cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/u6zbwt/h6p4dx
title: MonadicData.dta

description: :unav
DOI: 10.7910/dvn/u6zbwt/umiajc
title: DyadicData.dta

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DOI: 10.7910/dvn/5yqdu9/6yb0uz
title: growthis5e4_1_collisions.dat

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DOI: 10.7910/dvn/5yqdu9/cr5e5d
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DOI: 10.7910/dvn/5yqdu9/ex1h8n
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DOI: 10.7910/dvn/5yqdu9/n7alf6
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DOI: 10.7910/dvn/5yqdu9/xlncxf
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DOI: 10.7910/dvn/5yqdu9/zkclsu
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DOI: 10.7910/dvn/hhsjy8/gdtrsp
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DOI: 10.7910/dvn/hhsjy8/q8yybb
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DOI: 10.7910/dvn/hhsjy8/td5rbp
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DOI: 10.7910/dvn/hhsjy8/xcoomw
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DOI: 10.7910/dvn/hhsjy8/zmi0o2
title: img_4_3606141.jpg

description: :unav
DOI: 10.7910/dvn/u6zbwt/uaizxz
title: Within Country Models.do

description: :unav
DOI: 10.7910/dvn/u6zbwt/yawvyw
title: WithinCountryData.tab

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DOI: 10.7910/dvn/5yqdu9/dufyt7
title: growthis5e4_2_ss.02600000.origin_bins

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DOI: 10.7910/dvn/5yqdu9/f6ilvd
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DOI: 10.7910/dvn/5yqdu9/nzzbv7
title: growthis5e4_3_ss.02600000.origin_bins

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DOI: 10.7910/dvn/5yqdu9/olugrb
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DOI: 10.7910/dvn/5yqdu9/vrzi4j
title: growthis5e4_3_ss.02600000

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DOI: 10.7910/dvn/5yqdu9/wgf5ib
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DOI: 10.7910/dvn/hhsjy8/8x36wi
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DOI: 10.7910/dvn/hhsjy8/dolbzx
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DOI: 10.7910/dvn/hhsjy8/yeljjc
title: img_437_155086.jpg

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DOI: 10.7910/dvn/jrlyvn/ruoyab
title: 1_hist_data.tab

description: :unav
DOI: 10.7910/dvn/jrlyvn/udj8nx
title: 2_main results.do

description: :unav
DOI: 10.7910/dvn/jrlyvn/vz57cp
title: 1_import, tab1 and fig7 results.do

description: :unav
DOI: 10.7910/dvn/5yqdu9/erui0m
title: growthis5e4_4_ss.02600000

description: :unav
DOI: 10.7910/dvn/5yqdu9/mu4byl
title: growthis5e4_4_collisions.dat

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DOI: 10.7910/dvn/5yqdu9/r3clid
title: growthis5e4_4_ss.02600000.origin_bins

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DOI: 10.7910/dvn/5yqdu9/uqomyd
title: growthis5e4_4_ic.ss

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DOI: 10.7910/dvn/5yqdu9/w6xhqg
title: growthis5e4_5_collisions.dat

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DOI: 10.7910/dvn/hhsjy8/fnec12
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DOI: 10.7910/dvn/hhsjy8/hiia97
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DOI: 10.7910/dvn/hhsjy8/mkyqwm
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DOI: 10.7910/dvn/hhsjy8/q4ywot
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DOI: 10.7910/dvn/jrlyvn/7zdw65
title: 2_survey_data.tab

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DOI: 10.7910/dvn/jrlyvn/gvxdbx
title: 6_Figures2_to_7.R

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DOI: 10.7910/dvn/jrlyvn/nnfnil
title: 5_gen TablesA4_A5_A6_A7_A10.do

description: :unav
DOI: 10.7910/dvn/jrlyvn/q1v9di
title: 4_prep data for TablesA4_A5_A6_A7_A10.do

description: :unav
DOI: 10.7910/dvn/jrlyvn/rwz9i3
title: 3_appendix results.do

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DOI: 10.7910/dvn/5yqdu9/3smi3c
title: growthis5e4_6_ss.02600000

