cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/35f4ddd2c0a548628127ff029148a889
title: Sevilleta LTER Vegetation Sample Catalog- Ground Samples for Chemical Analysis

description: Several long-term studies at the Sevilleta LTER measure net primary production (NPP) across ecosystems and treatments. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. The NPP weight data (SEV 157) is obtained by harvesting a series of covers for species observed during plot sampling. These species are always harvested from habitat comparable to the plots in which they were recorded. This data is then used to make volumetric measurements of species and build regressions correlating biomass and volume. From these calculations, seasonal biomass and seasonal and annual NPP are determined. These sampled are then vouchered for use to do analyses of inorganic and organic components such as carbon, nitrogen, and phosphorous as well as and other macro and micro nutrients and organic components such as cellulose and lignin.
DOI: 10.25505/fiz.icsd.cc287plt
title: CSD 2092766: Experimental Crystal Structure Determination

description: Related Article: T. Yamada, N. Fujita, F. Labib|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|638|doi:10.1107/S2052520621006715
DOI: 10.25505/fiz.icsd.cc287pmv
title: CSD 2092767: Experimental Crystal Structure Determination

description: Related Article: T. Yamada, N. Fujita, F. Labib|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|638|doi:10.1107/S2052520621006715
DOI: 10.5517/ccdc.csd.cc27tmwl
title: CCDC 2080220: Experimental Crystal Structure Determination

description: Related Article: T.E. Gorelik, S. Habermehl, A.A. Shubin, T. Gruene, K. Yoshida, P. Oleynikov, U. Kaiser, M.U. Schmidt|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|662|doi:10.1107/S2052520621006806
DOI: 10.5517/ccdc.csd.cc2524t7
title: CCDC 1998068: Experimental Crystal Structure Determination

description: Related Article: Evyenia Shaili, Marίa J. Romero, Luca Salassa, Julie A. Woods, Jennifer S. Butler, Isolda Romero-Canelón, Guy Clarkson, Abraha Habtemariam, Peter J. Sadler, Nicola J. Farrer|2021|Dalton Trans.|50|10593|doi:10.1039/D1DT01730F
DOI: 10.5517/ccdc.csd.cc28czm8
title: CCDC 2096890: Experimental Crystal Structure Determination

description: Related Article: Qingyang Wang, Richard A. Manzano, Hendrik Tinnermann, Simon Sung, Baptiste Leforestier, Tobias Krämer, Rowan D. Young|2021|Angew.Chem.,Int.Ed.|60|18168|doi:10.1002/anie.202104130
DOI: 10.5517/ccdc.csd.cc28czn9
title: CCDC 2096891: Experimental Crystal Structure Determination

description: Related Article: Qingyang Wang, Richard A. Manzano, Hendrik Tinnermann, Simon Sung, Baptiste Leforestier, Tobias Krämer, Rowan D. Young|2021|Angew.Chem.,Int.Ed.|60|18168|doi:10.1002/anie.202104130
DOI: 10.5517/ccdc.csd.cc28dn1d
title: CCDC 2097523: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, K. Guille|2021|CSD Communication|||
DOI: 10.6084/m9.figshare.15109362.v1
title: Sample Metadata

description: Table contains information about the subjects and the molecular generated from iPSC-PPC
DOI: 10.6084/m9.figshare.15109362.v2
title: Sample Metadata

description: Table contains information about the subjects and the molecular generated from iPSC-PPC
DOI: 10.6073/pasta/5105a1eb980a2bfa0fb189b6de31a346
title: A search index for the ecocomDP R package

description: This data package contains summary information of datasets in the ecocomDP format published by EDI and NEON. This summary information is formatted in a search index used by the search function of the ecocomDP R package.
DOI: 10.5281/zenodo.4062510
title: Machine learning datasets for epigenomic landscapes in epidermal differentiation

description: Datasets for training classification and regression models on sequence and epigenomic features. The data used here is generated from an integrative analysis of the Genomics of Gene Regulation dataset (https://www.encodeproject.org/awards/U01HG007919). For classification models, the peak files used to label genomic regions as positives or negatives can be found in `ggr.label_files.tar.gz`. For regression models, the bigwig files used for target signals can be found at the ENCODE portal. he processed dataset stored in hdf5 format files along with processing details are in the file `nn.ggr.hdf5_files.tar.gz`.
DOI: 10.5281/zenodo.4062509
title: Machine learning datasets for epigenomic landscapes in epidermal differentiation

