cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4971825
title: Compilation of cumulative fission yields for Xe-131, Xe-131m, Xe-133, Xe-133m, Xe-135 and Xe-135m

description: Values on cumulative fission yields for the Xenon isotopes <sup>131</sup>Xe, <sup>131m</sup>Xe, <sup>133</sup>Xe, <sup>133m</sup>Xe, <sup>135</sup>Xe and <sup>135m</sup>Xe were compiled from the nuclear data libraries ENDF/B-VIII.0, JEFF-3.3 and JENDL-4.0, available via the Nuclear Data Services of the the International Atomic Energy Agency. This dataset lists the collected values and their uncertainties as reported.
description: In version 2.0.0, the yields for spontaneous fission of U-238 were added.
DOI: 10.5281/zenodo.4971419
title: Compilation of cumulative fission yields for Xe-131, Xe-131m, Xe-133, Xe-133m, Xe-135 and Xe-135m

description: Values on cumulative fission yields for the Xenon isotopes <sup>131</sup>Xe, <sup>131m</sup>Xe, <sup>133</sup>Xe, <sup>133m</sup>Xe, <sup>135</sup>Xe and <sup>135m</sup>Xe were compiled from the nuclear data libraries ENDF/B-VIII.0, JEFF-3.3 and JENDL-4.0, available via the Nuclear Data Services of the the International Atomic Energy Agency. This dataset lists the collected values and their uncertainties as reported.
DOI: 10.5281/zenodo.5004733
title: Compilation of cumulative fission yields for Xe-131, Xe-131m, Xe-133, Xe-133m, Xe-135 and Xe-135m

description: Values on cumulative fission yields for the Xenon isotopes <sup>131</sup>Xe, <sup>131m</sup>Xe, <sup>133</sup>Xe, <sup>133m</sup>Xe, <sup>135</sup>Xe and <sup>135m</sup>Xe were compiled from the nuclear data libraries ENDF/B-VIII.0, GEFY-6.2, JEFF-3.3, JENDL-4.0 and TENDL-2010, partly available via the Nuclear Data Services of the the International Atomic Energy Agency. This dataset lists the collected values and their uncertainties as reported.
description: In version 3.0.0, the yields for spontaneous fission of Pu-240 from GEFY-6.2 and TENDL-2010 were added.
DOI: 10.6084/m9.figshare.14818116
title: COVID-19 CT scans for CO-IRv2 model

description: The dataset has 2,482 computed tomography (CT) images where COVID-19 and normal patients are 1252 and 1230, respectively. <br>
DOI: 10.15468/uc9ys5
title: The genus Stenoterommata Holmberg, 1881 (Araneae, Pycnothelidae) in the Cerrado and Atlantic Forest from Southeastern and Central Brazil: description of four new species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ghirotto, Victor Morais, Guadanucci, José Paulo Leite, Indicatti, Rafael Prezzi (2021): The genus Stenoterommata Holmberg, 1881 (Araneae, Pycnothelidae) in the Cerrado and Atlantic Forest from Southeastern and Central Brazil: description of four new species. Zoosystema 43 (17): 311-339, DOI: 10.5252/zoosystema2021v43a17
DOI: 10.5281/zenodo.5006563
title: The Influence of Lactobacillus Acidophilus on MUC1, GAL-3, IL-1β and IL-17 Gene Expression in BALB/c Mice Stomach

description: Gene expression of MUC-1, GAL-3, IL-1 and IL-17 after treatment with low, moderate and high doses of Lactobacillus acidophilus ATCC 4356 in mice
DOI: 10.5281/zenodo.5006562
title: The Influence of Lactobacillus Acidophilus on MUC1, GAL-3, IL-1β and IL-17 Gene Expression in BALB/c Mice Stomach

description: Gene expression of MUC-1, GAL-3, IL-1 and IL-17 after treatment with low, moderate and high doses of Lactobacillus acidophilus ATCC 4356 in mice
DOI: 10.13012/b2idb-4499850_v1
title: StyleGAN2 trained on MR brain and face images: network weights

description: This repository contains the weights for two StyleGAN2 networks trained on two composite T1 and T2 weighted open-source brain MR image datasets, and one StyleGAN2 network trained on the Flickr Face HQ image dataset. Example images sampled from the respective StyleGANs are also included. The datasets themselves are not included in this repository. The weights are stored as `.pkl` files. The code and instructions to load and use the weights can be found at https://github.com/comp-imaging-sci/pic-recon . Additional details and citations can be found in the file "README.md".
DOI: 10.5281/zenodo.5008824
title: Model inputs and results from FastScape landscape evolution model runs for southern Africa

