cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc23d4h5
title: CCDC 1948086: Experimental Crystal Structure Determination

description: Related Article: Xu-Cheng Gan|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc23w247
title: CCDC 1962428: Experimental Crystal Structure Determination

description: Related Article: Yang Huang, Hui-Dan Zhang, Pei Yang, Bin Zhang, Na Li, Guo-Dong Zou, and Yang Fan|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc240bbv
title: CCDC 1966526: Experimental Crystal Structure Determination

description: Related Article: Kendrick L. Smith, Cody L. Padgett, William D. Mackay, and Jeffrey S. Johnson|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26mr3q
title: CCDC 2044763: Experimental Crystal Structure Determination

description: Related Article: V. D. Romanov, E. V. Tretyakov, I. Yu. Bagryanskaya, D. Luneau|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26mwj8
title: CCDC 2044900: Experimental Crystal Structure Determination

description: Related Article: Shuxian  Zhou|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26n052
title: CCDC 2045013: Experimental Crystal Structure Determination

description: Related Article: Atena B. Solea, Aurelien Crochet, Katharina M. Fromm, Olimpia Mamula Steiner|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26n0jf
title: CCDC 2045024: Experimental Crystal Structure Determination

description: Related Article: Rafael Stieler, Osvaldo de Lazaro Casagrande Jr, Lucilene Losch de Oliveira|2020|CSD Communication|||
description: Related Article: L.L. de Oliveira, F.I. Batista, R.S. Oliboni, A.C.A. Casagrande, R. Stieler, O.L. Casagrande|2021|J.Organomet.Chem.|936|121710|doi:10.1016/j.jorganchem.2021.121710
DOI: 10.5517/ccdc.csd.cc26n0mj
title: CCDC 2045027: Experimental Crystal Structure Determination

description: Related Article: Rafael Stieler, Osvaldo de Lazaro Casagrande Jr, Lucilene Losch de Oliveira|2020|CSD Communication|||
description: Related Article: L.L. de Oliveira, F.I. Batista, R.S. Oliboni, A.C.A. Casagrande, R. Stieler, O.L. Casagrande|2021|J.Organomet.Chem.|936|121710|doi:10.1016/j.jorganchem.2021.121710
DOI: 10.5517/ccdc.csd.cc26n467
title: CCDC 2045138: Experimental Crystal Structure Determination

description: Related Article: John Gallagher, Niall Hehir, Marina Kilduff|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2767k8
title: CCDC 2061579: Experimental Crystal Structure Determination

description: Related Article: K. Carsch, S.E. Elder, D.K. Dogutan, D.G. Nocera, J. Yang, S.-L. Zheng, T. Daniel, T.A. Betley|2021|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|77|161|doi:10.1107/S2053229621001388
DOI: 10.5517/ccdc.csd.cc2767l9
title: CCDC 2061580: Experimental Crystal Structure Determination

description: Related Article: K. Carsch, S.E. Elder, D.K. Dogutan, D.G. Nocera, J. Yang, S.-L. Zheng, T. Daniel, T.A. Betley|2021|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|77|161|doi:10.1107/S2053229621001388
DOI: 10.5517/ccdc.csd.cc276zk0
title: CCDC 2062292: Experimental Crystal Structure Determination

description: Related Article: S. Andreev, D. Schollmeyer, P. Koch|2021|IUCrData|6|x210159|doi:10.1107/S2414314621001590
DOI: 10.5517/ccdc.csd.cc278qpy
title: CCDC 2063970: Experimental Crystal Structure Determination

description: Related Article: M. Mirzaei, M. Bazargan, P. Ebtehaj, J.T. Mague|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|309|doi:10.1107/S2056989021002000
DOI: 10.5517/ccdc.csd.cc278s8l
title: CCDC 2064019: Experimental Crystal Structure Determination

description: Related Article: A. Temesgen, A.G. Tskhovrebov, A.V. Vologzhanina, T.A. Le, V.N. Khrustalev|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|305|doi:10.1107/S2056989021002024
DOI: 10.5517/ccdc.csd.cc2792rd
title: CCDC 2064313: Experimental Crystal Structure Determination

description: Related Article: M.O. Plutenko, M. Haukka, A.O. Husak, T.S. Iskenderov, N.U. Mulloev|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|298|doi:10.1107/S205698902100205X
DOI: 10.5517/ccdc.csd.cc2798kd
title: CCDC 2064493: Experimental Crystal Structure Determination

description: Related Article: M.O. Shyshkina, D.A. Lega, V.D. Goryachiy, L.M. Shemchuk, D.V. Levashov, L.A. Shemchuk|2021|Acta Crystallogr.,Sect.E:Cryst.Commun.|77|294|doi:10.1107/S2056989021002085
DOI: 10.5517/ccdc.csd.cc279czx
title: CCDC 2064599: Experimental Crystal Structure Determination

description: Related Article: G. Vinotha, T.V. Sundar, N. Sharmila|2021|IUCrData|6|x210210|doi:10.1107/S2414314621002108
DOI: 10.6084/m9.figshare.14156483
title: Data (associated by figure) for Investigation of fractal carbon nanotube networks for biophilic neural sensing applications.

