cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/51d4f3d0a91db1d68237e21b7bcafbcd
title: 2003 Prescribed Burn Effect on Chihuahuan Desert Grasses and Shrubs at the Sevilleta National Wildlife Refuge, New Mexico: Species Composition Study (2004-2018)

description: Disturbance from fire can affect the abundance and distribution of shrubs and grasses in arid ecosystems. In particular, fire may increase grass and forb production while hindering shrub encroachment. Therefore, prescribed fires are a common management tool for maintaining grassland habitats in the southwest. However, Bouteloua eriopoda (black grama), a dominant species in Chihuahuan Desert grassland, is highly susceptible to fire resulting in death followed by slow recovery rates. A prescribed fire on the Sevilleta National Wildlife refuge in central New Mexico in 2003 provided the opportunity to study the effects of infrequent fires on vegetation in this region. This study was conducted along a transition zone where creosote bushes (Larrea tridentata) are encroaching on a black grama grassland. Before and after the fire, above ground plant productivity and composition were monitored from 2003 to present. Following the prescribed fire, there were fewer individual grass clumps and less above ground grass cover in burned areas compared to unburned areas. This decrease in productivity was primarily from a loss of B. eriopoda. Specifically, B. eriopoda density and cover were significantly lower following the fire with a slow recovery rate in the five years following the fire. Other grasses showed no such adverse response to burning. Data were collected from 2004-2013 and 2018. Data were not collected for 2014-2017.
DOI: 10.5281/zenodo.5153908
title: First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt: scripts and models

description: In this repository you will find the models and the scripts used to generate the journal article: "First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt." The model.tar contains the script for making a model, in addition to the models used in the journal artcile. The proc.tar contains the scripts used for processing of the CMIP6 data. The plots.tar contains scripts for generating the plots in the journal article, as well as the supplementary information.
DOI: 10.5281/zenodo.5153907
title: First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt: scripts and models

description: In this repository you will find the models and the scripts used to generate the journal article: "First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt." The model.tar contains the script for making a model, in addition to the models used in the journal artcile. The proc.tar contains the scripts used for processing of the CMIP6 data. The plots.tar contains scripts for generating the plots in the journal article, as well as the supplementary information.
DOI: 10.6073/pasta/079019c988152e8ece0d38ee45684943
title: Point-Quarter Distance and Dimension Measurements to Calculate Shrub Density and Estimate Shrub ANPP in a Chihuahuan Desert Creosote Shrubland at the Sevilleta National Wildlife Refuge, New Mexico

description: In an effort to better quantify NPP of Creosotebush in the Five-Points region, it was decided to test the Point-Quarter method against the standard 1-m2 quadrat method that has been in use since 1998. Transects were laid out across the 5 mammal trapping webs as well as across burned and unburned plots of the Mixed Shrub site (MS). Repeated measures of the same bushes are performed seasonally. Whole shrubs of various size classes are collected, sorted, and weighed to develop regressions for biomass. Purpose: Data was collected initially to determine density and dimensions of creosote bush in the Five points area on core rodent webs and on burned and unburned plots following the 2003 prescribed burn. It was decided to expand the project by continuing measurements through time to quantify the change in shrub size and with simultaneous harvest of shrubs to measure NPP.
DOI: 10.6073/pasta/696c2d207b59556dd39d44eb656dda23
title: Gunnison's Prairie Dog Restoration Experiment (GPDREx): Vegetation Cover Data from the Sevilleta National Wildlife Refuge, New Mexico (2011-2016)

description: Prairie dogs (Cynomys spp.) are burrowing rodents considered to be ecosystem engineers and keystone species of the central grasslands of North America. Yet, prairie dog populations have declined by an estimated 98% throughout their historic range. This dramatic decline has resulted in the widespread loss of their important ecological role throughout this grassland system. The 92,060 ha Sevilleta NWR in central New Mexico includes more than 54,000 ha of native grassland. Gunnison's prairie dogs (C. gunnisoni) were reported to occupy ~15,000 ha of what is now the SNWR during the 1960's, prior to their systematic eradication. In 2010, we collaborated with local agencies and conservation organizations to restore the functional role of prairie dogs to the grassland system. Gunnison's prairie dogs were reintroduced to a site that was occupied by prairie dogs 40 years ago. This work is part of a larger, long-term study where we are studying the ecological effects of prairie dogs as they re-colonize the grassland ecosystem.
DOI: 10.6084/m9.figshare.15091509
title: Data repository Hidde Ferwerda 2021.xlsx

