cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17894/ucph.c6b0edea-91ec-4175-9d7a-edaffb66572c
title: eDNA based biomonitoring at an experimental German vineyard to characterise how management regimes shape ecosystem diversity

description: There is growing interest in the application of sustainable agricultural methods to minimise the environmental impact of farming, and thus aiding quantification of the actual benefit that such approaches may confer. We used DNA metabarcoding to explore how the diversity of fungi and arthropods were affected by different agricultural management systems (integrated, organic, biodynamic) at the experimental vineyard of Geisenheim (Rheingau, Germany). Data were generated for the bloom and harvest periods in 2017, using environmental DNA (eDNA) metabarcoding analysis of both soil and trap samples. Our data revealed four principal results. (I) Overall richness of airborne arthropods was unaffected by the management systems, likely due to the relatively small scale of the plots compared to arthropod ranges. In contrast however, the richness of soil-living arthropods appeared to be negatively affected by conventional treatments, especially at harvest. (II) Analysis of similarity revealed that the species composition was significantly differentiated by management systems for both arthropods and fungi in both sample types. (III) Taxonomic analysis of fungi revealed differentiated abundance patterns for wine related fungi according to management systems. Overall, our study reiterates the potential of eDNA techniques as a tool for assessing how fungal and arthropod biodiversity is affected by different agricultural management regimes, and we hope such approaches will be adopted in future research aimed at guiding vineyard management decisions.
DOI: 10.25345/c5tn0h
title: MassIVE MSV000085907 - Anaerobic_fungi_are_an_untapped_reservoir_of_natural_products
title: Raw spectra supporting Swift et al. "Anaerobic fungi are an untapped reservoir of natural products."
DOI: 10.15482/usda.adc/1519173
title: Data from: Phenolic Compounds as Antioxidants to Improve Oxidative Stability of Menhaden Oil-Based Structured Lipid as Butterfat Analog
DOI: 10.25345/c5px7p
title: MassIVE MSV000085908 - Nabeel-Shah_RebL1_characterization_P108_VS6
title: This submission contains the mass spectrometry files for the manuscript by Nabeel-Shah et al. that describes the identification and functional characterization of the tetrahymena RBBP4/RBBP7 orthologue. AP-MS experiments were performed from tetrahymena thermophila cells and MS files were acquired on Orbitrap Fusion, 5600 TripleTOF and LTQ mass spectrometers. Please refer to the individual Tables 1 for additional details of submitted files. For questions, please contact Jean-Philippe Lambert (Jean-Philippe.Lambert@crchudequebec.ulaval.ca).
DOI: 10.5281/zenodo.3972345
title: Codes and datasets associated with the paper "Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers"

description: From here, you will find some of the codes, simulation data in the article: Dai, Y., Basu, S., Maronga, B. and de Roode, S.R., 2020. Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers. <em>arXiv preprint arXiv:2003.09463</em>. <strong>Simulation results from DALES: </strong> within the folder of DALES, D52 denotes default scheme of Deardorff, H52 denotes the revised Deardorff scheme. Numbers of each fold indicates the grid number in each direction. <strong>Simulation results from MATLES:</strong> file name: MATLES/variable.out Example: MATLES/aT.out, the potential temperature data (2D, time and height) from MATLES <strong>Python code for plotting: </strong> dalesfunc.py is the function file used to process data DALES_plotting.py is the code file used for plotting the simulation results from DALES and MATLES <strong>DALES input:</strong> namoptions in each folder is the file for DALES options input
DOI: 10.5281/zenodo.3972344
title: Codes and datasets associated with the paper "Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers"

description: From here, you will find some of the codes, simulation data in the article: Dai, Y., Basu, S., Maronga, B. and de Roode, S.R., 2020. Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers. <em>arXiv preprint arXiv:2003.09463</em>. <strong>Simulation results from DALES: </strong> within the folder of DALES, D52 denotes default scheme of Deardorff, H52 denotes the revised Deardorff scheme. Numbers of each fold indicates the grid number in each direction. <strong>Simulation results from MATLES:</strong> file name: MATLES/variable.out Example: MATLES/aT.out, the potential temperature data (2D, time and height) from MATLES <strong>Python code for plotting: </strong> dalesfunc.py is the function file used to process data DALES_plotting.py is the code file used for plotting the simulation results from DALES and MATLES <strong>DALES input:</strong> namoptions in each folder is the file for DALES options input
DOI: 10.15785/sbgrid/793
title: Micro-Electron Diffraction data from Human carbonic anhydrase II, source of 6YMB structure

