cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5805651
title: Handwriting of the papal chancery and beyond: Dataset

description: <strong>Handwriting of the papal chancery and beyond: Dataset</strong> D. Stutzmann The original data was originally produced in Oct.-Nov. 2019 for the paper "Les écritures de la chancellerie pontificale dans le paysage européen (XIIe–XVe siècles)" at the conference "Les actes pontificaux. Un trésor à exploiter" held at the German Historical Institute in Paris on Nov. 28th, 2019. The text of the paper is published on HAL: https://hal.archives-ouvertes.fr/halshs-03628606. Parts of the present corpus (preliminary code and partial data) were released on Github on Jan. 4th, 2022 (https://github.com/oriflamms/RegVat_ArchNatJJ_MOM_LBA_2019). The study has been extended and deepened for a more comprehensive publication as an article in the proceedings. The analyzed corpora encompass images from Registra Vaticana (Vaticano, Archivio Apostolico, Reg. Vat.) Registers of French royal chancery (Paris, Archives Nationales, JJ series) images from Monasterium.net and Lichtbildarchiv The present dataset does not contain the images. The present dataset contains folder /_initial_corpus/: metadata on the "original corpus" that I intended to use folder /data/: (a) the original output data as produced by the Computer Vision library processing the images; (b) the data with its metadata. folder /rds/: RDS files produced as part of the analysis with the R software (cf. https://www.r-project.org/) folder /images/: figures and illustrations of the article folder /plotly/: corresponding interactive visualisations of the figures At the root, along witht this README file, the R code to produce the statistics, RDS files and images. For this library, see Nicolaou, Anguelos, A. D. Bagdanov, Marcus Liwicki, et D. Karatzas. « Sparse radial sampling lbp for writer identification ». In 2015 13th International Conference on Document Analysis and Recognition (ICDAR), 716-20, 2015., cf. https://doi.org/10.48550/arXiv.1504.06133
DOI: 10.5281/zenodo.6362361
title: Roundabout Aerial Images for Vehicle Detection

description: This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flow and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection. <strong>Roundabout (scenes)</strong> <strong>Frames</strong> <strong>Car</strong> <strong>Truck</strong> <strong>Cycle</strong> <strong>Bus</strong> <strong>Empty</strong> 1 (00001) 1,996 34,558 0 4229 0 0 2 (00002) 514 743 0 0 0 157 3 (00003-00017) 1,795 4822 58 0 0 0 4 (00018-00033) 1,027 6615 0 0 0 0 5 (00034-00049) 1,261 2248 0 550 0 81 6 (00050-00052) 5,501 180,342 1420 120 1376 0 7 (00053) 2,036 5,789 562 0 226 92 8 (00054) 1,344 1,733 222 0 150 222 <strong>Total</strong> 15,474 236,850 2,262 4,899 1,752 552 <strong>Data augmentation</strong> x4 x4 x4 x4 x4 x4 <strong>Total</strong> 61,896 947,400 9048 19,596 7,008 2,208
description: This publication is part of the I+D+i projects with reference PID2019-104793RB-C32, PIDC2021-121517-C33, funded by MCIN/AEI/10.13039/501100011033/, S2018/EMT-4362/"SEGVAUTO4.0-CM" funded by Regional Government of Madrid and "ESF and ERDF A way of making Europe".
DOI: 10.6084/m9.figshare.3761562.v2176
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.19500671
title: Record Linkage Datasets

