cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48370/ofd/5vm8wl
title: Sorex araneus - 159447, Zoological collection of the Mammal Research Institute, PAS

description: Sorex araneus (Common shrew) from Zoological collection of the Mammal Research Institute, PAS. Collected 1989-09-29, in: Białowieża Forest, Poland. Material type:
DOI: 10.48370/ofd/rsnqsv
title: Sorex araneus - 127287, Zoological collection of the Mammal Research Institute, PAS

description: Sorex araneus (Common shrew) from Zoological collection of the Mammal Research Institute, PAS. Collected 1977-09-09, in: Białowieża Forest, Poland. Material type:
DOI: 10.48370/ofd/ile8sw
title: Sorex araneus - 163710, Zoological collection of the Mammal Research Institute, PAS

description: Sorex araneus (Common shrew) from Zoological collection of the Mammal Research Institute, PAS. Collected 1994-09-27, in: Białowieża Forest, Poland. Material type:
DOI: 10.25318/1310083801-eng
title: COVID-19 Rapid Test kits demand and usage

description: Percent of businesses that used COVID-19 Rapid Test kits to test on-site employees and percent of businesses that plan to test on-site employees in the next threee months by North American Industry Classification System (NAICS) for Canada and regions. Includes business' reason for not having plans to use COVID-19 Rapid Test Kits in the next three months and the percent of businesses that test employees less than once a week/once a week/twice a week/more than twice a week. Further includes percent of businesses that indicated COVID-19 Rapid Test kits as being essential personal protective equipment (PPE) or that expect to have shortages within the next 3 months.
DOI: 10.5517/ccdc.csd.cc23rm6q
title: CCDC 1959113: Experimental Crystal Structure Determination

description: Related Article: Francis Lin, Werner Kaminsky, Alex Jen|2020|CSD Communication|||
description: Related Article: Francis Lin, Kui Jiang, Werner Kaminsky, Zonglong Zhu, Alex K.-Y. Jen|2020|J.Am.Chem.Soc.|142|15246|doi:10.1021/jacs.0c07083
DOI: 10.5517/ccdc.csd.cc26875x
title: CCDC 2033698: Experimental Crystal Structure Determination

description: Related Article: Xinglei He, Huijun Li, Jiwei Wang, Yanli Li, Meijie Su, Zhouqing Xu|2021|Cryst.Growth Des.|21|1625|doi:10.1021/acs.cgd.0c01465
DOI: 10.5517/ccdc.csd.cc29kyys
title: CCDC 2132426: Experimental Crystal Structure Determination

description: Related Article: Qiang-Qiang Jia, Liang Tong, Meng-Meng Lun, Da-Wei Fu, Tie Zhang, Hai-Feng Lu|2022|Cryst.Growth Des.|22|2799|doi:10.1021/acs.cgd.2c00183
DOI: 10.5517/ccdc.csd.cc29kyzt
title: CCDC 2132427: Experimental Crystal Structure Determination

description: Related Article: Qiang-Qiang Jia, Liang Tong, Meng-Meng Lun, Da-Wei Fu, Tie Zhang, Hai-Feng Lu|2022|Cryst.Growth Des.|22|2799|doi:10.1021/acs.cgd.2c00183
DOI: 10.5281/zenodo.6515035
title: Pumilisymbium mapping dataset Oalg Oilv

description: Pumilisymbium mapping dataset of <em>Olavius algarvensis</em> and <em>Olavius ilvae </em>specimens.
DOI: 10.5281/zenodo.6515034
title: Pumilisymbium mapping dataset Oalg Oilv

description: Pumilisymbium mapping dataset of <em>Olavius algarvensis</em> and <em>Olavius ilvae </em>specimens.
DOI: 10.25318/1310083801-fra
title: Demande et utilisation des trousses de test de dépistage rapide de la COVID-19