description: Datasets for training classification and regression models on sequence and epigenomic features. The data used here is generated from an integrative analysis of the Genomics of Gene Regulation dataset (https://www.encodeproject.org/awards/U01HG007919). For classification models, the peak files used to label genomic regions as positives or negatives can be found in `ggr.label_files.tar.gz`. For regression models, the bigwig files used for target signals can be found at the ENCODE portal. he processed dataset stored in hdf5 format files along with processing details are in the file `nn.ggr.hdf5_files.tar.gz`.
DOI: 10.5281/zenodo.4062727
title: Convolutional Neural Net (CNN) models for epigenomic landscapes in epidermal differentiation - Basset architecture, classification and regression

description: Deep learning models trained on epigenomic landscapes in keratinocyte differentiation. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4062509. The file `nn.ggr.models.basset.clf.tar.gz` contains 10 cross-validated models that were pretrained using ENCODE-Roadmap trained model weights as initialization weights and also 10 cross-validated models that were initialized with random weights. Similarly, the file `nn.ggr.models.basset.regr.tar.gz` contains 10 cross-validated models that were pretrained using the classification model weights as initialization weights and also 10 cross-validated models that were initialized with random weights.
DOI: 10.5281/zenodo.4062726
title: Convolutional Neural Net (CNN) models for epigenomic landscapes in epidermal differentiation - Basset architecture, classification and regression

description: Deep learning models trained on epigenomic landscapes in keratinocyte differentiation. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4062509. The file `nn.ggr.models.basset.clf.tar.gz` contains 10 cross-validated models that were pretrained using ENCODE-Roadmap trained model weights as initialization weights and also 10 cross-validated models that were initialized with random weights. Similarly, the file `nn.ggr.models.basset.regr.tar.gz` contains 10 cross-validated models that were pretrained using the classification model weights as initialization weights and also 10 cross-validated models that were initialized with random weights.
DOI: 10.6084/m9.figshare.14916692
title: Fast Univariate Inference for Longitudinal Functional Models

description: We propose fast univariate inferential approaches for longitudinal Gaussian and non-Gaussian functional data. The approach consists of three steps: (i) fit massively univariate pointwise mixed-effects models; (ii) apply any smoother along the functional domain; and (iii) obtain joint confidence bands using analytic approaches for Gaussian data or a bootstrap of study participants for non-Gaussian data. Methods are motivated by two applications: (i) Diffusion tensor imaging measured at multiple visits along the corpus callosum of multiple sclerosis patients; and (ii) physical activity (PA) data measured by body-worn accelerometers for multiple days. An extensive simulation study indicates that model fitting and inference are accurate and much faster than existing approaches. Moreover, the proposed approach was the only one that was computationally feasible for the PA data application. Methods are accompanied by R software, though the method is “read-and-use,” as it can be implemented by any analyst who is familiar with mixed-effects model software. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.14916692.v1
title: Fast Univariate Inference for Longitudinal Functional Models

description: We propose fast univariate inferential approaches for longitudinal Gaussian and non-Gaussian functional data. The approach consists of three steps: (i) fit massively univariate pointwise mixed-effects models; (ii) apply any smoother along the functional domain; and (iii) obtain joint confidence bands using analytic approaches for Gaussian data or a bootstrap of study participants for non-Gaussian data. Methods are motivated by two applications: (i) Diffusion tensor imaging measured at multiple visits along the corpus callosum of multiple sclerosis patients; and (ii) physical activity (PA) data measured by body-worn accelerometers for multiple days. An extensive simulation study indicates that model fitting and inference are accurate and much faster than existing approaches. Moreover, the proposed approach was the only one that was computationally feasible for the PA data application. Methods are accompanied by R software, though the method is “read-and-use,” as it can be implemented by any analyst who is familiar with mixed-effects model software. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.15108726
title: Runde et al MAGs from Pediatric Crohn's Patients