description: This simulated dataset is a corrupted segment from the Social Security Death Master File (SSDMF) available at https://ssdmf.info/. There are 11 original datasets: ``dsxo`` where `x` runs from `1...11` and the suffix `o` stands for `original`. The sizes (number of original records) of these datasets are as follows:<br>| dataset | size ||:----------:|:----:|| ds1o | 10K || ds2o | 20K | | ds3o | 40K || ds4o | 80K || ds5o | 120K || ds6o | 160K || ds7o | 200K || ds8o | 400K || ds9o | 600K || ds10o | 800K || ds11o | 1M |<br>These original records are then corrupted via a modified version of the `dsgen` Python script by `Peter Christen`.The modified/corrupted files are saved as: ``dsxm`` where the suffix `m` stands for `modified`.The modified records plus four original replicates are concatenated and mixed up (by the Linux command tool `shuf`).The resultant datasets are named: ``dsx.0`` ``(dsx.1)`` before(after) shuffling.So, the sizes of these datasets are as follows:<br>| dataset | size ||:-------:|:----:|| ds1.1 | 50k || ds2.1 | 100k || ds3.1 | 200k || ds4.1 | 400k || ds5.1 | 600k || ds6.1 | 800k || ds7.1 | 1M || ds8.1 | 2M || ds9.1 | 3M || ds10.1 | 4M || ds11.1 | 5M |<br>Furthermore, each dataset is split into two halves to serve as input for record linkage algorithms. For example, ds1.1 is split into ds1.1.1 & ds1.1.2.<br>
DOI: 10.26009/s0uyih3e
title: TDB-17-1

description: Description: TDB-17-1: Fan-delta experiment performed in Tulane University Delta Basin. Experiment evolved under constant forcings of water (0.17 l/s), sediment (0.00017 l/s), and sea-level rise rate 0.1 (mm/hr). Experiment run time was 560 hrs of base-level rise. Experiment used a strongly cohesive sediment that had a wide grain size distribution with a median diameter of 65 microns. Experiment performed to explore autogenic sediment transport and stratigraphy with topography monitored every 1 hour of run time.
DOI: 10.5281/zenodo.5841576
title: PrivacyAlert: a dataset for image privacy prediction

description: This is a dataset for image privacy prediction. Images are from Flickr and annotated as private/public by crowd-sourcing platforms. Private images are ones that contain sensitive information and cannot be shared with everyone on social networking sites. Public images are ones that are safe to be shared with everyone. Our dataset can be used to train machine learning/deep learning models as binary classifiers to predict whether images contain sensitive information.
DOI: 10.5281/zenodo.6383041
title: Time-synchronized Energy Harvesting Traces

description: 32h of time-synchronized energy-harvesting traces from 5 different scenarios involving solar panels and piezoelectric harvesters. The data was recorded with, a measurement tool that records time-synchronized voltage and current traces from one or more energy-harvesting nodes with high rate and resolution. The <em>jogging</em> dataset comprises traces from two participants, each equipped with two piezoelectric harvester at the ankles and a solar panel at the left wrist. The two participants run together for an hour in a public park, including short walking and standing breaks. For the <em>stairs</em> dataset, we recorded traces from six solar panels that are embedded into the surface of an outdoor stair in front of a lecture hall. Over the course of one hour, numerous students pass the stairs, leading to temporary shadowing effects on some or all of the solar panels. The <em>office</em> dataset comprises traces from five solar panels mounted on the doorframe and walls of an office with fluorescent lights. During the one-hour recording, people enter and leave the office and operate the lights. The <em>cars</em> dataset contains traces from two cars. Each car is equipped with three piezoelectric harvesters mounted on the windshield, the dashboard, and in the trunk. The cars drive for two hours in convoy over a variety of roads. The <em>washer</em> dataset includes five traces from piezoelectric harvesters mounted on a WPB4700H industrial washing machine, while the machine runs a washing program with maximum load for 45 minutes.
DOI: 10.5063/f11c1v99
title: The biodiversity enhancement value of seagrass beds (including potential recovery areas) within the Florida Gulf Coast under contemporary environmental conditions (1997-2017) and two sea-level rise (SLR) scenarios (+50cm and +100cm above current).