description: Pourcentage d'entreprises qui ont utilisé des trousses de test de dépistage rapide de la COVID-19 pour tester des employés sur place et qui prévoient tester des employés sur place au cours des trois prochains mois, selon le code du Système de classification des industries de l'Amérique du Nord (SCIAN) pour le Canada et les régions. Comprend les raisons pour lesquelles les entreprises ne prévoient pas tester des employés sur place au cours de trois prochains mois et le pourcentage d'entreprises qui testent leurs employés moins d'une fois par semaine/une fois par semaine/deux fois par semaine/plus de deux fois par semaine. Des informations sur le pourcentage d'entreprises pour lesquelles les trousses de test de dépistage rapide de la COVID-19 sont essentiels et qui s'attendent à une pénurie au cours des trois prochains mois.
DOI: 10.5281/zenodo.6515015
title: Total evidence annotation Candidatus Pumilisymbium abstrusum

description: Total evidence annotation and information on manual curation of the genome from Candidatus Pumilisymbium abstrusum.
DOI: 10.5281/zenodo.6515014
title: Total evidence annotation Candidatus Pumilisymbium abstrusum

description: Total evidence annotation and information on manual curation of the genome from Candidatus Pumilisymbium abstrusum.
DOI: 10.6084/m9.figshare.19604278
title: Random Surface Covariance Estimation by Shifted Partial Tracing

description: The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even though the assumption may fail in practice. We consider a setting where the covariance structure may fail to be separable locally—either due to noise contamination or due to the presence of a nonseparable short-range dependent signal component. That is, the covariance is an additive perturbation of a separable component by a nonseparable but banded component. We introduce nonparametric estimators hinging on the novel concept of shifted partial tracing, enabling computationally efficient estimation of the model under dense observation. Due to the denoising properties of shifted partial tracing, our methods are shown to yield consistent estimators even under noisy discrete observation, without the need for smoothing. Further to deriving the convergence rates and limit theorems, we also show that the implementation of our estimators, including prediction, comes at no computational overhead relative to a separable model. Finally, we demonstrate empirical performance and computational feasibility of our methods in an extensive simulation study and on a real dataset. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19604278.v1
title: Random Surface Covariance Estimation by Shifted Partial Tracing

description: The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even though the assumption may fail in practice. We consider a setting where the covariance structure may fail to be separable locally—either due to noise contamination or due to the presence of a nonseparable short-range dependent signal component. That is, the covariance is an additive perturbation of a separable component by a nonseparable but banded component. We introduce nonparametric estimators hinging on the novel concept of shifted partial tracing, enabling computationally efficient estimation of the model under dense observation. Due to the denoising properties of shifted partial tracing, our methods are shown to yield consistent estimators even under noisy discrete observation, without the need for smoothing. Further to deriving the convergence rates and limit theorems, we also show that the implementation of our estimators, including prediction, comes at no computational overhead relative to a separable model. Finally, we demonstrate empirical performance and computational feasibility of our methods in an extensive simulation study and on a real dataset. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.6376159
title: Crop classification dataset for testing domain adaptation or distributional shift methods