description: Microcrystal electron diffraction data of HCA II
DOI: 10.15785/sbgrid/792
title: Micro-Electron Diffraction data from Human carbonic anhydrase II complexed with acetazolamide, source of 6YMA structure

description: Microcrystal electron diffraction data of HCA II:AZM
DOI: 10.5281/zenodo.3972349
title: Oxford-102 Speech Captions

description: Speech captions (synthesized by Tacotron2) of Oxford-102 using their original textual captions.
DOI: 10.5281/zenodo.3972348
title: Oxford-102 Speech Captions

description: Speech captions (synthesized by Tacotron2) of Oxford-102 using their original textual captions.
DOI: 10.5517/ccdc.csd.cc1zp6vq
title: CCDC 1837644: Experimental Crystal Structure Determination

description: Related Article: Wensheng Liu, Xiaoyan Qu, Chengfeng Zhu, Yuanhao Gao, Changjie Mao, Jiming Song, Helin Niu|2020|Microchim.Acta|187|136|doi:10.1007/s00604-019-3979-3
DOI: 10.13012/b2idb-1094364_v1
title: Trained models for multi-task multi-dataset learning for text classification as well as sequence tagging in tweets

description: Trained models for multi-task multi-dataset learning for text classification as well as sequence tagging in tweets. Classification tasks include sentiment prediction, abusive content, sarcasm, and veridictality. Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging. Models were trained using: https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification_tagging.py See https://github.com/socialmediaie/SocialMediaIE and https://socialmediaie.github.io for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929
DOI: 10.7910/dvn/ckrztt/sztyvi
title: 03. Upland Rice Nicaragua - with GPS.tab

description: Upland rice in Nicaragua with GPS
DOI: 10.7910/dvn/ckrztt/oyw51z
title: 02. Upland Rice Nicaragua.tab

description: Upland rice in Nicaragua
DOI: 10.5281/zenodo.3965035
title: Flye 2.8 assemblies

description: Examples of assemblies generated with Flye v2.8 (https://github.com/fenderglass/Flye)
DOI: 10.5517/ccdc.csd.cc25f0f2
title: CCDC 2008503: Experimental Crystal Structure Determination

description: Related Article: Daiji Ogata, Junpei Yuasa|2020|Chem.Commun.|56|8679|doi:10.1039/D0CC03539D
DOI: 10.5517/ccdc.csd.cc25p603
title: CCDC 2016364: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and Daniel Rabinovich|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25p614
title: CCDC 2016365: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and Daniel Rabinovich|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25p625
title: CCDC 2016366: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and Daniel Rabinovich|2020|CSD Communication|||
DOI: 10.25345/c5k46f
title: MassIVE MSV000085909 - Global Phosphoproteome Analysis Using FAIMS on a Hybrid Orbitrap Mass Spectrometer
title: Mass spectrometry is the premier tool for identifying and quantifying protein phosphorylation on a global scale. Despite requiring enrichment for global analysis, phosphopeptide samples are still sufficiently complex; achieving maximal depth of coverage therefore still requires offline liquid chromatography pre-fractionation, a cumbersome and laborious approach. Here, we incorporate a recently commercialized aerodynamic high-field asymmetric waveform ion mobility spectrometry (FAIMS) device into the phosphoproteomic workflow. This approach substantially streamlines deep phosphoproteomics. We characterize the effects of phosphorylation on the FAIMS separation, describe optimized compensation voltage settings for unlabeled phosphopeptides, and demonstrate the advantages of FAIMS-enabled gas-phase fractionation. Standard FAIMS single-shot analyses identified around 15-20% more phosphorylation sites than control experiments without FAIMS. In comparison to liquid chromatography pre-fractionation, FAIMS experiments yielded similar or superior results when analyzing up to four discrete fractions. Although using FAIMS led to a modest reduction in the precision of quantitative measurements, data collected with FAIMS yielded a 26% increase in total reproducible measurements. Overall, we conclude that the new FAIMS technology is a valuable addition for any phosphoproteomics experiment, with the greatest benefits arising from longer analyses and higher amounts of starting material.
DOI: 10.5281/zenodo.3968903
title: GInSAR vertical surface displacements over the San Joaquin Valley, CA spanning April 2015 to October 2017

