cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21648369.v1
title: Cost-based feature selection for network model choice

description: Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate Bayesian computation. In practice, the cost of computing informative features also needs to be considered. This is particularly important for networks because the computational costs of individual features can span several orders of magnitude. We addressed this issue for the network model selection problem using two approaches. First, we adapted nine feature selection methods to account for the cost of features. We show for two classes of network models that the cost can be reduced by two orders of magnitude without considerably affecting classification accuracy (proportion of correctly identified models). Second, we selected features using pilot simulations with smaller networks. This approach reduced the computational cost by a factor of 50 without affecting classification accuracy. To demonstrate the utility of our approach, we applied it to three different yeast protein interaction networks and identified the best-fitting duplication divergence model. Supplemental materials, including computer code to reproduce our results, are available online.
DOI: 10.5281/zenodo.7555480
title: clariallabes_teugelsi_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7555479
title: clariallabes_teugelsi_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7555084
title: Arabisopsis 3D+T dataset

description: The dataset gathers points of 5 arabidopsis acquired at consecutive points in time (twice a day). The point cloud is generated using space carving based on 72 images which are acquired using a robotic arm moving around the plant. The soil base is removed using a height threshold. The plant is centered at (0,0,0) and voxel-based downsampled to a unified scale. For most plants, a topological skeleton is extracted and stored as a graph in the file: "skeleton_XXX_connected.txt"<br> And then the skeleton is segmented into different edges. The segmented skeletons' nodes are stored in "skeleton_XXX_noted.csv" This dataset was collected as part of the ROMI project. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 773875 The link to ROMI project: https://romi-project.eu/
DOI: 10.5281/zenodo.7555083
title: Arabisopsis 3D+T dataset

description: The dataset gathers points of 5 arabidopsis acquired at consecutive points in time (twice a day). The point cloud is generated using space carving based on 72 images which are acquired using a robotic arm moving around the plant. The soil base is removed using a height threshold. The plant is centered at (0,0,0) and voxel-based downsampled to a unified scale. For most plants, a topological skeleton is extracted and stored as a graph in the file: "skeleton_XXX_connected.txt"<br> And then the skeleton is segmented into different edges. The segmented skeletons' nodes are stored in "skeleton_XXX_noted.csv" This dataset was collected as part of the ROMI project. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 773875 The link to ROMI project: https://romi-project.eu/
DOI: 10.25625/m9blkn/nttmmk
title: EFForTS-LA_Z02-Arthropods_canopy+forestfloor_density+biomass_20230120.tab

description: Data: Arthropods from Canopy and Leaf Litter, Landscape Assessment
DOI: 10.25625/m9blkn/wlla9p
title: Z02 LA Sampling and Sorting_Methods Brief.pdf

description: Methods description
DOI: 10.25625/m9blkn/zm8uaj
title: Meta Data to EFForTS-LA_Z02-Arthropods_canopy+forestfloor_density+biomass_20230120.csv .txt

description: Meta Data description
DOI: 10.25625/m9blkn
title: Landscape Assessment (LA) - Z02 WP3 Canopy Leaf Litter Arthropods - Abundance, Size and Biomasses

description: Arthropods from Canopy and Leaf Litter Sampling, sorted to 15 taxa, collected numbers in total and per area (density), average body sizes, wet weight, and total biomass per taxon*plot*method combination. Methods in file Z02 LA Sampling and Sorting_Methods Brief.pdf Meta Data Description in Meta Data to EFForTS-LA_Z02-Arthropods_canopy+forestfloor_density+biomass_20230120.csv .txt
DOI: 10.5281/zenodo.7555485
title: Annotated dataset for the semantic segmentation of radishes

description: This folder contains pictures of radishes collected on the PMF experimental field during Spring 2017. There are two kinds of labeled images in the following folders: <br> - human annotations: human annotators draw polygons around each plant and those were then refined using an active contours algorithm.<br> - machine annotations: An SVM trained on the human annotations was used to produce labeled images. Images with bad segmentation were manually discarded. Each of these folder contains an images folder containing original pictures and a labels folder containing binary segmentation masks.
DOI: 10.5281/zenodo.7555484
title: Annotated dataset for the semantic segmentation of radishes

description: This folder contains pictures of radishes collected on the PMF experimental field during Spring 2017. There are two kinds of labeled images in the following folders: <br> - human annotations: human annotators draw polygons around each plant and those were then refined using an active contours algorithm.<br> - machine annotations: An SVM trained on the human annotations was used to produce labeled images. Images with bad segmentation were manually discarded. Each of these folder contains an images folder containing original pictures and a labels folder containing binary segmentation masks.
DOI: 10.5281/zenodo.7555492
title: clariallabes_teugelsi_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7555491
title: clariallabes_teugelsi_decontaminated_gx.fasta.gz