description: In this upload we share processed crop type datasets from both France and Kenya. These datasets can be helpful for testing and comparing various domain adaptation methods. The datasets are processed, used, and described in this paper: https://doi.org/10.1016/j.rse.2021.112488 (arXiv version: https://arxiv.org/pdf/2109.01246.pdf). In summary, each point in the uploaded datasets corresponds to a particular location. The label is the crop type grown at that location in 2017. The 70 processed features are based on Sentinel-2 satellite measurements at that location in 2017. The points in the France dataset come from 11 different departments (regions) in Occitanie, France, and the points in the Kenya dataset come from 3 different regions in Western Province, Kenya. Within each dataset there are notable shifts in the distribution of the labels and in the distribution of the features between regions. Therefore, these datasets can be helpful for testing for testing and comparing methods that are designed to address such distributional shifts. More details on the dataset and processing steps can be found in Kluger et. al. (2021). Much of the processing steps were taken to deal with Sentinel-2 measurements that were corrupted by cloud cover. For users interested in the raw multi-spectral time series data and dealing with cloud cover issues on their own (rather than using the 70 processed features provided here), the raw dataset from Kenya can be found in Yeh et. al. (2021), and the raw dataset from France can be made available upon request from the authors of this Zenodo upload. All of the data uploaded here can be found in "CropTypeDatasetProcessed.RData". We also post the dataframes and tables within that .RData file as separate .csv files for users who do not have R. The contents of each R object (or .csv file) is described in the file "Metadata.rtf". <strong>Preferred Citation:</strong> -Kluger, D.M., Wang, S., Lobell, D.B., 2021. Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sens. Environ. 262, 112488. https://doi.org/10.1016/j.rse.2021.112488. -URL to this Zenodo post https://zenodo.org/record/6376160
description: {"references": ["Kluger D.M., Wang S., and Lobell, D.B. (2021). Two shifts for crop mapping: leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sensing of Environment. 262, 112488", "C. Yeh, C. Meng, S. Wang, A. Driscoll, E. Rozi, P. Liu, J. Lee, M. Burke, D. Lobell, and S. Ermon, \"SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning,\" in Thirty-fifth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track (Round 2), Dec. 2021."]}
DOI: 10.6084/m9.figshare.19651400
title: Generalized Low-Rank Plus Sparse Tensor Estimation by Fast Riemannian Optimization

description: We investigate a generalized framework to estimate a latent low-rank plus sparse tensor, where the low-rank tensor often captures the multi-way principal components and the sparse tensor accounts for potential model mis-specifications or heterogeneous signals that are unexplainable by the low-rank part. The framework flexibly covers both linear and generalized linear models, and can easily handle continuous or categorical variables. We propose a fast algorithm by integrating the Riemannian gradient descent and a novel gradient pruning procedure. Under suitable conditions, the algorithm converges linearly and can simultaneously estimate both the low-rank and sparse tensors. The statistical error bounds of final estimates are established in terms of the gradient of loss function. The error bounds are generally sharp under specific statistical models, for example, the sub-Gaussian robust PCA and Bernoulli tensor model. Moreover, our method achieves nontrivial error bounds for heavy-tailed tensor PCA whenever the noise has a finite 2+ε moment. We apply our method to analyze the international trade flow dataset and the statistician hypergraph coauthorship network, both yielding new and interesting findings. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19651400.v2
title: Generalized Low-Rank Plus Sparse Tensor Estimation by Fast Riemannian Optimization

description: We investigate a generalized framework to estimate a latent low-rank plus sparse tensor, where the low-rank tensor often captures the multi-way principal components and the sparse tensor accounts for potential model mis-specifications or heterogeneous signals that are unexplainable by the low-rank part. The framework flexibly covers both linear and generalized linear models, and can easily handle continuous or categorical variables. We propose a fast algorithm by integrating the Riemannian gradient descent and a novel gradient pruning procedure. Under suitable conditions, the algorithm converges linearly and can simultaneously estimate both the low-rank and sparse tensors. The statistical error bounds of final estimates are established in terms of the gradient of loss function. The error bounds are generally sharp under specific statistical models, for example, the sub-Gaussian robust PCA and Bernoulli tensor model. Moreover, our method achieves nontrivial error bounds for heavy-tailed tensor PCA whenever the noise has a finite 2+ε moment. We apply our method to analyze the international trade flow dataset and the statistician hypergraph coauthorship network, both yielding new and interesting findings. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19651400.v1
title: Generalized Low-rank plus Sparse Tensor Estimation by Fast Riemannian Optimization

