured Bidirectional Scattering Distribution Function (BSDF) of a LCP. The RADIANCE tool-chain pabopto2bsdf, bsdf2ttree was employed. Initial tensor resolution set to 128x128 incident, and equal number of outgoing, scattered directions (corresponding to approx. 1.4 degree). Subsequent adaptive data reduction by approx. 98%. Photometric BSDF. The measured data is included as Differential Scattering Function (DSF): DSF = BSDF x cos(theta_s), where theta_s is the off-normal angle of the outgoing, scattered direction of light (the first column in the measured data). The data was measured on a scanning gonio-photometer (pab advanced technologies pgII) at the optical laboratory of CC Building Envelopes. A halogen lamp was employed, focud on the detector plane for maximum resolution, with a hot-mirror installed in the illuminator to block near infrared emission. The Si photocell of the detector was equipped with a weighing filter to match photometric response v(lambda). The profiles through the unobstructed beam are given for the phi=0°,180°, and phi=90°,270° planes. The angular coordinates theta=90, phi=0 correspond to the intended up direction when installed vertically, e.g. in a window. When publishing any work making use of this data-set, please reference either this data-set, including its Digital Object Identifier (DOI:10.5281/zenodo.3375294), or (preferred) by this article which describes sample, model and its exemplary application: Lars Oliver Grobe. Photon mapping in image-based visual comfort assessments with BSDF models of high resolution. Journal of Building Performance Simulation. DOI:10.1080/19401493.2019.1653994
description: This research was financially supported by the Swiss Innovation Agency Innosuisse and is part of the Swiss Competence Center for Energy Research SCCER FEEB&D.
description: {"references": ["Lars Oliver Grobe. Photon mapping in image-based visual comfort assessments with BSDF models of high resolution. Journal of Building Performance Simulation. DOI:10.1080/19401493.2019.1653994"]}
DOI: 10.7910/dvn/4cnelh/20optg
title: PI_Sub_20_Drive_12.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/2pgnnp
title: PI_Sub_19_Drive_5.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/3ltzm6
title: PI_Sub_19_Drive_6_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/dd2yxn
title: PI_Sub_19_Drive_12_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/ijf70c
title: PI_Sub_19_Drive_2.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/rcybp2
title: PI_Sub_19_Drive_11.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/rpdpy0
title: PI_Sub_19_Drive_00.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/ucsexe
title: PI_Sub_19_Drive_9_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/vxaqk1
title: PI_Sub_19_Drive_3_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/8a1xo3/cibice
title: apple-angle-0-trial-4.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/ignwlr
title: banana-angle-0-trial-4.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/mdp8tz
title: strawberry-angle-0-trial-11.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/nofyfp
title: pepper-angle-0-trial-4.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/ufqhuw
title: pineapple-angle-90-trial-1.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/v9iyte
title: cantaloupe-angle-90-trial-5.zip

description: :unav
DOI: 10.17632/63vvxc3xvf.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D007 section 1 & 2)
DOI: 10.17632/8w9kh2n45n.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D014 section 1 & 2-1)
DOI: 10.17632/cfhcfhthdf.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D008 section 2)
DOI: 10.17632/djsdf4rk2f.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D011)
DOI: 10.17632/h3z52v2pck.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D008 section 1)
DOI: 10.17632/hjcvrxf55x.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D012 section 1 & 2-1)
DOI: 10.17632/v4dcg34yst.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D012 section 2-2)
DOI: 10.17632/v4dcg34yst.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D012 section 2-2)
DOI: 10.17632/yfj5zt2wkf.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D003 section 2)
DOI: 10.7910/dvn/4cnelh/5wmfxg
title: PI_Sub_20_Drive_3_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/67hp5n
title: PI_Sub_20_Drive_6.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/bquvja
title: PI_Sub_20_Drive_7.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/c156y9
title: PI_Sub_20_Drive_10_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/jhg8bq
title: PI_Sub_20_Drive_9_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/lbltqp
title: PI_Sub_20_Drive_6_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/o7hook
title: PI_Sub_20_Drive_8.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/p7hte6
title: PI_Sub_20_Drive_1.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/phckau
title: PI_Sub_20_Drive_4_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/umjqdl
title: PI_Sub_20_Drive_9.plt