description: These spatial datasets describe the biodiversity enhancement value (BEV) of seagrasses in the Florida Gulf Coast at the 1-hectare scale estimated from modeled scenarios for the contemporary period (mean annual summertime; 1997-2017) and two sea-level rise (SLR) forecasts (i.e., +50cm and +100cm above contemporary levels). Biodiversity enhancement value is quantified in terms the number of additional species supported by the presence of seagrass per sampling event. BEV is estimated within two depth bins (i.e., shallow waters and deep waters) using faunal species richness datasets collected by the Florida Fish and Wildlife Conservation Commision. BEV estiamtes are also parsed into separate data layers representing confirmed seagrass beds and potential recovery areas. Confirmed beds include areas that have been previously surveyed and found to have seagrass beds by Florida Fish and Wildlife Conservation Commission (FWCC) aerial surveys between 1985 and 2019. Potential recovery areas include areas where seagrass beds are absent from FWCC maps but could potentially survive if restored according to seagrass model predictions. For more information about the methods used to develop model predictions see McHenry et al. (2021).Paper Citation: McHenry, J., Rassweiler, A., Hernan, G., Uejio, C. K., Pau, S., Dubel, A. K., & Lester, S. E. (2021). Modelling the biodiversity enhancement value of seagrass beds. Diversity and Distributions, 00, 1–14. https://doi.org/10.1111/ddi.13379Interactively view these datasets through our "Seagrass Biodiversity Enhancement Explorer" App deployed using RShiny (http://jennifermc henry.shiny apps.io/seagrass_bev)
DOI: 10.5063/f1wm1bt0
title: Model predictions of the total canopy cover and species composition of seagrass beds (including potential recovery areas) within the Florida Gulf Coast under contemporary environmental conditions (1997-2017) and two sea-level rise (SLR) scenarios (+50cm and +100cm above current)

description: These spatial datasets describe the total canopy cover and species composition of seagrasses in the Florida Gulf Coast at the 1-hectare scale according to model predictions for the contemporary period (mean annual summertime; 1997-2017) and two sea-level rise scenarios (SLR), including +50cm and +100cm above contemporary levels. Model predictions are parsed into separate data layers representing confirmed seagrass beds (i.e., areas that have been previously surveyed and found to have seagrass beds by Florida Fish and Wildlife Conservation Commission aerial surveys between 1985 and 2019) and potential recovery areas (i.e., areas where seagrass beds are absent from FWCC maps but could potentially survive if restored according to seagrass model predictions). For more information about the methods used to develop model predictions see McHenry et al. (2021).Paper Citation: McHenry, J., Rassweiler, A., Hernan, G., Uejio, C. K., Pau, S., Dubel, A. K., & Lester, S. E. (2021). Modelling the biodiversity enhancement value of seagrass beds. Diversity and Distributions, 00, 1–14. https://doi.org/10.1111/ddi.13379Interactively view these datasets through our "Seagrass Biodiversity Enhancement Explorer" App deployed using RShiny (http://jenni fermchenry.shiny apps.io/seagrass_bev)
DOI: 10.5063/f1rv0m44
title: The biodiversity enhancement value of seagrass beds (including potential recovery areas) within the Florida Gulf Coast under contemporary environmental conditions (1997-2017) and two sea-level rise (SLR) scenarios (+50cm and +100cm above current).

description: These spatial datasets describe the biodiversity enhancement value (BEV) of seagrasses in the Florida Gulf Coast at the 1-hectare scale estimated from modeled scenarios for the contemporary period (mean annual summertime; 1997-2017) and two sea-level rise (SLR) forecasts (i.e., +50cm and +100cm above contemporary levels). Biodiversity enhancement value is quantified in terms the number of additional species supported by the presence of seagrass per sampling event. BEV is estimated within two depth bins (i.e., shallow waters and deep waters) using faunal species richness datasets collected by the Florida Fish and Wildlife Conservation Commision. BEV estiamtes are also parsed into separate data layers representing confirmed seagrass beds and potential recovery areas. Confirmed beds include areas that have been previously surveyed and found to have seagrass beds by Florida Fish and Wildlife Conservation Commission (FWCC) aerial surveys between 1985 and 2019. Potential recovery areas include areas where seagrass beds are absent from FWCC maps but could potentially survive if restored according to seagrass model predictions. For more information about the methods used to develop model predictions see McHenry et al. (2021).Paper Citation: McHenry, J., Rassweiler, A., Hernan, G., Uejio, C. K., Pau, S., Dubel, A. K., & Lester, S. E. (2021). Modelling the biodiversity enhancement value of seagrass beds. Diversity and Distributions, 00, 1–14. https://doi.org/10.1111/ddi.13379Interactively view these datasets through our "Seagrass Biodiversity Enhancement Explorer" App deployed using RShiny (http://jennifermc henry.shiny apps.io/seagrass_bev)
DOI: 10.34740/kaggle/dsv/3398733
title: BBC News
DOI: 10.5281/zenodo.6408919
title: Stop-codon recoding in diverse phage lineages may regulate translation of lytic genes