description: We investigate a generalized framework to estimate a latent low-rank plus sparse tensor, where the low-rank tensor often captures the multi-way principal components and the sparse tensor accounts for potential model mis-specifications or heterogeneous signals that are unexplainable by the low-rank part. The framework flexibly covers both linear and generalized linear models, and can easily handle continuous or categorical variables. We propose a fast algorithm by integrating the Riemannian gradient descent and a novel gradient pruning procedure. Under suitable conditions, the algorithm converges linearly and can simultaneously estimate both the low-rank and sparse tensors. The statistical error bounds of final estimates are established in terms of the gradient of loss function. The error bounds are generally sharp under specific statistical models, e.g., the sub-Gaussian robust PCA and Bernoulli tensor model. Moreover, our method achieves non-trivial error bounds for heavy-tailed tensor PCA whenever the noise has a finite 2+ε moment. We apply our method to analyze the international trade flow dataset and the statistician hypergraph co-authorship network, both yielding new and interesting findings.
DOI: 10.5281/zenodo.6547341
title: The attraction of western honey bees (Apis mellifera L.) to commercially available pollen substitutes and wildflower pollen

description: Many beekeepers supplement their western honey bee (<em>Apis mellifera</em>) colonies with artificial pollen substitutes to provide colonies with adequate nutrition during times of low pollen availability or limited pollen quality. Here, we provided caged adult worker bees with three commercially available pollen substitutes (AP23, MegaBee, UltraBee) and wildflower pollen to determine which diet is most attractive to the bees. We measured diet weight change (as a proxy for consumption) and observed honey bee behavior to test for bee preferences for certain diets. We found that the bees interacted with and consumed more wildflower pollen than they did with any of the commercially available pollen substitutes. Ultimately, our data suggest that bees have a strong preference for wildflower pollen over that of commercially available pollen substitutes.
description: Data associated with publication.
DOI: 10.5281/zenodo.6547340
title: The attraction of western honey bees (Apis mellifera L.) to commercially available pollen substitutes and wildflower pollen

description: Many beekeepers supplement their western honey bee (<em>Apis mellifera</em>) colonies with artificial pollen substitutes to provide colonies with adequate nutrition during times of low pollen availability or limited pollen quality. Here, we provided caged adult worker bees with three commercially available pollen substitutes (AP23, MegaBee, UltraBee) and wildflower pollen to determine which diet is most attractive to the bees. We measured diet weight change (as a proxy for consumption) and observed honey bee behavior to test for bee preferences for certain diets. We found that the bees interacted with and consumed more wildflower pollen than they did with any of the commercially available pollen substitutes. Ultimately, our data suggest that bees have a strong preference for wildflower pollen over that of commercially available pollen substitutes.
description: Data associated with publication.
DOI: 10.5281/zenodo.6547307
title: Dataset for "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"

description: <strong>Data and scripts used to create the figures in the manuscript titled: "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"</strong>
DOI: 10.5281/zenodo.6547306
title: Dataset for "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"

description: <strong>Data and scripts used to create the figures in the manuscript titled: "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"</strong>
DOI: 10.5281/zenodo.6474726
title: MItosis DOmain Generalization Challenge 2022 (MICCAI MIDOG 2022), Training data set (PNG version)

description: This is the training dataset of the MItosis DOmain Generalization (MIDOG) challenge 2022, held in conjunction with MICCAI 2022. Please find the structured challenge description at 10.5281/zenodo.6362337. The training set consists of 405 tumor cases in total across six tumor types: Canine Lung Cancer (44 cases, scanned with 3DHistech Pannoramic Scan II) Human Breast Cancer (150 cases, scanned using three scanners, part of MIDOG2021 dataset) Canine Lymphoma (55 cases, scanned with 3DHistech Pannoramic Scan II) Human neuroendocrine tumor (55 cases, scanned with Hamamatsu NanoZoomer XR) Canine Cutaneous Mast Cell Tumor (50 cases, scanned with Aperio ScanScope CS2) Human melanoma (51 cases, scanned with Hamamatsu NanoZoomer XR) (no labels provided) From each WSI, a trained pathologist selected an area of 2mm² corresponding to approximately 10 high power fields, according to the grading scheme of Elston and Ellis. We cropped this area and provide it as PNG files in this data set due to restrictions in data set size on zenodo. Each file includes the resolution (in dots per inch, DPI) of the original scanned images. The training set contains 9501 mitotic figures (MF) and 11051 hard examples (non-mitotic figures). All annotations are provided in MS COCO JSON format and as SQLITE database (SlideRunner format).
DOI: 10.25573/data.10304306.v7
title: Monthly Rain_ACP_Vertical.xlsx