description: :unav
DOI: 10.7910/dvn/8a1xo3/d2aml1
title: honeydew-angle-0-trial-7.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/h5mfng
title: spinach-angle-0-trial-7.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/iuynzi
title: red_grape-angle-90-trial-2.zip

description: :unav
DOI: 10.17632/357f736283.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D000 section 1 &2)
DOI: 10.17632/3dbfd7wrdv.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample 997270T section 1)
DOI: 10.17632/c4dgfbrkv9.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample 994308T section 1 & 2)
DOI: 10.17632/dxx7kf5dwd.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample 997270T section 2)
DOI: 10.17632/f3ymxwc9yv.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample 998741T section 1 & 2)
DOI: 10.17632/jy3xk9bsxz.1
title: Data for: Global versus local processes during the Pliensbachian–Toarcian transition at the Peniche GSSP, Portugal: A multi-proxy record

description: Raw data, figure showing the readjustment of the carbon isotope data, and clumped isotope values (brachiopod shells) discussed in the text
DOI: 10.17632/z7p9wsg8yk.1
title: Spectral data of a diterpene mixture

description: 1H and 13C NMR spectra of a diterpene mixture from Xylopia sericea.
DOI: 10.21227/z4hm-4804
title: Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems

description: Indoor positioning systems based on radio frequency systems such as UWB inherently present multipath related phenomena. This causes ranging systems such as UWB}to lose accuracy by detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will make important errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques for a previous classification and mitigation of the propagation effects. For this purpose, real cross scenarios are considered, where the data extracted from UWB low-cost devices for the training of the algorithms come from different environments than those considered for the real application and its analysis.
DOI: 10.7910/dvn/4cnelh/1bxq14
title: PI_Sub_20_Drive_10.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/3ti87b
title: PI_Sub_20_Drive_8_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/5sg2er
title: PI_Sub_20_Drive_1_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/djnxqy
title: PI_Sub_20_Drive_5.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/jxtaqn
title: PI_Sub_20_Drive_12_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/ljh7dz
title: PI_Sub_20_Drive_2.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/lrxdjy
title: PI_Sub_20_Drive_3.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/rbuxxt
title: PI_Sub_20_Drive_5_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/8a1xo3/2rfyev
title: kiwi-angle-0-trial-3.zip

description: :unav
DOI: 10.7910/dvn/8a1xo3/xjjvq7
title: lettuce-angle-0-trial-7.zip

description: :unav
DOI: 10.17632/cj4sznjw7w.1
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample D003 section 1)
DOI: 10.17632/hhvhn7t8wn.1
title: Language quality in Predatory and Non-Predatory Journal articles

description: The study compared the spelling, punctuation and grammar (SPaG)errors in 30 predatory and 30 nonpredatory journal English and literature-related articles. We used updates of Beall's list of predatory journals to select predatory journals published in 2018. Journals that were considered nonpredatory were open access journals published by institutions, learned organizations,indexed in DOAJ, Scopus, and /or web of science. The first 1000 words in each selected journal were checked for SPaG errors. The data were tabulated. Research questions were answered using simple percentages and charts. The analysis showed that predatory journal articles presented more SPaG errors than nonpredatory journal articles. The most prevalent errors were grammar errors in both types of articles.
DOI: 10.17632/xpywbvs5zn.1
title: Data for: Development of the Sensitizer for Generating Higher-Energy Photons under Diluted Condition via the Triplet-Triplet Annihilation-Supported Upconversion

description: supporting information
DOI: 10.18112/openneuro.ds002147.v1.0.0
title: Multi-echo multi-band pilot
DOI: 10.5066/p9wyd6ls
title: Geospatial and survey data for flood-inundation maps in a 23-mile reach of the Medina River at Bandera, Texas, 2018
DOI: 10.7910/dvn/4cnelh/8tzjcv
title: PI_Sub_20_Drive_11_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/fqey08
title: PI_Sub_20_Drive_2_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/lcvf0n
title: PI_Sub_20_Drive_7_GroundSim.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/vjaeyy
title: PI_Sub_20_Drive_11.plt

description: :unav
DOI: 10.7910/dvn/4cnelh/ylvd0w
title: PI_Sub_20_Drive_4.plt

description: :unav
DOI: 10.7910/dvn/8a1xo3/pz2exj
title: honeydew-angle-0-trial-9.zip

description: :unav