description: These are the input files and results for the models presented in the revised version of the paper "Constraining plateau uplift in southern Africa by combining thermochronology, sediment flux, topography, and landscape evolution modeling" submitted to JGR:Solid Earth in October 2020 and revised in May 2017. The corresponding code needed to run the inputs can be found here: http://doi.org/10.5281/zenodo.4150333. The readme.txt file contained here explains the included data and results, as well as simple instructions for how to run the models.
DOI: 10.5281/zenodo.4153802
title: Model inputs and results from FastScape landscape evolution model runs for southern Africa

description: These are the input files and results for the models presented in the revised version of the paper "Constraining plateau uplift in southern Africa by combining thermochronology, sediment flux, topography, and landscape evolution modeling" submitted to JGR:Solid Earth in October 2020 and revised in May 2017. The corresponding code needed to run the inputs can be found here: http://doi.org/10.5281/zenodo.4150333. The readme.txt file contained here explains the included data and results, as well as simple instructions for how to run the models.
DOI: 10.5258/soton/d1799
title: Dataset for: Disentangling the effects of ecology and life history on ectothermic temperature-size responses

description: Data used for analyses in the PhD thesis titled 'Disentangling the effects of ecology and life history on ectothermic temperature-size responses'. Species from two ectothermic taxa (Gastropoda and Lepidoptera) were included in analyses. Size measurements were taken from a combination of museum specimens and field studies, and temperature data was obtained from UK MET office.
DOI: 10.3886/e119446v105
title: COVID-19 US State Policy Database

description: For questions or comments about the database please contact:<br>Alexandra Skinner <br>skinnera@bu.edu <br>Research Fellow & Database Manager<br>Department of Health Law, Policy & Management <br>Boston University School of Public Health<br><br>Database of state policies on closures, shelter-in-place orders, housing protections, changes to Medicaid and SNAP, physical distancing closures, reopening, and more created by researchers at the Boston University School of Public Health. Policies included are state-wide directives or mandates, not guidance or recommendations. In order for a policy to be included, it must apply to the entire state. We are working quickly to go through state government websites to make the policy database as complete and accurate as possible in a rapidly changing policy context. If you use data on a given policy, we encourage you to triangulate based on additional sources of policy data and to review the source documentation to consider the coding decisions that are right for your work. Of course, please email us if you note a discrepancy so we can improve the database for everyone. State policy source documentation can be found at: tinyurl.com/statepolicysources.
description: State policies. Smallest Geographic Unit: State
description: other; Contributors: Megan Cole Brahim, Noa Krawczyk, Carolyn Hoffman, Claire Corkish, Elizabeth Ferrara, Elizabeth Long, Emily Baroni, Faith Contador, Hannah Simon, Morgan Simko, Rachel Scheckman, Sarah Brewer, Sue Kulkarni, Felicia Heykoop, Manish Patel, Aishwarya Vidyasagaran, Andrew Chiao, Cara Safon, Samantha Burkhart, Harry Jung, Victor Lee, Marina MacKinnon, Aarohi Rede, Marie Vearling, Sophie Li, Christina Wang, Tim Levengood, María Jordán P. Emma Chistolini
DOI: 10.6084/m9.figshare.14818080
title: Trypan Blue stained Cells Image Dataset

description: This Image Dataset contains 39 real world color images of trypan blue stained animal cells. Out of the 39 images, 21 were acquired using a digital camera mounted on an EclipseE200 (Nikon, Japan) bright field optical microscope with a 4x objective lens. For these images, NIH 3T3 cells were cultured, mixed on a 1:1 ratio with 0.4% trypan blue and loaded onto a Neubauer Chamber (Marienfeld, Germany). The field of view of each image is of approximately 2 mm × 1.5 mm, and its resolution of 2592×1936 pixels. The rest of the images were obtained from <b>Chan, L. L.-Y., Rice, W. L. & Qiu, J. (2020). Observation and quantification of the morphological effect of trypan blue rupturing dead or dying cells. Plos one, 15(1), e0227950.</b> Real world images were used to create a larger Synthetic Image Dataset. In order to do so, single-cell masks were obtained from each image and classified according to its state: live or dead. Also, from each image, masks of cell clusters and debris were obtained and separated from its background. The synthetic image generation process is described as follows. First, a random background was selected from the background image pool and was resized to occupy a total of 3280x2464 pixels. Second, live and dead cell masks were randomly selected from the live and dead image pools and were pasted on top of the background using random <i>x</i> and <i>y</i> coordinates. Backgrounds, as well as live and dead masks, were randomly flipped (vertically) and/or mirrored (horizontally) and modified in its brightness, contrast and sharpness when generating each image. Cell masks were also rotated. Bounding box annotations were included in individual .txt files using the YOLO format and were automatically generated simultaneously with the image synthesis. A total of 2192 training images and 250 validation images were generated. Single-cell masks and backgrounds used for the image synthesis were obtained from 24 of the 39 real world images. The remaining 15 images (8, 16, 19, 21-25 & 33-39) were used for model testing. The trained YOLOv4 cell counting model obtained a mAP<sub>50 </sub>of 87.30%, 88.47% of Precision and 90.24% of Recall in real world images. This model was used for the development of a stand-alone, portable and low-cost Automated cell-counter.
DOI: 10.6084/m9.figshare.14818017.v1
title: Semiconductor Thermionics for Next Generation Solar Cells: Photon Enhanced or Pure Thermionic?