description: A collection of metagenome-assembled genomes reconstructed by Joseph Runde et al. from gut metagenomes of pediatric Crohn's disease patients who went through EEN therapy.<br>Original metagenomes were initially analyzed at short-read level and made publicly available by Quince et al via https://pubmed.ncbi.nlm.nih.gov/26526081/.
DOI: 10.6084/m9.figshare.15108726.v1
title: Runde et al MAGs from Pediatric Crohn's Patients

description: A collection of metagenome-assembled genomes reconstructed by Joseph Runde et al. from gut metagenomes of pediatric Crohn's disease patients who went through EEN therapy.<br>Original metagenomes were initially analyzed at short-read level and made publicly available by Quince et al via https://pubmed.ncbi.nlm.nih.gov/26526081/.
DOI: 10.6084/m9.figshare.14642580
title: Organized Crime Dataset (InsightCrime)

description: This is the real-world input dataset (InsightCrime) used in the paper: "3M-Transformers for Event Coding on Organized Crime Domain".
DOI: 10.5281/zenodo.4050570
title: post peak weakening distance data

description: files: fr103ef_gg, fr102ek_gp, fr97hi_ei, fr100dl_gf time (seconds) friction internal axial displacement (microns) load velocity (microns/s) files: 02-2010-07-09-13-38.txt, 03-2010-07-09-15-02.txt, 04-2010-07-09-16-25.txt shear stress (mpa) fractional contact area interface stiffness (mpa/micron) time (s) files: 2010-07-28-15-45.txt, 2010-08-31-16-34.txt shear stress (mpa) time (s)
DOI: 10.5281/zenodo.4050569
title: post peak weakening distance data

description: files: fr103ef_gg, fr102ek_gp, fr97hi_ei, fr100dl_gf time (seconds) friction internal axial displacement (microns) load velocity (microns/s) files: 02-2010-07-09-13-38.txt, 03-2010-07-09-15-02.txt, 04-2010-07-09-16-25.txt shear stress (mpa) fractional contact area interface stiffness (mpa/micron) time (s) files: 2010-07-28-15-45.txt, 2010-08-31-16-34.txt shear stress (mpa) time (s)
DOI: 10.6073/pasta/ac872953e3e15f097eac1c9e2f4b948d
title: Net Primary Productivity (NPP) Weight Data at the Sevilleta National Wildlife Refuge, New Mexico

description: Several long-term studies at the Sevilleta LTER measure net primary production (NPP) across ecosystems and treatments. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. Above-ground net primary production (ANPP) is the change in plant biomass, including loss to death and decomposition, over a given period of time. To measure this change, vegetation variables, including species composition and the cover and height of individuals, are sampled up to three times yearly (winter, spring, and fall) at permanent plots within a study site. The weight data presented here is obtained by harvesting a series of covers for species observed during plot sampling. These species are always harvested from habitat comparable to the plots in which they were recorded. This data is then used to make volumetric measurements of species and build regressions correlating biomass and volume. From these calculations, seasonal biomass and seasonal and annual NPP are determined.
DOI: 10.6084/m9.figshare.13213914
title: Decomposing the Role of Rehearsal in Auditory Distraction during Serial Recall

description: According to the interference-by-process mechanism of auditory distraction, irrelevant changing sounds interfere with subvocal articulatory-motor sequencing during rehearsal. However, previous attempts to limit rehearsal with concurrent articulation and examine the residual irrelevant sound effect have limited both cumulative rehearsal as well as the initial assembly of articulatory-phonological labels. The current research decomposed rehearsal into these two levels of articulatory-phonological sequencing: silent concurrent articulation limits the availability of both serial repetition and articulatory-phonological recoding; rapid serial visual presentation allows for articulatory-phonological recoding but presents items too quickly for cumulative serial repetition. As predicted by the interference-by-process account, concurrent articulation – but not rapid serial visual presentation – reduced the irrelevant sound effect. Not only did the irrelevant sound effect persist in the face of rapid serial visual presentation, a steady-state effect also emerged. These findings indicate that irrelevant sounds interfere with both serial processing of articulatory-motor planning at the word level as well as in the formation of item-to-item associations created via serial repetition of complete items. Moreover, these findings highlight the benefits of articulatory-phonological recoding – independent of pure rehearsal – within serial recall.
DOI: 10.6084/m9.figshare.13213914.v1
title: Decomposing the Role of Rehearsal in Auditory Distraction during Serial Recall