description: <strong>Genomes, predicted proteins, and basic information for alternatively coded phages and close standard code relatives. I have also included untrimmed lysogenic contigs for prophages that I recovered. </strong>
DOI: 10.34740/kaggle/dsv/3398750
title: US counties COVID 19 dataset
DOI: 10.5281/zenodo.6408922
title: Stop-codon recoding in diverse phage lineages may regulate translation of lytic genes

description: <strong>Genomes, predicted proteins, and basic information for alternatively coded phages and close standard code relatives. I have also included untrimmed lysogenic contigs for prophages that I recovered. </strong>
DOI: 10.34740/kaggle/dsv/3398858
title: Russian Invasion of Ukraine
DOI: 10.17632/2tx7bbnx3b.1
title: PhD thesis_videos

description: This data includes a series of videos to show some tests in my PhD thesis:

All the videos in this folder are in mp4 format.

The videos 1~15 demonstrate the experiments of finger grasping, including five objects and three surfaces.

The videos 16~19 demonstrate the experiments of dynamic hand grasping, including four objects.

The videos 20~22 demonstrate the experiments of static hand grasping, including three objects.

The videos 23~28 demonstrate the experiments of hand manipulation, including six tasks.
DOI: 10.34740/kaggle/dsv/3398910
title: FIFA World Cup 2018
DOI: 10.6084/m9.figshare.19500800
title: Data files and code.zip

description: This zip folder consists of the data set and code for our paper "Sub-MHz spectral dip in a resonator-free twisted gainmedium"
DOI: 10.6084/m9.figshare.19500800.v1
title: Data files and code.zip

description: This zip folder consists of the data set and code for our paper "Sub-MHz spectral dip in a resonator-free twisted gainmedium"
DOI: 10.6084/m9.figshare.19500356
title: Annotation of mRNA, lncRNA and small RNA of five organs (leaf, stem, root, disc flower and ray flower) in Chrysanthemum seticuspe

description: This is a collection containing the annotation and expression quantification of small RNA, mRNA and lncRNA , the fasta files of mRNA and lncRNA, and the RNAs targeted by miRNA.
DOI: 10.6084/m9.figshare.19500356.v1
title: Annotation of mRNA, lncRNA and small RNA of five organs (leaf, stem, root, disc flower and ray flower) in Chrysanthemum seticuspe

description: This is a collection containing the annotation and expression quantification of small RNA, mRNA and lncRNA , the fasta files of mRNA and lncRNA, and the RNAs targeted by miRNA.
DOI: 10.5517/ccdc.csd.cc26wkj6
title: CCDC 2052278: Experimental Crystal Structure Determination

description: Related Article: Tarun Kumar Roy, Sachin S. Gorad, Prasanta Ghorai|2022|Org.Lett.|24|1889|doi:10.1021/acs.orglett.2c00074
DOI: 10.5517/ccdc.csd.cc27fy2q
title: CCDC 2068973: Experimental Crystal Structure Determination

description: Related Article: Tarun Kumar Roy, Sachin S. Gorad, Prasanta Ghorai|2022|Org.Lett.|24|1889|doi:10.1021/acs.orglett.2c00074
DOI: 10.5517/ccdc.csd.cc28291q
title: CCDC 2087572: Experimental Crystal Structure Determination

description: Related Article: Jingyun Tan, Guanghui Zhang, Congwu Ge, Jun Liu, Long Zhou, Chao Liu, Xike Gao, Akimitsu Narita, Yingping Zou, Yunbin Hu|2022|Org.Lett.|24|2414|doi:10.1021/acs.orglett.2c00690
DOI: 10.5517/ccdc.csd.cc28kc3b
title: CCDC 2102051: Experimental Crystal Structure Determination