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DOI: 10.7910/dvn/5yqdu9/b66qym
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DOI: 10.7910/dvn/5yqdu9/cfcyvn
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DOI: 10.7910/dvn/5yqdu9/heyiia
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DOI: 10.7910/dvn/5yqdu9/ua01ba
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DOI: 10.7910/dvn/5yqdu9/vdhndn
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DOI: 10.7910/dvn/hhsjy8/awqdqn
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DOI: 10.7910/dvn/hhsjy8/dwstrl
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DOI: 10.7910/dvn/hhsjy8/qw5woi
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DOI: 10.7910/dvn/hhsjy8/tjlwib
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DOI: 10.7910/dvn/hhsjy8/zui67q
title: img_439_1239490.jpg

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DOI: 10.7910/dvn/jrlyvn/647yjh
title: log bechtel_mannino_disasterrelief.smcl

description: :unav
DOI: 10.7910/dvn/jrlyvn/cwom6y
title: 7_FiguresA1_to_A10.R

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DOI: 10.7910/dvn/jrlyvn/pokugj
title: empirical_results.txt

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DOI: 10.7910/dvn/jrlyvn/ppgftb
title: Bechtel-Mannino-Disasterrelief.pdf

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DOI: 10.7910/dvn/jrlyvn/ztclem
title: conjoint_scenariolevel.tab

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DOI: 10.7910/dvn/mchymp/pkvisk
title: nextseq_170608_rsem.genes.counts.matrix

description: Bulkseq of ECs in WT and APP/PS1 mice, x1, x2 and x3 are WT mice and x4, x5 and x6 are APP/PS1 mice
DOI: 10.7910/dvn/5yqdu9/23y2ju
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DOI: 10.7910/dvn/5yqdu9/586aqj
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DOI: 10.7910/dvn/5yqdu9/i19o0k
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DOI: 10.7910/dvn/5yqdu9/ll1ksa
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DOI: 10.7910/dvn/hhsjy8/9nuhnt
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DOI: 10.7910/dvn/hhsjy8/bodxft
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DOI: 10.7910/dvn/hhsjy8/o8gp6h
title: img_441_1814190.jpg

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DOI: 10.7910/dvn/jrlyvn/kyd8dh
title: Readme.docx

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DOI: 10.7910/dvn/3nsu5r/8iggwg
title: WT2.counts.tsv.gz

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DOI: 10.7910/dvn/3nsu5r/o8rj8y
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DOI: 10.7910/dvn/3nsu5r/uvyxyx
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DOI: 10.7910/dvn/3nsu5r/zkqhfz
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DOI: 10.7910/dvn/5yqdu9/h8zaop
title: growthis5e4_8_ic.ss

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DOI: 10.7910/dvn/5yqdu9/mlfekg
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DOI: 10.7910/dvn/5yqdu9/sjqojj
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DOI: 10.7910/dvn/5yqdu9/xu196u
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DOI: 10.7910/dvn/5yqdu9/2c3rgc
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DOI: 10.7910/dvn/5yqdu9/ec3btb
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DOI: 10.7910/dvn/5yqdu9/eqle1c
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DOI: 10.7910/dvn/5yqdu9/fypd2c
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DOI: 10.7910/dvn/5yqdu9/rpe6we
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DOI: 10.7910/dvn/hhsjy8/dgyovb
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DOI: 10.7910/dvn/hhsjy8/vgutuf
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DOI: 10.7910/dvn/oioefn/4hoydo
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DOI: 10.7910/dvn/5yqdu9/d8nowh
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DOI: 10.7910/dvn/5yqdu9/sh5vrs
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DOI: 10.7910/dvn/5yqdu9/vn4uux
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DOI: 10.7910/dvn/5yqdu9/wijrj7
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DOI: 10.7910/dvn/oioefn/nnmisz
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DOI: 10.7910/dvn/a8ceuw/e6xi6t
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title: cmplxconv.py

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title: cmplxdropout.py

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title: cmplxBatchNorm.py

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title: P2_YA_xInt.tab

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DOI: 10.7910/dvn/icmxvh/mspyhl
title: P2_YA_xInt_Analysis.R