description: The data found to determine the species composition in the Noordzee kanaal with the use of fieldwork and eDNA sampling.
DOI: 10.6084/m9.figshare.15091509.v1
title: Data repository Hidde Ferwerda 2021.xlsx

description: The data found to determine the species composition in the Noordzee kanaal with the use of fieldwork and eDNA sampling.
DOI: 10.6084/m9.figshare.14916393.v1
title: Differences in mineral accumulation and gene expression profiles between two metal hyperaccumulators, <i>Noccaea japonica</i> and <i>Noccaea caerulescens</i> ecotype Ganges, under excess nickel condition

description: Here we compare mineral accumulation and global gene expression patterns between two metal hyperaccumulator plants – <i>Noccaea japonica</i>, originating from Ni-rich serpentine soils, and <i>Noccaea caerulescens</i> (ecotype Ganges), originating from Zn/Pb-mine soils – under excess Ni conditions. Significant differences in the accumulation of K, P, Mg, B, and Mo were explained by the expression levels of specific transporters for each mineral. We previously showed that total Ni accumulation in the whole plant is higher in <i>N. caerulescens</i> than in <i>N. japonica</i>. Here we found a similar tendency for Fe under excess Ni; however, the expression of <i>iron-regulated transporter 1</i> (<i>IRT1</i>), which encodes the primary Fe uptake transporter and causes excess Ni uptake in <i>Arabidopsis thaliana</i>, was higher in <i>N. japonica</i>. NjIRT1 has a point mutation at Asp100, which is essential for Fe transport, and so might lack its Fe and possibly Ni transport function. <i>Noccaea japonica</i> might have lost its IRT1 function, which would prevent excess Ni uptake via IRT1 in Ni-rich soils, and come to rely on other transporters.
DOI: 10.6084/m9.figshare.14916393
title: Differences in mineral accumulation and gene expression profiles between two metal hyperaccumulators, <i>Noccaea japonica</i> and <i>Noccaea caerulescens</i> ecotype Ganges, under excess nickel condition

description: Here we compare mineral accumulation and global gene expression patterns between two metal hyperaccumulator plants – <i>Noccaea japonica</i>, originating from Ni-rich serpentine soils, and <i>Noccaea caerulescens</i> (ecotype Ganges), originating from Zn/Pb-mine soils – under excess Ni conditions. Significant differences in the accumulation of K, P, Mg, B, and Mo were explained by the expression levels of specific transporters for each mineral. We previously showed that total Ni accumulation in the whole plant is higher in <i>N. caerulescens</i> than in <i>N. japonica</i>. Here we found a similar tendency for Fe under excess Ni; however, the expression of <i>iron-regulated transporter 1</i> (<i>IRT1</i>), which encodes the primary Fe uptake transporter and causes excess Ni uptake in <i>Arabidopsis thaliana</i>, was higher in <i>N. japonica</i>. NjIRT1 has a point mutation at Asp100, which is essential for Fe transport, and so might lack its Fe and possibly Ni transport function. <i>Noccaea japonica</i> might have lost its IRT1 function, which would prevent excess Ni uptake via IRT1 in Ni-rich soils, and come to rely on other transporters.
DOI: 10.6084/m9.figshare.14761905.v1
title: <i>Arabidopsis thaliana</i> TCP15 interacts with the MIXTA-like transcription factor MYB106/NOECK

description: MYB106 and MYB16 are MIXTA-like transcription factors that control trichome maturation and cuticle formation in <i>Arabidopsis</i>. In a recent study, we found that the TEOSINTE BRANCHED 1, CYCLOIDEA and PROLIFERATING CELL FACTORS (TCP) transcription factor TCP15 also acts as an important regulator of aerial epidermis specialization in <i>Arabidopsis</i> through the control of trichome development and cuticle formation. TCP15 and MYB106 regulate the expression of common groups of genes, including genes coding for transcription factors and enzymes of the cuticle biosynthesis pathway. In this study, we report that TCP15 physically interacts with MYB106 when both proteins are expressed in yeast cells or <i>Nicotiana bentamiana</i> leaves. Furthermore, we also observed interaction in leaves of <i>Arabidopsis thaliana</i>. Altogether, our findings raise the possibility that TCP15 and MYB106 bind together to the promoters of target genes to exert their action. Our data provide a base to investigate the role of TCP-MIXTA complexes in the context of cuticle development in <i>Arabidopsis thaliana</i>.
DOI: 10.6084/m9.figshare.14761905
title: <i>Arabidopsis thaliana</i> TCP15 interacts with the MIXTA-like transcription factor MYB106/NOECK