description: .
DOI: 10.7916/945c-9w77
title:  Sulfate reduction accelerates groundwater arsenic contamination even in aquifers with abundant iron oxides: Model files
DOI: 10.5519/qd.qsn5sdn9
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (214 records)
DOI: 10.5281/zenodo.5256678
title: AG's news corpus (AGNEWS)

description: AG’s news corpus (AGNEWS): This AG’s corpus of news articles was collected from the web. The whole corpus contains 496,835 categorized news articles from more than 2000 news sources. Four largest classes (World, Sports, Business and Sci/Tech) from this corpus were chosen to construct the dataset used in the experiments, using only the title and description fields
description: {"references": ["https://doi.org/10.1016/j.ipm.2020.102481"]}
DOI: 10.5281/zenodo.5256677
title: AG's news corpus (AGNEWS)

description: AG’s news corpus (AGNEWS): This AG’s corpus of news articles was collected from the web. The whole corpus contains 496,835 categorized news articles from more than 2000 news sources. Four largest classes (World, Sports, Business and Sci/Tech) from this corpus were chosen to construct the dataset used in the experiments, using only the title and description fields The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition. The .zip contains all aforementioned files + the tfidf representation in the CSR matrix format.
description: {"references": ["https://doi.org/10.1016/j.ipm.2020.102481"]}
DOI: 10.5281/zenodo.7555424
title: AG's news corpus (AGNEWS)

description: AG’s news corpus (AGNEWS): This AG’s corpus of news articles was collected from the web. The whole corpus contains 496,835 categorized news articles from more than 2000 news sources. Four largest classes (World, Sports, Business and Sci/Tech) from this corpus were chosen to construct the dataset used in the experiments, using only the title and description fields The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_&lt;k&gt;.pkl: pandas DataFrame with k-cross validation partition. The .zip contains all aforementioned files + the tfidf representation in the CSR matrix format.
description: {"references": ["https://doi.org/10.1016/j.ipm.2020.102481"]}
DOI: 10.6084/m9.figshare.21685127
title: Computationally Efficient Learning of Gaussian Linear Structural Equation Models with Equal Error Variances

description: This study develops a computationally efficient and statistically consistent learning algorithm for large-scale Gaussian linear structural equation models with equal error variances. The novelty of the proposed algorithm is that it applies a hypothesis test rather than a score when recovering the directions of the edges. Specifically, the proposed algorithm estimates the topological layers using a Z-test. Subsequently, it estimates the parents of each node using a consistent conditional independence test. This study proves that the proposed topological layer estimation approach is asymptotically consistent. It further shows that the proposed method is computationally more efficient than existing score-based algorithms for Gaussian linear SEMs by comparing the computational complexities. It is confirmed through synthetic and real-world data that the proposed algorithm is consistent and computationally much faster than the state-of-the-art large-scale Gaussian linear SEM learning algorithms, such as the USF, USB, TD, and HGSM algorithms. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21685127.v1
title: Computationally Efficient Learning of Gaussian Linear Structural Equation Models with Equal Error Variances

description: This study develops a computationally efficient and statistically consistent learning algorithm for large-scale Gaussian linear structural equation models with equal error variances. The novelty of the proposed algorithm is that it applies a hypothesis test rather than a score when recovering the directions of the edges. Specifically, the proposed algorithm estimates the topological layers using a Z-test. Subsequently, it estimates the parents of each node using a consistent conditional independence test. This study proves that the proposed topological layer estimation approach is asymptotically consistent. It further shows that the proposed method is computationally more efficient than existing score-based algorithms for Gaussian linear SEMs by comparing the computational complexities. It is confirmed through synthetic and real-world data that the proposed algorithm is consistent and computationally much faster than the state-of-the-art large-scale Gaussian linear SEM learning algorithms, such as the USF, USB, TD, and HGSM algorithms. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21899499
title: ppc_isoform_norminal_pvalues.robj

description: This file contains full summary statistics for iPSC-PPC isoform usage.
DOI: 10.6084/m9.figshare.21899499.v1
title: ppc_isoform_norminal_pvalues.robj

description: This file contains full summary statistics for iPSC-PPC isoform usage.
DOI: 10.6084/m9.figshare.21899496
title: ppc_gene_nominal_pvalues.robj