description: The data related to various figures can be found in the xlsx files.
DOI: 10.5281/zenodo.4884775
title: Example dataset

description: <strong>Example dataset</strong> This is merely an example dataset, intended only for illustration purposes. It is used as an example in the Data Management Plan of the ERC-ZARATHUSTRA project, hosted at https://github.com/ep2lab/zarathustra-dmp It contains the README template proposed by the Data Management Plan at its 3.0.0 version.
DOI: 10.5281/zenodo.4884774
title: Example dataset

description: <strong>Example dataset</strong> This is merely an example dataset, intended only for illustration purposes. It is used as an example in the Data Management Plan of the ERC-ZARATHUSTRA project, hosted at https://github.com/ep2lab/zarathustra-dmp It contains the README template proposed by the Data Management Plan at its 3.0.0 version.
DOI: 10.6084/m9.figshare.14818017
title: Semiconductor Thermionics for Next Generation Solar Cells: Photon Enhanced or Pure Thermionic?

description: The data related to various figures can be found in the xlsx files.
DOI: 10.6084/m9.figshare.14818017.v3
title: Semiconductor Thermionics for Next Generation Solar Cells: Photon Enhanced or Pure Thermionic?

description: The data related to various figures can be found in the xlsx files.
DOI: 10.6084/m9.figshare.14818017.v2
title: Semiconductor Thermionics for Next Generation Solar Cells: Photon Enhanced or Pure Thermionic?

description: The data related to various figures can be found in the xlsx files.
DOI: 10.5517/ccdc.csd.cc25wp0s
title: CCDC 2022595: Experimental Crystal Structure Determination

description: Related Article: Quanyou Feng, Xiaojun Zheng, Hongjian Wang, He Zhang, Yue Qian, Kesheng Tan, Hongtao Cao, Linghai Xie, Wei Huang|2021|Mat.Advs.|2|4000|doi:10.1039/D1MA00181G
DOI: 10.5517/ccdc.csd.cc25x80d
title: CCDC 2023153: Experimental Crystal Structure Determination

description: Related Article: Quanyou Feng, Xiaojun Zheng, Hongjian Wang, He Zhang, Yue Qian, Kesheng Tan, Hongtao Cao, Linghai Xie, Wei Huang|2021|Mat.Advs.|2|4000|doi:10.1039/D1MA00181G
DOI: 10.5517/ccdc.csd.cc269jgj
title: CCDC 2034947: Experimental Crystal Structure Determination

description: Related Article: Masashi Mamada, Hajime Nakanotani, Chihaya Adachi|2021|Mat.Advs.|2|3906|doi:10.1039/D0MA00756K
DOI: 10.5517/ccdc.csd.cc269jhk
title: CCDC 2034948: Experimental Crystal Structure Determination

description: Related Article: Masashi Mamada, Hajime Nakanotani, Chihaya Adachi|2021|Mat.Advs.|2|3906|doi:10.1039/D0MA00756K
DOI: 10.5517/ccdc.csd.cc269jjl
title: CCDC 2034949: Experimental Crystal Structure Determination

description: Related Article: Masashi Mamada, Hajime Nakanotani, Chihaya Adachi|2021|Mat.Advs.|2|3906|doi:10.1039/D0MA00756K
DOI: 10.5517/ccdc.csd.cc269jkm
title: CCDC 2034950: Experimental Crystal Structure Determination

description: Related Article: Masashi Mamada, Hajime Nakanotani, Chihaya Adachi|2021|Mat.Advs.|2|3906|doi:10.1039/D0MA00756K
DOI: 10.5517/ccdc.csd.cc269jln
title: CCDC 2034951: Experimental Crystal Structure Determination