description: MYB106 and MYB16 are MIXTA-like transcription factors that control trichome maturation and cuticle formation in <i>Arabidopsis</i>. In a recent study, we found that the TEOSINTE BRANCHED 1, CYCLOIDEA and PROLIFERATING CELL FACTORS (TCP) transcription factor TCP15 also acts as an important regulator of aerial epidermis specialization in <i>Arabidopsis</i> through the control of trichome development and cuticle formation. TCP15 and MYB106 regulate the expression of common groups of genes, including genes coding for transcription factors and enzymes of the cuticle biosynthesis pathway. In this study, we report that TCP15 physically interacts with MYB106 when both proteins are expressed in yeast cells or <i>Nicotiana bentamiana</i> leaves. Furthermore, we also observed interaction in leaves of <i>Arabidopsis thaliana</i>. Altogether, our findings raise the possibility that TCP15 and MYB106 bind together to the promoters of target genes to exert their action. Our data provide a base to investigate the role of TCP-MIXTA complexes in the context of cuticle development in <i>Arabidopsis thaliana</i>.
DOI: 10.6084/m9.figshare.14813328
title: Can plants fool artificial intelligence? Using machine learning to compare between bee orchids and bees

description: Bee orchids have long been an excellent example of how dishonest signal works in plant-animal interaction. Many studies compared the flower structures that resemble female bees, leading toward pseudo-copulation of the male bees on the flower. Using Machine Learning, we tested whether nature is capable of besting artificial intelligence. A total of 2000 images of related bees, wasps, and <i>Ophrys</i> sp. were collected from the Google Image Repository. Unsuitable images were later filtered out manually, leaving a total of 995 images in the final selection. 80% of these images were used to build a supervised model using Logistic Regression, while the model accuracy was tested using 20% of the remaining images. Based on our results using Wolfram Mathematica, the <i>Ophrys</i> is not capable of fooling artificial intelligence. The accuracy, accuracy baseline, mean cross-entropy, Area Under ROC (receiver operating characteristic curve) curve (AUC) and the confusion matrix gave excellent image classification. However, we can now show the key points and highlights of the images and how the structures closely resemble actual bees using the SURF method. Rather than just a descriptive method, ML learning has enabled a more quantitative approach. Since this is a simple test, we encourage other scientists to adopt our approach using a larger dataset and better database samples.
DOI: 10.6084/m9.figshare.14813328.v1
title: Can plants fool artificial intelligence? Using machine learning to compare between bee orchids and bees

description: Bee orchids have long been an excellent example of how dishonest signal works in plant-animal interaction. Many studies compared the flower structures that resemble female bees, leading toward pseudo-copulation of the male bees on the flower. Using Machine Learning, we tested whether nature is capable of besting artificial intelligence. A total of 2000 images of related bees, wasps, and <i>Ophrys</i> sp. were collected from the Google Image Repository. Unsuitable images were later filtered out manually, leaving a total of 995 images in the final selection. 80% of these images were used to build a supervised model using Logistic Regression, while the model accuracy was tested using 20% of the remaining images. Based on our results using Wolfram Mathematica, the <i>Ophrys</i> is not capable of fooling artificial intelligence. The accuracy, accuracy baseline, mean cross-entropy, Area Under ROC (receiver operating characteristic curve) curve (AUC) and the confusion matrix gave excellent image classification. However, we can now show the key points and highlights of the images and how the structures closely resemble actual bees using the SURF method. Rather than just a descriptive method, ML learning has enabled a more quantitative approach. Since this is a simple test, we encourage other scientists to adopt our approach using a larger dataset and better database samples.
DOI: 10.5281/zenodo.5153910
title: Fine-scale tree spatial patterns are shaped by dispersal limitation which correlates to functional traits in a natural temperate forest - Raw SPPA data