description: Raw data for figures in<br>Browning, L.A.; Watterson, W.; Happe, E.; Silva, S.; Abril Valenzuela, R.; Smith, J.; Dierkes, M.P.; Taylor, R.P.; Plank, N.O.V.; Marlow, C.A. Investigation of fractal carbon nanotube networks for biophilic neural sensing applications. <em>Nanomaterials</em> 2021, <em>11</em>(3), 636; https://doi.org/10.3390/nano11030636 (registering DOI) - 04 Mar 2021
DOI: 10.6084/m9.figshare.14156483.v2
title: Data (associated by figure) for Investigation of fractal carbon nanotube networks for biophilic neural sensing applications.

description: Raw data for figures in<br>Browning, L.A.; Watterson, W.; Happe, E.; Silva, S.; Abril Valenzuela, R.; Smith, J.; Dierkes, M.P.; Taylor, R.P.; Plank, N.O.V.; Marlow, C.A. Investigation of fractal carbon nanotube networks for biophilic neural sensing applications. <i>Nanomaterials </i><b>2021</b>, <i>11</i>, x.
DOI: 10.17861/529cdcbc-7c18-47a6-970f-755a5da19071
title: Cygnus A images at X, C and S band obtained via joint calibration and imaging

description: The dataset consists of monochromatic images of the celebrated radio galaxy Cyg A at X, C and S band, reconstructed via a joint calibration and imaging approach, leveraging non-convex optimisation (Dabbech et al. 2021) . X band images consists of estimated model and residual images, obtained with joint DDE calibration and imaging. C band images consists of estimated model and residual images, obtained with joint DDE calibration and imaging for various spatial bandwidths of the estimated DDEs, investigated in the associated paper (Dabbech et al. 2021). S band images consists of estimated model and residual images, obtained with joint DDE calibration and imaging.
DOI: 10.25387/g3.12017100
title: Supplemental Material for Iversen et al., 2020

description: S1 Differentially expressed genes between FW and SW at times T4, T5 and T6, for the different treatments (LL, SP, SPLL). Data filtered for logFC&gt;|1| and FDR&lt;0.01. S2 Box plots showing the expression of genes KCNJ1, KCNJ4, and JCAD (raw counts). S3 Additional GO plots showing GO-terms analysis of SW-sensitive genes expression at T4, T5 and T6 for the different photoperiod treatments. Data are shown as Bubble-plots of GO-terms and the number of genes linked to each term. Terms enriched across groups are indicated by colour. Strongly represented GO-terms are labelled. For T4 and T5 terms connected to biological processes, cellular components and molecular function are shown, for T6 only cellular comonnent and molecular function are shown as biological process can be viewed in figure 2. S4 Set of tables (sheet 1,2,3) showing the enriched GO-terms at T4, for the three photoperiod treatments, adjusted p-value&gt;0.05. Data has also been filtered for number of connected genes. Also included is an overview of the data behind T4 GO plots in S3, listing all GO terms, number of genes in each term, and overlap between photoperiod treatments. S5 Set of tables (sheet 1,2,3) showing the enriched GO-terms at T5, for the three photoperiod treatments, adjusted p-value&gt;0.05. Data has also been filtered for number of connected genes. Also included is an overview of the data behind T5 GO plots in S3, listing all GO terms, number of genes in each term, and overlap between photoperiod treatments. S6 Set of tables (sheet 1,2,3) showing the enriched GO-terms at T6, for the three photoperiod treatments, adjusted p-value&gt;0.05. Data has also been filtered for number of connected genes. Also included is an overview of the data behind T6 GO plots in S3, listing all GO terms, number of genes in each term, and overlap between photoperiod treatments. S7 Overview of NFAT-genes, including raw count data. Genes are ordered as in the heatmap. S8 Boxplots showing the expression of the NFAT genes (raw counts).
DOI: 10.25387/g3.12017100.v1
title: Supplemental Material for Iversen et al., 2020