description: This file contains full summary statistics for iPSC-PPC gene expression.
DOI: 10.6084/m9.figshare.21899496.v1
title: ppc_gene_nominal_pvalues.robj

description: This file contains full summary statistics for iPSC-PPC gene expression.
DOI: 10.6084/m9.figshare.21932289.v1
title: Data for poverty mapping in Pakistan with ensemble deep learning

description: This contains data and scripts used in the paper entitled "High resolution mapping of rural poverty in Pakistan with ensemble deep learning". The "README.me" file provides additional information about the scripts and underlying data.
DOI: 10.6084/m9.figshare.21932289
title: Data for poverty mapping in Pakistan with ensemble deep learning

description: This contains data and scripts used in the paper entitled "High resolution mapping of rural poverty in Pakistan with ensemble deep learning". The "README.me" file provides additional information about the scripts and underlying data.
DOI: 10.5281/zenodo.7542663
title: ePol: Impacts of Diversity on Software Teams Dataset

description: Data set that has all the records of the activities carried out in the ePol Project, with their respective attributes. Additionally, it also includes code that does a cleanup according to some criteria. After the cleaning performed by the aforementioned code, the results are also available in the "dataset_epol_result.csv" file. For more information read: SOUZA, NATAN. MASSONI, TIAGO. SARMENTO, CAMILLA. <strong>Impactos da Diversidade em Equipes de Software</strong>, 2023.
DOI: 10.5517/ccdc.csd.cc2d39g9
title: CCDC 2207710: Experimental Crystal Structure Determination

description: Related Article: Ziting Hou, Yanji Huang, Yushan Ruan, Han Xu, Yu Tan, Li-Rong Lin, Zhen-yi Wu|2023|RSC Advances|13|2269|doi:10.1039/D2RA07133A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d39hb
title: CCDC 2207711: Experimental Crystal Structure Determination

description: Related Article: Ziting Hou, Yanji Huang, Yushan Ruan, Han Xu, Yu Tan, Li-Rong Lin, Zhen-yi Wu|2023|RSC Advances|13|2269|doi:10.1039/D2RA07133A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d39jc
title: CCDC 2207712: Experimental Crystal Structure Determination

description: Related Article: Ziting Hou, Yanji Huang, Yushan Ruan, Han Xu, Yu Tan, Li-Rong Lin, Zhen-yi Wu|2023|RSC Advances|13|2269|doi:10.1039/D2RA07133A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d39kd
title: CCDC 2207713: Experimental Crystal Structure Determination

description: Related Article: Ziting Hou, Yanji Huang, Yushan Ruan, Han Xu, Yu Tan, Li-Rong Lin, Zhen-yi Wu|2023|RSC Advances|13|2269|doi:10.1039/D2RA07133A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d3j02
title: CCDC 2207913: Experimental Crystal Structure Determination

description: Related Article: Xian-He Zhao, Le-Le Meng, Xiao-Tao Liu, Peng-Fei Shu, Cheng Yuan, Xian-Tao An, Tian-Xi Jia, Qi-Qiong Yang, Xiang Zhen, Chun-An Fan|2022|J.Am.Chem.Soc.|145|311|doi:10.1021/jacs.2c09985
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d3j13
title: CCDC 2207914: Experimental Crystal Structure Determination

description: Related Article: Xian-He Zhao, Le-Le Meng, Xiao-Tao Liu, Peng-Fei Shu, Cheng Yuan, Xian-Tao An, Tian-Xi Jia, Qi-Qiong Yang, Xiang Zhen, Chun-An Fan|2022|J.Am.Chem.Soc.|145|311|doi:10.1021/jacs.2c09985
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7314591
title: Imaging the Breaking of Electrostatic Dams in Graphene for Ballistic and Viscous Fluids

description: These datasets can be used to reproduce the experimental and theoretical results in the manuscript "Imaging the Breaking of Electrostatic Dams in Graphene for Ballistic and Viscous Fluids," and corresponding supplementary material. "STP-measurement1" and "STP-measurement2" contain the raw scanning tunneling potentiometry (STP) data used to generate Figures 1-4 in the main text and Figures S2-5 in the supplement. Each ".dat" file contains the tunneling I-V curve used to solve for the electrochemical potential at a single pixel in each STP dataset. "Estimation of c_G.nb" contains the raw Mathematica code to generate Figure 5 in the main text and Figures S7-8 in the supplement. "device-resistance_gate-sweep_T=4.5K.txt" and "device-resistance_gate-sweep_T=77K.txt" can be used to reproduce Figure S1 in the supplement.
DOI: 10.5281/zenodo.7314592
title: Imaging the Breaking of Electrostatic Dams in Graphene for Ballistic and Viscous Fluids