description: Raw data allowing the reproduction of the spatial point pattern analysis detailed in Beyns, R. et al. (2021) Fine-scale tree spatial patterns are shaped by dispersal limitation which correlates to functional traits in a natural temperate forest. <em>Journal of Vegetation Science</em>.
DOI: 10.5281/zenodo.5153909
title: Fine-scale tree spatial patterns are shaped by dispersal limitation which correlates to functional traits in a natural temperate forest - Raw SPPA data

description: Raw data allowing the reproduction of the spatial point pattern analysis detailed in Beyns, R. et al. (2021) Fine-scale tree spatial patterns are shaped by dispersal limitation which correlates to functional traits in a natural temperate forest. <em>Journal of Vegetation Science</em>.
DOI: 10.5519/qd.5tyglz1h
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4887633 records)
DOI: 10.6084/m9.figshare.14925236
title: Low-rank, Orthogonally Decomposable Tensor Regression With Application to Visual Stimulus Decoding of fMRI Data

description: We consider the problem of fitting a generalized linear model with a three-dimensional image covariate, such as one obtained by functional magnetic resonance imaging (fMRI). A major challenge for fitting such a model is that the image is a multidimensional array, called a tensor, containing tens of thousands of elements, called voxels. Because there is a parameter associated with each voxel, fitting the model entails estimating tens of thousands of parameters with a typical sample size on the order of hundreds of subjects. We propose to reduce the dimensionality of the problem by imposing a low-rank assumption on the parameter tensor, which not only reduces the number of parameters to estimate, but also exploits the implicit spatial information in fMRI data. In addition, we assume the parameter tensor is orthogonally decomposable, enabling us to penalize the so-called tensor “singular values” to obtain a low-rank estimate. We develop algorithms based on projected gradient descent and the proximal gradient method to estimate the model. In simulation, the proposed methods yielded estimates with smaller squared errors than an existing method based on alternating minimization. For visual stimulus decoding of a real fMRI dataset, the proposed methods resulted in better classification accuracies than the existing method. Visualization of the estimates revealed compact clusters of voxels with relatively large values, potentially representing brain regions relevant to the task. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.14925236.v1
title: Low-rank, Orthogonally Decomposable Tensor Regression with Application to Visual Stimulus Decoding of fMRI Data

description: We consider the problem of fitting a generalized linear model with a three-dimensional image covariate, such as one obtained by functional magnetic resonance imaging (fMRI). A major challenge for fitting such a model is that the image is a multidimensional array, called a tensor, containing tens of thousands of elements, called voxels. Because there is a parameter associated with each voxel, fitting the model entails estimating tens of thousands of parameters with a typical sample size on the order of hundreds of subjects. We propose to reduce the dimensionality of the problem by imposing a low rank assumption on the parameter tensor, which not only reduces the number of parameters to estimate, but also exploits the implicit spatial information in fMRI data. In addition, we assume the parameter tensor is orthogonally decomposable, enabling us to penalize the so-called tensor “singular values” to obtain a low rank estimate. We develop algorithms based on projected gradient descent and the proximal gradient method to estimate the model. In simulation, the proposed methods yielded estimates with smaller squared errors than an existing method based on alternating minimization. For visual stimulus decoding of a real fMRI dataset, the proposed methods resulted in better classification accuracies than the existing method. Visualization of the estimates revealed compact clusters of voxels with relatively large values, potentially representing brain regions relevant to the task.
DOI: 10.15468/42jugt
title: Rediscovery of Orientotlos iishibai Sakai, 1980 (Crustacea, Decapoda, Brachyura, Leucosiidae) in Taiwan

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ng, Peter K. L., Chan, Tin-Yam (2021): Rediscovery of Orientotlos iishibai Sakai, 1980 (Crustacea, Decapoda, Brachyura, Leucosiidae) in Taiwan. ZooKeys 1053: 185-195, DOI: http://dx.doi.org/10.3897/zookeys.1053.67326, URL: http://dx.doi.org/10.3897/zookeys.1053.67326
DOI: 10.5517/ccdc.csd.cc27ps7z
title: CCDC 2076511: Experimental Crystal Structure Determination

description: Related Article: Christina Legendre, Dietmar Stalke|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27pyc8
title: CCDC 2076670: Experimental Crystal Structure Determination

description: Related Article: Eric W. Reinheimer, Hanhua Zhao, Kim R.Dunbar|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc26w7s4
title: CCDC 2051976: Experimental Crystal Structure Determination