description: Related Article: Emma Campbell, Andrea Taladriz-Sender, Olivia I. Paisley, Alan R. Kennedy, Jacob T. Bush, Glenn A. Burley|2022|J.Org.Chem.|87|4603|doi:10.1021/acs.joc.1c02943
DOI: 10.5517/ccdc.csd.cc28kc4c
title: CCDC 2102052: Experimental Crystal Structure Determination

description: Related Article: Emma Campbell, Andrea Taladriz-Sender, Olivia I. Paisley, Alan R. Kennedy, Jacob T. Bush, Glenn A. Burley|2022|J.Org.Chem.|87|4603|doi:10.1021/acs.joc.1c02943
DOI: 10.5517/ccdc.csd.cc28kc5d
title: CCDC 2102053: Experimental Crystal Structure Determination

description: Related Article: Emma Campbell, Andrea Taladriz-Sender, Olivia I. Paisley, Alan R. Kennedy, Jacob T. Bush, Glenn A. Burley|2022|J.Org.Chem.|87|4603|doi:10.1021/acs.joc.1c02943
DOI: 10.5517/ccdc.csd.cc290tbh
title: CCDC 2115946: Experimental Crystal Structure Determination

description: Related Article: Yuji Liu, Zhiwei Zeng, Wei Huang, Jean’ne M. Shreeve, Yongxing Tang|2022|J.Org.Chem.|87|4226|doi:10.1021/acs.joc.1c03065
DOI: 10.5517/ccdc.csd.cc290tcj
title: CCDC 2115947: Experimental Crystal Structure Determination

description: Related Article: Yuji Liu, Zhiwei Zeng, Wei Huang, Jean’ne M. Shreeve, Yongxing Tang|2022|J.Org.Chem.|87|4226|doi:10.1021/acs.joc.1c03065
DOI: 10.5517/ccdc.csd.cc2914zh
title: CCDC 2116276: Experimental Crystal Structure Determination

description: Related Article: Bahadur Sk, Ezhakudiyan Ravindran, Upasana Deori, Nisha Yadav, Gyana Prakash Nanda, Pachaiyappan Rajamalli|2022|J.Mater.Chem.C|10|4886|doi:10.1039/D1TC05027C
DOI: 10.5517/ccdc.csd.cc29ybj4
title: CCDC 2143356: Experimental Crystal Structure Determination

description: Related Article: Jie Jia, Fushan Yuan, Zihao Zhang, Xuejiao Song, Fangdong Hu, Ying Xia|2022|Org.Lett.|24|1985|doi:10.1021/acs.orglett.2c00403
DOI: 10.5517/ccdc.csd.cc29ybk5
title: CCDC 2143357: Experimental Crystal Structure Determination

description: Related Article: Jie Jia, Fushan Yuan, Zihao Zhang, Xuejiao Song, Fangdong Hu, Ying Xia|2022|Org.Lett.|24|1985|doi:10.1021/acs.orglett.2c00403
DOI: 10.5517/ccdc.csd.cc2b6gmn
title: CCDC 2151171: Experimental Crystal Structure Determination

description: Related Article: Sae Young Han, Rachael K. Mow, Amymarie K. Bartholomew, Fay Ng, Michael L. Steigerwald, Xavier Roy, Colin Nuckolls, Ren A. Wiscons|2022|J.Am.Chem.Soc.|144|5263|doi:10.1021/jacs.2c01650
DOI: 10.5517/ccdc.csd.cc2b6gnp
title: CCDC 2151172: Experimental Crystal Structure Determination

description: Related Article: Sae Young Han, Rachael K. Mow, Amymarie K. Bartholomew, Fay Ng, Michael L. Steigerwald, Xavier Roy, Colin Nuckolls, Ren A. Wiscons|2022|J.Am.Chem.Soc.|144|5263|doi:10.1021/jacs.2c01650
DOI: 10.34740/kaggle/dsv/3399118
title: FIFA World Cup 2018
DOI: 10.6084/m9.figshare.19500824
title: Iota