description: S1 Differentially expressed genes between FW and SW at times T4, T5 and T6, for the different treatments (LL, SP, SPLL). Data filtered for logFC&gt;|1| and FDR&lt;0.01. S2 Box plots showing the expression of genes KCNJ1, KCNJ4, and JCAD (raw counts). S3 Additional GO plots showing GO-terms analysis of SW-sensitive genes expression at T4, T5 and T6 for the different photoperiod treatments. Data are shown as Bubble-plots of GO-terms and the number of genes linked to each term. Terms enriched across groups are indicated by colour. Strongly represented GO-terms are labelled. For T4 and T5 terms connected to biological processes, cellular components and molecular function are shown, for T6 only cellular comonnent and molecular function are shown as biological process can be viewed in figure 2. S4 Set of tables (sheet 1,2,3) showing the enriched GO-terms at T4, for the three photoperiod treatments, adjusted p-value&gt;0.05. Data has also been filtered for number of connected genes. Also included is an overview of the data behind T4 GO plots in S3, listing all GO terms, number of genes in each term, and overlap between photoperiod treatments. S5 Set of tables (sheet 1,2,3) showing the enriched GO-terms at T5, for the three photoperiod treatments, adjusted p-value&gt;0.05. Data has also been filtered for number of connected genes. Also included is an overview of the data behind T5 GO plots in S3, listing all GO terms, number of genes in each term, and overlap between photoperiod treatments. S6 Set of tables (sheet 1,2,3) showing the enriched GO-terms at T6, for the three photoperiod treatments, adjusted p-value&gt;0.05. Data has also been filtered for number of connected genes. Also included is an overview of the data behind T6 GO plots in S3, listing all GO terms, number of genes in each term, and overlap between photoperiod treatments. S7 Overview of NFAT-genes, including raw count data. Genes are ordered as in the heatmap. S8 Boxplots showing the expression of the NFAT genes (raw counts).
DOI: 10.5281/zenodo.4390539
title: Assortative Mating in Space and Time: Patterns and Biases

description: The data set is part of the article "Assortative mating in space and time: Patterns and biases" (https://onlinelibrary.wiley.com/doi/10.1111/ele.13690). The details of data collection are described in the Material and Methods section. Reference: Moura, R.R., Gonzaga, M.O., Pinto, N.S., Vasconcellos‐Neto, J. & Requena, G.S. (2021). Assortative mating in space and time: Patterns and biases. Ecol. Lett., ele.13690. https://doi.org/10.1111/ele.13690
description: {"references": ["Moura, R. R.; Gonzaga, M. O.; Pinto, N. S.; Vasconcellos-Neto, J.; Requena, G. S. Assortative mating in space and time: Patterns and biases. Ecology Letters, in press."]}
DOI: 10.5281/zenodo.4390538
title: Assortative Mating in Space and Time: Patterns and Biases

description: The data set is part of the article "Assortative mating in space and time: Patterns and biases" (https://onlinelibrary.wiley.com/doi/10.1111/ele.13690). The details of data collection are described in the Material and Methods section. Reference: Moura, R.R., Gonzaga, M.O., Pinto, N.S., Vasconcellos‐Neto, J. & Requena, G.S. (2021). Assortative mating in space and time: Patterns and biases. Ecol. Lett., ele.13690. https://doi.org/10.1111/ele.13690
description: {"references": ["Moura, R. R.; Gonzaga, M. O.; Pinto, N. S.; Vasconcellos-Neto, J.; Requena, G. S. Assortative mating in space and time: Patterns and biases. Ecology Letters, in press."]}
DOI: 10.17632/nk88nmvshx.1
title: Data for: Numerical modelling and optimization of Vertical Axis Wind Turbine pairs: a scale up approach

description: Results from CFD simulations. ID code refers to the following: [Turbine spacing with respect to turbine diameter] _ [Angle to wind direction] _ [Direction of rotation] The values were normalised with respect to the performance of a VAWT in isolation.
DOI: 10.5281/zenodo.4552736
title: Replication Package for "Global Imbalances and Policy Wars at the Zero Lower Bound"

description: The package contains the data to reproduce Figures 1-2, and the code to reproduce Figures 3-4 and A.1 in Caballero, Farhi and Gourinchas, "Global Imbalances and Policy Wars at the Zero Lower Bound" (forthcoming), Review of Economic Studies. Detailed instructions on data sources are also provided.
DOI: 10.5281/zenodo.4536618
title: Replication Package for "Global Imbalances and Policy Wars at the Zero Lower Bound"