description: Monthly summaries of the Panama Canal Authority meteorological monitoring program. These data were collected from approximately 50 stations located in the Panama Canal watershed. Information about station locations can be found in the metadata in the file.<br><br>This file has been formatted vertically so that long-term trends can be easily analyzed.<br><br>Published with permission from the Panama Canal Authority.
DOI: 10.25573/data.10304303.v11
title: Monthly Rain_ACP_Horizontal.xlsx

description: Monthly summaries of the Panama Canal Authority meteorological monitoring program. These data were collected from approximately 50 stations located in the Panama Canal watershed. Information about station locations can be found in the metadata in the file.<br><br>This file has been formatted horizontally so that monthly and seasonal patterns can be easily analyzed.<br><br>Published with permission from the Panama Canal Authority.
DOI: 10.5281/zenodo.6547079
title: Dataset for Bate (2022): Dust coagulation during the early stages of star formation: molecular cloud collapse and first hydrostatic core evolution

description: This data set contains 12 smoothed particle hydrodynamics (SPH) dump files that were used to produce some of the figures in the journal paper: Bate, Matthew. R., 2022, Monthly Notices of the Royal Astronomical Society, accepted 13 May 2022 Each of the SPH dump files is from a different calculation of the early stages of star formation: the gravitational collapse of a molecular cloud core, including dust coagulation. Each SPH dump file gives the state of the SPH calculation when the maximum temperature reached 1500 K, except for the beta=0.05 cases which give the state when the maximum hydrogen number density reaches 10^{14} cm^{-3}. The calculations were each performed using 3 million SPH particles and differed by their initial rotation rate, which was parameterised by beta=0, 0.0025, 0.005, 0.01, 0.02, and 0.05 (the magnitude of the ratio of the rotational and gravitational potential energies). Dump files from calculations that include and exclude envelope turbulence are provided (both are used for Figure B1). The dump files associated with each calculation are: beta=0: B1M0123 (does not include envelope turbulence)<br> beta=0.0025: B1M2123 (does not include envelope turbulence)<br> beta=0.005: B1M5123 (does not include envelope turbulence)<br> beta=0.01: B1M1128 (does not include envelope turbulence)<br> beta=0.02: B1M2126 (does not include envelope turbulence)<br> beta=0.05: B1M5109_b05_NoEnvTurb (does not include envelope turbulence) beta=0.0: B1M0123_b0_EnvTurb<br> beta=0.0025: B1M2177_b0025_EnvTurb<br> beta=0.005: B1M5209_b005_EnvTurb<br> beta=0.01: B1M1219_b01_EnvTurb<br> beta=0.02: B1M2221_b02_EnvTurb<br> beta=0.05: B1M5321_b05_EnvTurb The SPH dump files are Fortran binary files written in big endian format and generated by the sphNG code (Benz 1990; Bate 1995; Bate & Keto 2015). They can be read, visualised, and manipulated using the free, publicly available SPLASH visualisation code (which reads sphNG dump files), written by Daniel J. Price, that can be downloaded from: http://users.monash.edu.au/~dprice/splash/ The SPLASH configuration files used to produce Figs. 10,11,12,and B1 in Bate (2022) are included with this dataset in a gzipped tar file.