description: These datasets can be used to reproduce the experimental and theoretical results in the manuscript "Imaging the Breaking of Electrostatic Dams in Graphene for Ballistic and Viscous Fluids," and corresponding supplementary material. "STP-measurement1" and "STP-measurement2" contain the raw scanning tunneling potentiometry (STP) data used to generate Figures 1-4 in the main text and Figures S2-5 in the supplement. Each ".dat" file contains the tunneling I-V curve used to solve for the electrochemical potential at a single pixel in each STP dataset. "Estimation of c_G.nb" contains the raw Mathematica code to generate Figure 5 in the main text and Figures S7-8 in the supplement. "device-resistance_gate-sweep_T=4.5K.txt" and "device-resistance_gate-sweep_T=77K.txt" can be used to reproduce Figure S1 in the supplement.
DOI: 10.25919/43pt-j166
title: Parkes observations for project P1149 semester 2022OCTS_03

description: We propose continued Parkes follow-up observations of a new pulsar discovered from the ongoing SMART pulsar survey with the MWA. Initial analysis from Parkes and GMRT follow-ups suggests that the pulsar likely belongs to the rare class of low-luminosity, steep-spectrum pulsars. This class of pulsars make up ~2% of the currently-known population of long-period pulsars, and as such understanding their emission and spectral properties is vital for developing a more comprehensive picture of the Galactic population of pulsars, and also to make meaningful forecast for the detectable population of pulsars in future low-frequency surveys, including those planned with SKA-Low. The proposed observations, along with an interferometric position that we hope to obtain from GMRT imaging will allow us to obtain a timing solution on a faster time frame, thereby allowing us to determine the pulsar's spin and astrometric parameters. The observations will also yield more reliable estimates of pulsar flux density measurements, allowing us to determine the pulsar's spectral and luminosity properties.
DOI: 10.25919/58mn-b195
title: Parkes observations for project P1021 semester 2022OCTS_05

description: We propose to observe PSR J1812-15 and PSR J1831-04, a pair of long spin-period binary pulsars which both show degrees of orbitally-dependent variability. Binary pulsars are valuable objects of scientific study, allowing for multiple applications including tests of gravity, probes of the neutron star equation of state, and fossil records of stellar evolution. Long spin-period pulsars in binary systems are generally much rarer than faster-spinning `recycled’ pulsars, and represent an underexplored region of pulsar binary evolution. This is particularly true of PSR J1812-15, for which only one other pulsar (B1718-19) seems remotely comparable, while PSR J1831-04 may yet turn out to be a third member of this class Our objectives differ for each pulsar. For PSR J1812-15, a minimal low-cadence timing campaign is still required to address ongoing problems with phase connection, which may be caused by an undiagnosed glitch or other un-modelled timing effect. Beyond this, the utter uniqueness of the source warrants further monitoring in the event it displays further undocumented behaviour. For PSR J1831-04 (to date detected only once), our campaign is designed to provide the optimal chance of redetecting the pulsar through a randomised observing schedule, thereby characterising the pulsar and its binary system. If confirmed as similar to PSRs J1812 and B1718, it would contribute significantly to our understanding of this rare class of binary pulsar.
DOI: 10.5281/zenodo.7222831
title: Coupled Model Machine Learning Weights

description: The coupled model weights file contains the necessary weights to the machine learning network to run our machine learning coupled model. The regridded ERA5 file contained a year's worth of initial conditions to be used for starting forecasts or climate simulations.
DOI: 10.5281/zenodo.7555534
title: ePol: Impacts of Diversity on Software Teams Dataset

description: Data set that has all the records of the activities carried out in the ePol Project, with their respective attributes. Additionally, it also includes code that does a cleanup according to some criteria. After the cleaning performed by the aforementioned code, the results are also available in the "dataset_epol_result.csv" file. For more information read: SOUZA, NATAN. MASSONI, TIAGO. SARMENTO, CAMILLA. <strong>Impactos da Diversidade em Equipes de Software</strong>, 2023.
DOI: 10.5281/zenodo.7542662
title: ePol: Impacts of Diversity on Software Teams Dataset

description: Data set that has all the records of the activities carried out in the ePol Project, with their respective attributes. Additionally, it also includes code that does a cleanup according to some criteria. After the cleaning performed by the aforementioned code, the results are also available in the "dataset_epol_result.csv" file. For more information read: SOUZA, NATAN. MASSONI, TIAGO. SARMENTO, CAMILLA. <strong>Impactos da Diversidade em Equipes de Software</strong>, 2023.