description: The package contains the data to reproduce Figures 1-2, and the code to reproduce Figures 3-4 and A.1 in Caballero, Farhi and Gourinchas, "Global Imbalances and Policy Wars at the Zero Lower Bound" (forthcoming), Review of Economic Studies. Detailed instructions on data sources are also provided.
DOI: 10.6073/pasta/32f331063c6b2f2d9bfe647c2bf4ab36
title: Aboveground Biomass of Florida Scrub Plants

description: Biomass regressions are useful in non-destructively deriving biomass estimates for many applications. We present aboveground biomass regressions for 14 species of resprouting plants found in pyrogenic Florida scrub and related ecosystems. Aboveground biomass was collected from sites within five time-since-fire classes and predicted by plant height, canopy length, canopy width, species, and time-since-fire class. We were generally able to predict 72-95% of biomass variation from the three plant measurements. Regressions without plant width were almost as successful. For six of the species, time-since-fire affected the allometric equations, so we present regressions for specific time-since-fire classes for these species. Species differed markedly in their biomass equations. Within species groups, individual species usually differed but were similar for Lyonias. These equations will be useful in summarizing species responses to fire frequency and fire intensity in Florida scrub and related ecosystems.
DOI: 10.5281/zenodo.4582249
title: Locating a National Collection Our Place audience survey results

description: Results of an audience survey conducted by Locating a National Collection and funded by the AHRC. The research has been led by the National Trust in collaboration with the British Library and Research Bods, a market research company who have delivered results using the NT’s ‘Our Place’ online audience research platform. The survey consisted of a series of questions to be completed over the web by 992 members of the public in around 15 minutes. The survey aimed to investigate attitudes and behaviours in two areas: cultural heritage and digital technologies, with a clear focus on location in each.
DOI: 10.34943/9fcc97aa-5667-4d79-9e45-5ec4dd84df23
title: Burrard Inlet Acoustic Doppler Current Profiler 600 kHz Deployed 2019-03-28

description: The RDI Workhorse Monitor ADCP 600 kHz (SN 25471) was deployed on 2019-03-28 at Burrard Inlet. Burrard Inlet is a coastal fjord in southwestern British Columbia. This device is a Acoustic Doppler Current Profiler 600 kHz. Acoustic Doppler Current Profilers (ADCP) are hydroacoustic instruments, similar to sonars. ADCPs measure current speed and direction at multiple predetermined depths simultaneously. ADCPs use the Doppler effect of sound waves that are scattered by particles in seawater over a depth range. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.4231/acc5-mb58
title: Soil Explorer
DOI: 10.5521/forestplots.net/2018_4
title: Data for 'Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities'. 2019 Journal of Ecology. DOI: doi:10.1111/1365-2745.13067

description: Floristic plot data, spectrally derived data and associated analytical code (R and python) to replicate analysis in: 'Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities'  Draper et al  2018, Journal of Ecology.

Paper abstract:
 1.	The forests of Amazonia are among the most biodiverse on Earth, yet accurately quantifying how species composition varies through space (i.e. beta-diversity) remains a significant challenge. Here we use high-fidelity airborne imaging spectroscopy from the Carnegie Airborne Observatory to quantify a key component of beta-diversity, the distance decay in species similarity through space, across three landscapes in Northern Peru. We then compared our derived distance decay relationships to theoretical expectations obtained from a Poisson Cluster Process, known to match well with empirical distance decay relationships at local scales. 
2.	We used an unsupervised machine learning approach to estimate spatial turnover in species composition from the imaging spectroscopy data. We first validated this approach across two landscapes using an independent dataset of forest composition in 49 forest census plots (0.1-1.5 ha). We then applied our approach to three landscapes, which together represented terra firme clay forest, seasonally-flooded forest and white-sand forest. We finally used our approach to quantify landscape-scale distance decay relationships and compared these with theoretical distance decay relationships derived from a Poisson Cluster Process.
3.	We found a significant correlation of similarity metrics between spectral data and forest plot data, suggesting that beta-diversity within and among forest types can be accurately estimated from airborne spectroscopic data using our unsupervised approach. We also found that estimated distance decay in species similarity varied among forest types, with seasonally-flooded forests showing stronger distance decay than white-sand and terra firme forests. Finally, we demonstrated that distance decay relationships derived from the theoretical Poisson Cluster Process compare poorly with our empirical relationships.
4.	Synthesis: Our results demonstrate the efficacy of using high-fidelity imaging spectroscopy to estimate beta-diversity and continuous distance decay in lowland tropical forests. Furthermore, our findings suggest that distance decay relationships vary substantially among forest types, which has important implications for conserving these valuable ecosystems. Finally, we demonstrate that a theoretical Poisson Cluster Process poorly predicts distance decay in species similarity as conspecific aggregation occurs across a range of nested scales within larger landscapes.
DOI: 10.21227/wqy2-h486
title: IPTV dataset