description: A time series of vertical surface displacements over the San Joaquin Valley (SJV) spanning April 2015 to October 2017. Base Data: European Space Agency’s Sentinel-1A/B descending track 144; National Science Foundation Plate Boundary Observatory Global Positioning System (GPS) network; Southern California Earthquake Center’s Community Geodetic Model Time: 2015.04.01 – 2017.10.23, 51 acquisition times, 263 interferograms Processors/Software: GMTSAR, MATLAB Interferogram construction and unwrapping was performed in GMTSAR. GPS correction, time series inversion, and vertical projection was performed in MATLAB. This dataset was constructed following similar methods outlined in Neely et al., (2020). Neely, Wesley R., Adrian A. Borsa, and Francesca Silverii. “GInSAR: A CGPS Correction for Enhanced InSAR Time Series.” <em>IEEE Transactions on Geoscience and Remote Sensing</em> 58, no. 1 (January 2020): 136–46. https://doi.org/10.1109/TGRS.2019.2934118. This dataset is provided as a “.mat” compatible with MATLAB. Contents include: TS – 3110x2940x51 matrix of the vertical surface displacements (mm) relative to April 1, 2015 X – 3110x2940 matrix of the pixel longitudes (decimal degrees) Y – 3110x2940 matrix of the pixel latitudes (decimal degrees) rate – 3110x2940 matrix of the estimated vertical velocity (mm/yr) over April 2015 to October 2017 time – 51x1 array of the dates for each acquisition in MATLAB data. Provided as integer day where time(1) = 736055 à April 1, 2015 Example plotting in MATLAB: % plot the rate map figure pcolor(X,Y,rate) shading flat % plot the 25<sup>th</sup> time slice map of the displacement time series figure pcolor(X,Y,squeeze(TS(:,:,25)) shading flat % plot the time series at location X(1500,1500) and Y(1500,1500) figure plot(time,squeeze(TS(1500,1500,:)))
DOI: 10.5281/zenodo.3968902
title: GInSAR vertical surface displacements over the San Joaquin Valley, CA spanning April 2015 to October 2017

description: A time series of vertical surface displacements over the San Joaquin Valley (SJV) spanning April 2015 to October 2017. Base Data: European Space Agency’s Sentinel-1A/B descending track 144; National Science Foundation Plate Boundary Observatory Global Positioning System (GPS) network; Southern California Earthquake Center’s Community Geodetic Model Time: 2015.04.01 – 2017.10.23, 51 acquisition times, 263 interferograms Processors/Software: GMTSAR, MATLAB Interferogram construction and unwrapping was performed in GMTSAR. GPS correction, time series inversion, and vertical projection was performed in MATLAB. This dataset was constructed following similar methods outlined in Neely et al., (2020). Neely, Wesley R., Adrian A. Borsa, and Francesca Silverii. “GInSAR: A CGPS Correction for Enhanced InSAR Time Series.” <em>IEEE Transactions on Geoscience and Remote Sensing</em> 58, no. 1 (January 2020): 136–46. https://doi.org/10.1109/TGRS.2019.2934118. This dataset is provided as a “.mat” compatible with MATLAB. Contents include: TS – 3110x2940x51 matrix of the vertical surface displacements (mm) relative to April 1, 2015 X – 3110x2940 matrix of the pixel longitudes (decimal degrees) Y – 3110x2940 matrix of the pixel latitudes (decimal degrees) rate – 3110x2940 matrix of the estimated vertical velocity (mm/yr) over April 2015 to October 2017 time – 51x1 array of the dates for each acquisition in MATLAB data. Provided as integer day where time(1) = 736055 à April 1, 2015 Example plotting in MATLAB: % plot the rate map figure pcolor(X,Y,rate) shading flat % plot the 25<sup>th</sup> time slice map of the displacement time series figure pcolor(X,Y,squeeze(TS(:,:,25)) shading flat % plot the time series at location X(1500,1500) and Y(1500,1500) figure plot(time,squeeze(TS(1500,1500,:)))
DOI: 10.25919/5f29eae22f770
title: Parkes observations for project PX500 semester 2020APRS_03