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DOI: 10.7910/dvn/5yqdu9/5oeoh4
title: GT20gm1e5_50k_hg1_ss.00120000

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DOI: 10.7910/dvn/5yqdu9/nw8oqj
title: GT20gm1e5_50k_hg1_collisions.dat

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title: GT20gm1e5_50k_hg1_ss.00120000.origin_bins

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title: Digraph.gv

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title: fftutils.py

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title: dataset_radcine.py

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DOI: 10.7910/dvn/ci3wb6/jp9kcq
title: cmplxupsample.py

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DOI: 10.7910/dvn/ci3wb6/jvhtat
title: __init__-1.py

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title: data_vis.py

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DOI: 10.7910/dvn/5yqdu9/nh8wlq
title: GT20gm1e5_50k_hg1_ss.12480000.origin_bins

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DOI: 10.7910/dvn/5yqdu9/rvupkf
title: GT20gm1e5_50k_hg1_ss.26400000

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DOI: 10.7910/dvn/ci3wb6/30uaw1
title: myloss.py

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DOI: 10.7910/dvn/ci3wb6/bnpt2t
title: __init__.py

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DOI: 10.7910/dvn/ci3wb6/gdfd0y
title: modules.xml

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DOI: 10.7910/dvn/ci3wb6/rftkfh
title: main_radcine.py

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DOI: 10.7910/dvn/ci3wb6/vbrmpq
title: misc.xml

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DOI: 10.7910/dvn/hhsjy8/t9ig53
title: img_456_1190418.jpg

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DOI: 10.7910/dvn/hhsjy8/tzdffq
title: img_456_1190418.jpg

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DOI: 10.7910/dvn/hhsjy8/v0gtzw
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DOI: 10.7910/dvn/hhsjy8/vrdha4
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DOI: 10.23729/b2ea87e6-b845-46b8-abf3-cdbe299ce8b0
title: KORTE Social Distance Estimation

description: __ The dataset is provided for research purposes only. To obtain access to the data, a [license agreement](https://tuni-my.sharepoint.com/:b:/g/personal/jenni_raitoharju_tuni_fi/EWtLHFXv_29JhPQJmG3VGbcBvz348ulYXw95eaIe61EU2A?e=x0sbUc) must be signed and sent to jenni.k.raitoharju at jyu.fi__ .

The dataset contains 96 high resolution RGB images of multiple people with all of their locations annotated for each image. The dataset is meant to be used for evaluating the performance of methods that can automatically estimate social distances between people from RGB images.

All of the images are in JPG format. 81 of the images have the resolution 4180x2768 and 15 of the images have the resolution 4080x2720. Two different camera models were used and the sensor size for both of these cameras is 36 mm in width and 24 mm in height. The camera models are Canon EOS 5D Mark II and Canon EOS 6D Mark II. Different focal length lenses were used for capturing the images. These focal lengths are: 16, 24, 35, 50, 105, 200, and 300 mm. Focal length information is present in the metadata of each image. The cameras were stabilized on a tripod and the tripod’s height was 135 cm for all images.

The dataset contains images from 2 different photoshoots/locations that were taken in an outdoor and an indoor setting. During each of the photo shoots, test subjects stayed on the same known positions, while the photographer changed his position and used multiple cameras and lenses at each spot.

The dataset contains annotations for the relative ground truth 3D locations of each person and the cameras and also contains pixel locations for 4 body parts of each person (center of eyes, torso, shoulders and head). Along with the dataset, we provide codes to evaluate our own social distance estimation method and we also provide the necessary API for other methods to evaluate their performances on this dataset. Furthermore, additional images to the dataset can be added by following the annotation format. Detailed explanations are found in the attached README.txt file.

All of the evaluation code and the tutorial may also be found in: [https://github.com/mertseker-dev/social-distance-estimation](https://github.com/mertseker-dev/social-distance-estimation).

Citation/further infomation: 
M. Seker, A. Mannisto, A. Iosifidis, J. Raitoharju, Mert Seker, Anssi Männistö, Alexandros Iosifidis, Jenni Raitoharju, __ Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm__ , arXiv:2103.06759, 2021.
DOI: 10.7910/dvn/5yqdu9/asjwr9
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