description: https://www.scholat.com/teamworkdownloadscholar.html?id=5885teamId=612Note that, you must register on https://www.scholat.com, and join our team "new_media", finally download the dataset. Pretecting the user pravicy.The dataset is from a major IPTV service provider in southern China. Similar to the dataset used for research [1][2], not containing the detailed time when a viewer switched into a channel. This is the first to publish a complete dataset with the time granularity of second.There are totally 221k devices and 164 channels in the original dataset. Each entry consists of four fields. "DeviceID" refers to a subscriber, i.e. a user or a family;"ChannelNumber" represents the number of watching channel; "StartTime"is when the user starts to watch the channel; and "Duration" is how long the user stays in the channel. For instance, device {AC3B81E6-E531-4B03-8044-7649817F8836} switched into channel 19 at 2014-08-31 23:59:32.603000 and stayed for nearly 47120ms. [1]“Can Yang, Sixuan Ren, Yong Liu, Houwei Cao, Qihu Yuan, and Guoqiang Han. 2018. Personalized channel recommendation deep learning from a switch sequence. IEEE Access 6 (2018), 50824– 50838.”[2]“Chenguang Yu, Hao Ding, Houwei Cao, Yong Liu, and Can Yang. 2017. Follow me: Personalized IPTV channel switching guide. In Proceedings of the 8th ACM on Multimedia Systems Conference. ACM, 147–157”
DOI: 10.5521/forestplots.net/2018_3
title: Data package for 'Forest plot inventory data from seasonally dry and moist Atlantic forest in Rio de Janeiro State'

description: 'Forest plot inventory data from seasonally dry and moist Atlantic forest in Rio de Janeiro State'. Tree by tree census data and metadata for Dry forest biomes in Brazil - Newton Fund RCUK-FAPERJ , DryFlorBrazil project.
DOI: 10.5281/zenodo.4446165
title: Replication package: Does the duration of rapid release cycles affect the bug handling activity?

description: Replication package for the paper: "Does the duration of rapid release cycles affect the bug handling activity?"
DOI: 10.5281/zenodo.4446164
title: Replication package: Does the duration of rapid release cycles affect the bug handling activity?

description: Replication package for the paper: "Does the duration of rapid release cycles affect the bug handling activity?"
DOI: 10.6073/pasta/4fc3c563c93621cddc9b81299d1db467
title: Fern leaf traits observation at the Luquillo Experimental Forest (LEF)

description: Ferns are a common element of the understory of forests, yet little is known about the dynamics of leaf production. The long-term role of an individual fern in the ecosystem understory is a function of the number and size of leaves produced over time and the quality of those leaves. Selected functional plant traits (see also LUQ186 -Fern nutrients) were measured in order to supplement non-destructive measurements and detect patterns of primary productivity of ferns in the long-term studies at the Luquillo forest where ferns have been included (eg. Fern growth and demography (LUQ75), Canopy Trimming Experiment (LUQ143 and LUQ146) and the Luquillo Forest Dynamics Plot). Among the important characteristics of fern leaves in the forest understory are the area and biomass of leaves needed to calculate specific leaf area (SLA), leaf dry-matter content (LDMC) and leaf shrinkage. Therefore a large sample of whole leaves and leaf material from several species in the Luquillo Experimental Forest understory was collected, weighed and leaf area measured. The means and regression relationships among these functional traits for species, leaf type and leaf size can then be used to estimate leaf production and turnover rates in temporal studies of fern growth.
DOI: 10.15468/f2twe3
title: Ixodidae (Acari: Ixodoidea): descriptions and redescriptions of all known species from 1758 to December 31, 2019

description: This dataset contains the digitized treatments in Plazi based on the original journal article Guglielmone, Alberto A., Petney, Trevor N., Robbins, Richard G. (2020): Ixodidae (Acari: Ixodoidea): descriptions and redescriptions of all known species from 1758 to December 31, 2019. Zootaxa 4871 (1): 1-322, DOI: https://doi.org/10.11646/zootaxa.4871.1.1