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.5517/ccdc.csd.cc25p669
title: CCDC 2016370: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and Daniel Rabinovich|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25ph7m
title: CCDC 2016650: Experimental Crystal Structure Determination

description: Related Article: Moses I. Ihachi, M. Graca H. Vicente, Frank R. Fronczek|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25phbq
title: CCDC 2016653: Experimental Crystal Structure Determination

description: Related Article: Moses I. Ihachi, M. Graca H. Vicente, Frank R. Fronczek|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25r9mw
title: CCDC 2018398: Experimental Crystal Structure Determination

description: Related Article: J.R. Travis, G.P. Van Trieste III, M. Zeller, C.M. Zaleski|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1378|doi:10.1107/S2056989020010130
DOI: 10.5517/ccdc.csd.cc25r9nx
title: CCDC 2018399: Experimental Crystal Structure Determination

description: Related Article: J.R. Travis, G.P. Van Trieste III, M. Zeller, C.M. Zaleski|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1378|doi:10.1107/S2056989020010130
DOI: 10.5517/ccdc.csd.cc25sdm0
title: CCDC 2019452: Experimental Crystal Structure Determination

description: Related Article: S. Ossinger, C. Näther, F. Tuczek|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1398|doi:10.1107/S2056989020010361
DOI: 10.34740/kaggle/dsv/1397235
title: Healthcare Personnel Movement Data
DOI: 10.25422/azu.data.12712856.v3
title: How Cholesterol Stiffens Unsaturated Lipid Membranes

description: Neutron spin-echo (NSE) spectroscopy, solid-state deuterium nuclear magnetic resonance (2H NMR) spectroscopy, and molecular dynamics (MD) simulations are reported. Cholesterol locally increases the bending rigidity of DOPC membranes, similar to saturated membranes, by increasing the bilayer’s packing density. All three techniques, inherently sensitive to mesoscale bending fluctuations, show up to a 3-fold increase in effective bending rigidity with increasing cholesterol content approaching a mole fraction of 50%. Our observations are in good agreement with the known effects of cholesterol on the area compressibility modulus and membrane structure, reaffirming membrane structure-property relationships.<br><br><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i><br>
DOI: 10.3886/e119268
title: Data and code for: The economics of urban density

description: This repository distributes and documents computer programs and data to replicate the results obtained by Gilles Duranton and Diego Puga in their article 'The economics of urban density', to be published in Journal of Economic Perspectives.<br>Urban density boosts productivity and innovation, improves access to goods and services, reduces typical travel distances, encourages energy-efficient construction and transport, and facilitates sharing scarce amenities. However, density is also synonymous with crowding, makes living and moving in cities more costly, and concentrates exposure to pollution and disease. In this article, we explore the appropriate measurement of density and describe how it is both a cause and a consequence of the evolution of cities. We then discuss whether and how policy should target density and why the trade-off between its pros and cons is unhappily resolved by market and political forces.<br><br>This replication package calculates two measures of density: "naive density" (population per square kilometre) and "experienced density" (population within ten kilometres of the average resident) for US metropolitan areas and uses these data to produce the two panels in figure 1 in the article. It also calculates three elasticities for US metropolitan areas reported in the text of the article: the elasticity of experienced density with respect to city population, the elasticity of naive density with respect to city population, and the elasticity of average distance to the centre with respect to city population. Finally, it calculates experienced density for the entire Canada and for the entire United States.<br>
DOI: 10.3886/e119268v1
title: Data and code for: The economics of urban density