description: Related Article: Junqing Zhang   Shuoxin Li|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27q0bb
title: CCDC 2076731: Experimental Crystal Structure Determination

description: Related Article: Zhidong Jiang, Jianhui Zhou, Haoran Zhu, Hong Liu, Yu Zhou|2021|Org.Lett.|23|4406|doi:10.1021/acs.orglett.1c01341
description: Related Article: zhou yu|2021|CSD Communication|||
DOI: 10.15468/9tddab
title: Seek and ye shall find: new species and new records of Microporella (Bryozoa, Cheilostomatida) in the Mediterranean

description: This dataset contains the digitized treatments in Plazi based on the original journal article Martino, Emanuela Di, Rosso, Antonietta (2021): Seek and ye shall find: new species and new records of Microporella (Bryozoa, Cheilostomatida) in the Mediterranean. ZooKeys 1053: 1-42, DOI: http://dx.doi.org/10.3897/zookeys.1053.65324, URL: http://dx.doi.org/10.3897/zookeys.1053.65324
DOI: 10.5517/ccdc.csd.cc27xt11
title: CCDC 2083263: Experimental Crystal Structure Determination

description: Related Article: Yao Sun, Mingjun Chi, Muhammad Sohail Bashir, Yusong Wang, Muhammad Qasim|2021|New J.Chem.|45|13280|doi:10.1039/D1NJ02437J
DOI: 10.5517/ccdc.csd.cc27y6s6
title: CCDC 2083658: Experimental Crystal Structure Determination

description: Related Article: Duy T. M. Chung, Phuong Vy Tran, Khiem Chau Nguyen, Pengzhi Wang, Jonathan S. Lindsey|2021|New J.Chem.|45|13302|doi:10.1039/D1NJ02469H
DOI: 10.5517/ccdc.csd.cc27y6t7
title: CCDC 2083659: Experimental Crystal Structure Determination

description: Related Article: Duy T. M. Chung, Phuong Vy Tran, Khiem Chau Nguyen, Pengzhi Wang, Jonathan S. Lindsey|2021|New J.Chem.|45|13302|doi:10.1039/D1NJ02469H
DOI: 10.5517/ccdc.csd.cc27y6v8
title: CCDC 2083660: Experimental Crystal Structure Determination

description: Related Article: Duy T. M. Chung, Phuong Vy Tran, Khiem Chau Nguyen, Pengzhi Wang, Jonathan S. Lindsey|2021|New J.Chem.|45|13302|doi:10.1039/D1NJ02469H
DOI: 10.5517/ccdc.csd.cc27y6w9
title: CCDC 2083661: Experimental Crystal Structure Determination

description: Related Article: Duy T. M. Chung, Phuong Vy Tran, Khiem Chau Nguyen, Pengzhi Wang, Jonathan S. Lindsey|2021|New J.Chem.|45|13302|doi:10.1039/D1NJ02469H
DOI: 10.5517/ccdc.csd.cc27y6xb
title: CCDC 2083662: Experimental Crystal Structure Determination

description: Related Article: Duy T. M. Chung, Phuong Vy Tran, Khiem Chau Nguyen, Pengzhi Wang, Jonathan S. Lindsey|2021|New J.Chem.|45|13302|doi:10.1039/D1NJ02469H
DOI: 10.5281/zenodo.5117798
title: GWTC-2.1: Deep extended-catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Sensitivity of search pipelines to simulated signals

description: Results of search pipelines (GSTLAL, MBTA, PYCBC, PYCBC BBH) used to identify candidates in GWTC-2.1 on a set of simulated signals corresponding to binary neutron star (BNS), neutron star black holes (NSBH), and binary black holes (BBH) signals. Additionally, we include a README file which provides information on how to read these files.
description: LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain.
DOI: 10.15468/ukdq99
title: Review of the Nordic Gymnocheta Robineau-Desvoidy (Diptera, Tachinidae) with report of two species new to Europe

description: This dataset contains the digitized treatments in Plazi based on the original journal article Pohjoismaeki, Jaakko, Bergstroem, Christer (2021): Review of the Nordic Gymnocheta Robineau-Desvoidy (Diptera, Tachinidae) with report of two species new to Europe. ZooKeys 1053: 145-184, DOI: http://dx.doi.org/10.3897/zookeys.1053.52761, URL: http://dx.doi.org/10.3897/zookeys.1053.52761