description: Related Article: Masashi Mamada, Hajime Nakanotani, Chihaya Adachi|2021|Mat.Advs.|2|3906|doi:10.1039/D0MA00756K
DOI: 10.5517/ccdc.csd.cc269jmp
title: CCDC 2034952: Experimental Crystal Structure Determination

description: Related Article: Masashi Mamada, Hajime Nakanotani, Chihaya Adachi|2021|Mat.Advs.|2|3906|doi:10.1039/D0MA00756K
DOI: 10.5517/ccdc.csd.cc269jnq
title: CCDC 2034953: Experimental Crystal Structure Determination

description: Related Article: Masashi Mamada, Hajime Nakanotani, Chihaya Adachi|2021|Mat.Advs.|2|3906|doi:10.1039/D0MA00756K
DOI: 10.1184/r1/14814813
title: Paint Project

description: <b>tubeBackMass</b>: The background level information for each of the Tenax tubes after cleaning.<br><b>'X'_ptr</b>: (sometimes with _background) (where "X" represents the experiment name for one of 12 experiments). The PTR-MS measurements for each of the 12 paint experiments with the exact experiment specified as the "X" at the start of the name. These experiments took place over the course of the two-day drying period following paint application. If the name has background in it, then it is a blank background measurement with the PTR-MS prior to the start of that experiment.<br><br><b>headspace_'X'</b>: (sometimes with _background) (where "X" represents the experiment name for one of 12 experiments). The headspace PTR-MS measurements for each of the 12 paint experiments with the exact experiment specified as the "X" at the start of the name. These experiments were conducted by placing the inlet to the PTR-MS over the headspace of the paints for ~15 minutes. If the name has background in it, then it is a blank background measurement with the PTR-MS prior to the start of that experiment.<br><br><b>'X'dataStruct.mat</b>: (where "X" represents the experiment name for one of 13 experiments). These are the .mat files which can be uploaded into matlab. They all have the same structure but for different experiments as specified by their names.<br>
DOI: 10.1184/r1/14773374.v1
title: RAMP and EJ Datasets

description: This project contains two datasets as described below:<br><b>RebeccaQuery20190123</b>: has hourly averaged RAMP data for all sites specified by site LOCATION (not specific RAMP) from April 2017 through May 2018. For each numbered site thre is CO, SO2, NO2, and PM2.5 data, not all columns for each numbered site are available for all the times. Data is only included when available, this is data corrected into concentration values (ppb for gases, ug/m^3 for PM2.5).<br><b>EJ spreadsheet</b>: all 42 sites, hours of sampling by RAMPS, latitude, longitude, % of the population in the census block belonging to a minority group, % of the population living below the poverty line (column name: "%poor"), mean PM2.5 at the site, standard deviation of PM2.5 measurements, median PM2.5 measurement at the site, and same information (mean, standard devaition, and median) for NO2 at each site.
DOI: 10.1184/r1/14773374
title: RAMP and EJ Datasets

description: This project contains two datasets as described below:<br><b>RebeccaQuery20190123</b>: has hourly averaged RAMP data for all sites specified by site LOCATION (not specific RAMP) from April 2017 through May 2018. For each numbered site thre is CO, SO2, NO2, and PM2.5 data, not all columns for each numbered site are available for all the times. Data is only included when available, this is data corrected into concentration values (ppb for gases, ug/m^3 for PM2.5).<br><b>EJ spreadsheet</b>: all 42 sites, hours of sampling by RAMPS, latitude, longitude, % of the population in the census block belonging to a minority group, % of the population living below the poverty line (column name: "%poor"), mean PM2.5 at the site, standard deviation of PM2.5 measurements, median PM2.5 measurement at the site, and same information (mean, standard devaition, and median) for NO2 at each site.
DOI: 10.1184/r1/14773335
title: COVID air quality RAMP datasets

description: There are two data files uploaded into this project a description of each is shown here:<br><br><b>Rebecca_Spring2020_all_1hr</b>: (Spring 2020 pre- and post- covid closure data) first column has the date and time, on an hourly basis from March 1st 2020 through May 14th 2020, each column after that has a different RAMP location, data that was available is filled in in the appropriate location and time.<br><b>Mar2019_all_1hr</b>: (pre covid data) under the tab labelled Mar2019_all_1hr, first column has the date and time, on an hourly basis from March 1th 2019 through March 31st 2019, each column after that has a different RAMP location, data that was available is filled in in the appropriate location and time.<br>
DOI: 10.6084/m9.figshare.14550870
title: User Experience in Social Robots