description: This repository distributes and documents computer programs and data to replicate the results obtained by Gilles Duranton and Diego Puga in their article 'The economics of urban density', to be published in Journal of Economic Perspectives.<br>Urban density boosts productivity and innovation, improves access to goods and services, reduces typical travel distances, encourages energy-efficient construction and transport, and facilitates sharing scarce amenities. However, density is also synonymous with crowding, makes living and moving in cities more costly, and concentrates exposure to pollution and disease. In this article, we explore the appropriate measurement of density and describe how it is both a cause and a consequence of the evolution of cities. We then discuss whether and how policy should target density and why the trade-off between its pros and cons is unhappily resolved by market and political forces.<br><br>This replication package calculates two measures of density: "naive density" (population per square kilometre) and "experienced density" (population within ten kilometres of the average resident) for US metropolitan areas and uses these data to produce the two panels in figure 1 in the article. It also calculates three elasticities for US metropolitan areas reported in the text of the article: the elasticity of experienced density with respect to city population, the elasticity of naive density with respect to city population, and the elasticity of average distance to the centre with respect to city population. Finally, it calculates experienced density for the entire Canada and for the entire United States.<br>
DOI: 10.3886/e120524
title: About Immortality Ro Sept2019

description: Single question questionnaire applied online on September 2019: You meet the "golden fish" that can fulfil your every wish. How long would you ask to live? (N=137) - database with English labels
DOI: 10.3886/e120524v1
title: About Immortality Ro Sept2019

description: Single question questionnaire applied online on September 2019: You meet the "golden fish" that can fulfil your every wish. How long would you ask to live? (N=137) - database with English labels
DOI: 10.3886/e120524v1-43459
title: About Immortality Ro Sept2019

description: Single question questionnaire applied online on September 2019: You meet the "golden fish" that can fulfil your every wish. How long would you ask to live? (N=137) - database with English labels
DOI: 10.3886/e117884
title: Code and Data for: Report for 2019 by the AEA Data Editor

description: Code and some data to support figures and tables in the 2019 Report by the AEA Data Editor.<br>
DOI: 10.3886/e117884v1
title: Code and Data for: Report for 2019 by the AEA Data Editor

description: Code and some data to support figures and tables in the 2019 Report by the AEA Data Editor.<br>
DOI: 10.3886/e120537
title: Checking the Evidence for Declining Discount Rates

description: A numerical model is used to experimentally compute certainty equivalent discount rates (CERs) of risk neutral and risk-averse decision makers. Investors are characterized by utility functions of the constant-intertemporal-elasticity-of-substitution (CIES) type. Stochastic interest rates are generated using a Cox, Ingersoll & Ross (CIR) type model, calibrated to 1992-2017 US three-month Treasury Bill rates. The paper replicates empirical studies providing evidence for declining discount rates (DDRs) and tests claims regarding risk averse CERs in a descriptive discounting context. It is shown that DDRs as proposed by Weitzman are based on a fallacy. The reviewed papers seeking empirical evidence of DDRs repeat the mistake. Risk averse CERs can be decline with time because of portfolio effects. If these are low, risk averse CERs are slightly lower than risk neutral ones but not secularly declining.<br><br><br><br><br>
DOI: 10.3886/e120537v1
title: Checking the Evidence for Declining Discount Rates

description: A numerical model is used to experimentally compute certainty equivalent discount rates (CERs) of risk neutral and risk-averse decision makers. Investors are characterized by utility functions of the constant-intertemporal-elasticity-of-substitution (CIES) type. Stochastic interest rates are generated using a Cox, Ingersoll & Ross (CIR) type model, calibrated to 1992-2017 US three-month Treasury Bill rates. The paper replicates empirical studies providing evidence for declining discount rates (DDRs) and tests claims regarding risk averse CERs in a descriptive discounting context. It is shown that DDRs as proposed by Weitzman are based on a fallacy. The reviewed papers seeking empirical evidence of DDRs repeat the mistake. Risk averse CERs can be decline with time because of portfolio effects. If these are low, risk averse CERs are slightly lower than risk neutral ones but not secularly declining.<br><br><br><br><br>