description: Input and output of Iota phylogenetic analyses
DOI: 10.6084/m9.figshare.19500824.v1
title: Iota

description: Input and output of Iota phylogenetic analyses
DOI: 10.6084/m9.figshare.19500818
title: Alpha

description: Input and output of phylogenetic analyses for Alpha genomes
DOI: 10.6084/m9.figshare.19500818.v1
title: Alpha

description: Input and output of phylogenetic analyses for Alpha genomes
DOI: 10.6084/m9.figshare.19500812
title: Logistic Regression

description: Output of logistic regression model (Fig. 1)
DOI: 10.6084/m9.figshare.19500812.v1
title: Logistic Regression

description: Output of logistic regression model (Fig. 1)
DOI: 10.6084/m9.figshare.12845711.v1
title: Bilateral international migration flow estimates for 200 countries by sex (1990-1995 to 2015-2020).

description: Estimates of international migration flows by sex based on International Migrant Stock (IMS2020) data inputs by the United Nations and the most recent WPP (WPP2019). <br><br>Download<br>Use the download button above or directly download into R using the URL:<br><br>f <br><br>Data Details<br><br>Row for each migration corridor - period combination (198 origins x 198 destinations x 2 sexes x 4 periods + 200 origins x 200 destinations x 2 sexes x 2 periods = 470,472).<br>year0 - first year of five year periodsex - sex of persons<br>orig - origin ISO three letter country codedest - destination ISO three letter country<br> Columns for estimates based on the following migration flow estimation methods:<b><br>Stock Differencing Approaches:</b><br><br>sd_drop_neg - see for example Beine, M., Docquier, F., & Özden, Ç. (2011). Diasporas. Journal of Development Economics, 95(1), 30–41. https://doi.org/10.1016/j.jdeveco.2009.11.004<br><br>sd_rev_neg - see for example Beine, M., & Parsons, C. R. (2015). Climatic Factors as Determinants of International Migration. The Scandinavian Journal of Economics, 117(2), 723–767. https://doi.org/10.1111/sjoe.12098<br><br><b>Migration Rate Approach:</b><br><br>mig_rate - see Dennett, A. (2016). Estimating an Annual Time Series of Global Migration Flows - An Alternative Methodology for Using Migrant Stock Data. In Global Dynamics (pp. 125–142). Chichester, UK: John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118937464.ch7<br><br><b>Demographic Accounting Approaches:</b><br><br>da_min_open - see Abel, G. J. (2013). Estimating global migration flow tables using place of birth data. Demographic Research, 28(March), 505–546. https://doi.org/10.4054/DemRes.2013.28.18<br><br>da_min_closed - see Abel, G. J. (2018). Estimates of Global Bilateral Migration Flows by Gender between 1960 and 2015. International Migration Review, (Fall), imre.12327. https://doi.org/10.1111/imre.12327<br><br>da_pb_closed - see Azose, J. J., & Raftery, A. E. (2018). Estimation of emigration, return migration, and transit migration between all pairs of countries. Proceedings of the National Academy of Sciences, 201722334. https://doi.org/10.1073/PNAS.1722334116<br>
DOI: 10.6084/m9.figshare.19500827
title: Rt estimates

description: Output of Rt estimates for New Haven and New York City
DOI: 10.6084/m9.figshare.19500827.v1
title: Rt estimates

description: Output of Rt estimates for New Haven and New York City
DOI: 10.6084/m9.figshare.19500830.v1
title: Source data for figures

description: Raw data for figures
DOI: 10.6084/m9.figshare.19500830
title: Source data for figures

description: Raw data for figures
DOI: 10.6084/m9.figshare.18737768
title: country_list.csv and country_period_validation.csv files used in the bilateral international migration flow estimates by sex

description: Supplementary data files for Abel & Cohen (2022) including<br><br>1. <code>country_list.csv</code> with country codes, country names, first and last period covered and availability of reported data used for validation exercise.<br><br>2. <code>country_period_validation.csv</code> with types and sources of reported migration statistics for each country and period in each of collections used for the validation exercise.