description: Social robots are increasingly penetrating our daily lives. They are used in various domains, such as healthcare, education, home use and manufacturing. However, introducing this technology for use into conventional environments is not trivial. For users to accept social robots, a positive user experience is vital and it should be considered as a critical part of the robots development process. This may potentially lead to excessive use of social robots and strengthen their diffusion in society<b>. </b>The goal of this study is to summarize the extant literature that is focused on user experience in social robots, and to identify the challenges and benefits of UX evaluation in social robots. To achieve this goal, the authors carried out a systematic literature review that relies on the guidelines for secondary studies in software engineering.<b> </b>Our findings revealed that the most common methods to evaluate UX in social robots are interviews and surveys. UX evaluations were found out to be beneficial in providing early feedback and consequently in handling errors at an early stage. However, despite the importance of UX in social robots, robot developers often neglect to set UX goals due to lack of knowledge or lack of time. This study emphasizes the need for robot developers to acquire the required theoretical and practical knowledge on how to perform a successful UX evaluation.<br>
DOI: 10.6084/m9.figshare.14550870.v1
title: User Experience in Social Robots

description: Social robots are increasingly penetrating our daily lives. They are used in various domains, such as healthcare, education, home use and manufacturing. However, introducing this technology for use into conventional environments is not trivial. For users to accept social robots, a positive user experience is vital and it should be considered as a critical part of the robots development process. This may potentially lead to excessive use of social robots and strengthen their diffusion in society<b>. </b>The goal of this study is to summarize the extant literature that is focused on user experience in social robots, and to identify the challenges and benefits of UX evaluation in social robots. To achieve this goal, the authors carried out a systematic literature review that relies on the guidelines for secondary studies in software engineering.<b> </b>Our findings revealed that the most common methods to evaluate UX in social robots are interviews and surveys. UX evaluations were found out to be beneficial in providing early feedback and consequently in handling errors at an early stage. However, despite the importance of UX in social robots, robot developers often neglect to set UX goals due to lack of knowledge or lack of time. This study emphasizes the need for robot developers to acquire the required theoretical and practical knowledge on how to perform a successful UX evaluation.<br>
DOI: 10.5281/zenodo.4313029
title: Cleveland

description: Cleveland RadioSet object generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://www.orcestra.ca/radiogenomics/10.5281/zenodo.4313029 <strong>Disclaimer</strong> The Cleveland data were generated and shared by the Broad Institute CTD^2 Center. The Haibe-Kains Lab has re-annotated the data to maximize overlap with other radiogenomic datasets. <strong>Data Usage Policy</strong> The CTD^2 releases data in accordance with their data release policy. <strong>Please cite the following when using these data</strong> Yard, B., Adams, D., Chie, E. et al. A genetic basis for the variation in the vulnerability of cancer to DNA damage. Nat Commun 7, 11428 (2016).
DOI: 10.5281/zenodo.4313028
title: Cleveland

description: Cleveland RadioSet object generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://www.orcestra.ca/radiogenomics/10.5281/zenodo.4313029 <strong>Disclaimer</strong> The Cleveland data were generated and shared by the Broad Institute CTD^2 Center. The Haibe-Kains Lab has re-annotated the data to maximize overlap with other radiogenomic datasets. <strong>Data Usage Policy</strong> The CTD^2 releases data in accordance with their data release policy. <strong>Please cite the following when using these data</strong> Yard, B., Adams, D., Chie, E. et al. A genetic basis for the variation in the vulnerability of cancer to DNA damage. Nat Commun 7, 11428 (2016).
DOI: 10.5281/zenodo.4312144
title: MetaGxPancreas

description: Clinical genomics (MetaGxPancreas) object generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://www.orcestra.ca/clinical/10.5281/zenodo.4312144 <strong>Disclaimer</strong> The MetaGxPancreas data were re-processed and shared by the Haibe-Kains Lab (University Health Network) and the Waldron Lab (CUNY Graduate School of Public Health & Health Policy). The Haibe-Kains Lab has re-annotated the data to maximize overlap with other clinical datasets. <strong>Data Usage Policy</strong> The data is under Creative Commons Attribution 4.0 International License provided. Source: https://www.nature.com/articles/s41598-019-45165-4#rightslink <strong>Please cite the following when using these data</strong> Gendoo, D.M.A., Zon, M., Sandhu, V. et al. MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature. Sci Rep 9, 8770 (2019). https://doi.org/10.1038/s41598-019-45165-4.