description: According to the interference-by-process mechanism of auditory distraction, irrelevant changing sounds interfere with subvocal articulatory-motor sequencing during rehearsal. However, previous attempts to limit rehearsal with concurrent articulation and examine the residual irrelevant sound effect have limited both cumulative rehearsal as well as the initial assembly of articulatory-phonological labels. The current research decomposed rehearsal into these two levels of articulatory-phonological sequencing: silent concurrent articulation limits the availability of both serial repetition and articulatory-phonological recoding; rapid serial visual presentation allows for articulatory-phonological recoding but presents items too quickly for cumulative serial repetition. As predicted by the interference-by-process account, concurrent articulation – but not rapid serial visual presentation – reduced the irrelevant sound effect. Not only did the irrelevant sound effect persist in the face of rapid serial visual presentation, a steady-state effect also emerged. These findings indicate that irrelevant sounds interfere with both serial processing of articulatory-motor planning at the word level as well as in the formation of item-to-item associations created via serial repetition of complete items. Moreover, these findings highlight the benefits of articulatory-phonological recoding – independent of pure rehearsal – within serial recall.
DOI: 10.17632/j37rwkbtmm.1
title: Data for: Atmospheric and Ionospheric Disturbances associated with 2018 M 6.4 Hualien Earthquake

description: The atmospheric data were retrieved from NASA online database GIOVANNI to inspect Taiwanese earthquake 2018 where significant anomalies were found and ionospheric TEC data was extracted from ground based IGS receivers to monitor ionospheric conditions prior to the earthquake
DOI: 10.6084/m9.figshare.15109488
title: RAW data

description: Raw measurements
DOI: 10.5517/ccdc.csd.cc28fby0
title: CCDC 2098202: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Magnus Rueping, Stefan Moreth|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fc03
title: CCDC 2098204: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Winai Ieawsunan, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fclp
title: CCDC 2098222: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Erli Sugiono, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fcmq
title: CCDC 2098223: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Cengiz Azap, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fcnr
title: CCDC 2098224: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Cengiz Azap, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fcqt
title: CCDC 2098226: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Boris Nachtsheim, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fcrv
title: CCDC 2098227: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Boris Nachtsheim, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fcvy
title: CCDC 2098230: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Thomas Theissmann, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fcwz
title: CCDC 2098231: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Boris Nachtsheim, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fk1b
title: CCDC 2098391: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Cengiz Azap, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fk2c
title: CCDC 2098392: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Cengiz Azap, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28flgs
title: CCDC 2098435: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte Boris Nachtsheim, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fllx
title: CCDC 2098439: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Thomas Theissmann, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fly8
title: CCDC 2098450: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Boris Nachtsheim, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fmfs
title: CCDC 2098465: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Erli Sudioni, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fmgt
title: CCDC 2098466: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fmhv
title: CCDC 2098467: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Rene Königs, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fmkx
title: CCDC 2098469: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Thomas Theissmann, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fmmz
title: CCDC 2098471: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Thomas Theissmann, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fmp1
title: CCDC 2098473: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Rene Königs, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fmy9
title: CCDC 2098481: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Erli Sudioni, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fmzb
title: CCDC 2098482: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Estebaliz Merino, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fn2g
title: CCDC 2098485: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Winai Ieawsunan, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fn3h
title: CCDC 2098486: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Estebaliz Merino, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fn4j
title: CCDC 2098487: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Francisco Tato, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fn6l
title: CCDC 2098489: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Effendy, Allan H. White|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnds
title: CCDC 2098495: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Henrik Sunden, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnhw
title: CCDC 2098498: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Estebaliz Merino, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnjx
title: CCDC 2098499: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Francisco Tato, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnky
title: CCDC 2098500: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Francisco Tato, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnlz
title: CCDC 2098501: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Rene Königs, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnn1
title: CCDC 2098503: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Winai Ieawsunan, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnp2
title: CCDC 2098504: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Rene Königs, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnq3
title: CCDC 2098505: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Rene Königs, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnr4
title: CCDC 2098506: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Rene Königs, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fns5
title: CCDC 2098507: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Estebaliz Merino, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnt6
title: CCDC 2098508: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Estebaliz Merino, Magnus Rueping|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28fnv7
title: CCDC 2098509: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Magnus Rueping|2021|CSD Communication|||