cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3408014
title: The Eurosprite 2009-2013 Catalogue

description: The Eurosprite 2009-2013 Catalogue contains the summary of optical observations of transient luminous events (TLEs) collected by the Eurosprite observational network and partners over Europe and the Mediterranean Sea during 2009 to 2013. Each entry in the catalogue consists of a summary of TLEs recorded by the observer during a thunderstorm/observing period (e.g., the active period of a thunderstorm or more cells together) and reports the time span and approximate geographical area of the observations, the number and types of TLEs that were recorded, a reference person and additional comments. Optical images related to each entry of the catalogue are stored and maintained by the observer and can be reached through its contact person. A detailed description of the observational network, of the observations and on the interpretation of the observations can be found in Arnone et al., 2019, Surveys in Geophysics. Each entry is reported in the following terms. DATE: date of start of sample (only start even if crossing the night) UT: UT time of start and end of sample (end can be in the next day) LON: approximate Lon1/Lon2 range of the area affected by TLEs LAT: approximate Lat1/Lat2 range of the area affected by TLEs TLEs: total number of TLEs observed (with the number of individual types of TLEs observed, i.e. sp: sprites, el: elves, ha: halos, bj: blue jets, gj: gigantic jets, ot: other types). Clearly distinguished TLEs in the same frame count as separate ones. TLEs that appear part of the same event count as one. For this reason the total number of TLEs may be smaller than the sum of individual types. REF: a contact person for the entry COM: additional comments on e.g., cameras involved or partners
description: For usage of the catalogue we request to cite the publication on the zenodo archive and the publication by Arnone et al., 2019, Surveys in Geophysics following the license. If the data are used as a substantial support to new works, a discussion of possible co-authorship is appreciated. Disclaimer: The catalogue consists of a summary of key information for TLE observations with approximate geolocation. It was written to allow a search for individual observations and the usage for statistical studies. Great care should be taken in interpreting its overall content and individual entries. Please contact the authors of the catalogue or individual observers for guidance.
description: {"references": ["Enrico Arnone, J\u00f3zsef B\u00f3r, Olivier Chanrion, Veronika Barta, Stefano Dietrich, Carl-Fredrik Enell, Thomas Farges, Martin F\u00fcllekrug, Antti Kero, Roberto Labanti, Antti M\u00e4kel\u00e4, Keren Mezuman, Anna Odzimek, Martin Popek, Marco Prevedelli, Marco Ridoli, Serge Soula, Oscar van der Velde, Yoav Yair, Przemyslaw Zoladek and Torsten Neubert: Climatology of Transient Luminous Events and Lightning Observed Above Europe and the Mediterranean Sea. Surveys in Geophysics, 2019. https://doi.org/10.1007/s10712-019-09573-5"]}
DOI: 10.7910/dvn/oawdzc/tms3lp
title: 20180219_UAS_NTUBWI_14CM_NIR.tif

description: :unav
DOI: 10.7910/dvn/oawdzc/xfy4gx
title: 20180219_UAS_NTUBWI_14CM_GREEN.tif

description: :unav
DOI: 10.21227/45g6-hf90
title: Dataset for article named "ARIMA Modelling as an Alternative to the Least Squares Method: An Application for Electric Field Propagation"

description: This dataset includes information on measured points used to obtain the exposed results in article named "ARIMA Modelling as an Alternative to the Least Squares Method: An Application for Electric Field Propagation"
DOI: 10.21227/pzpn-sm42
title: Dataset for article named "A linear parametric propagation model applied to DTV frequencies in typical Amazon cities of Brazil"

description: This dataset includes information on measured points used to obtain the exposed results in article named "ARIMA Modelling as an Alternative to the Least Squares Method: An Application for Electric Field Propagation"
DOI: 10.21227/c611-x759
title: 300-Dimensional Word Embeddings for Nepali Language

description: This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 100 million running words.Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.
DOI: 10.21227/vg06-cm19
title: 300-Dimensional Word Embeddings for Nepali Language

description: This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 100 million running words.Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.
DOI: 10.3886/e109748
title: Environmental DNA in Streams to Detect Feral Swine MI

description: Invasive feral swine can damage ecosystems, disrupt plant and animal populations, and transmit diseases. Monitoring feral swine populations depends on expensive and labor intensive techniques such as aerial surveys, field surveys for sign, trail cameras, and landowner reports. Environmental DNA (eDNA) provides an alternative method for locating feral swine. From May 2017 to April 2018, swine DNA was artificially introduced into two streams in mid-Michigan. Water samples and data on various stream variables were collected at the location of DNA insertion and along a 400 m transect to investigate potential factors affecting swine DNA detection. <br>
DOI: 10.3886/e109748v3
title: Environmental DNA in Streams to Detect Feral Swine MI

description: Invasive feral swine can damage ecosystems, disrupt plant and animal populations, and transmit diseases. Monitoring feral swine populations depends on expensive and labor intensive techniques such as aerial surveys, field surveys for sign, trail cameras, and landowner reports. Environmental DNA (eDNA) provides an alternative method for locating feral swine. From May 2017 to April 2018, swine DNA was artificially introduced into two streams in mid-Michigan. Water samples and data on various stream variables were collected at the location of DNA insertion and along a 400 m transect to investigate potential factors affecting swine DNA detection. <br>
DOI: 10.5281/zenodo.3407839
title: 3D60 (Central Viewpoint)

description: Modern 3D vision advancements rely on data driven methods and thus, task specific annotated datasets. Especially for geometric inference tasks like depth and surface estimation, the collection of high quality data is very challenging, expensive and laborious. While considerable efforts have been made for traditional pinhole cameras, the same cannot be said for omnidirectional ones. <strong>3D60</strong> is a collective dataset generated in the context of various 360<sup>o</sup> vision research works. It comprises multi-modal omnidirectional stereo renders of scenes from realistic and synthetic large-scale 3D datasets (<em>Matterport3D</em>, <em>Stanford2D3D </em>and <em>SunCG</em>). Our dataset fills a very important gap in data-driven spherical 3D vision and, more specifically, for the monocular and stereo dense depth and surface estimation tasks. We originate by exploiting the efforts made in providing synthetic and real scanned 3D datasets of interior spaces and re-using them via ray-tracing in order to generate high quality, densely annotated spherical panoramas.
description: Instructions, code and data splits available @ https://vcl3d.github.io/3D60/
DOI: 10.5281/zenodo.3407840
title: 3D60 (Central Viewpoint)

description: Modern 3D vision advancements rely on data driven methods and thus, task specific annotated datasets. Especially for geometric inference tasks like depth and surface estimation, the collection of high quality data is very challenging, expensive and laborious. While considerable efforts have been made for traditional pinhole cameras, the same cannot be said for omnidirectional ones. <strong>3D60</strong> is a collective dataset generated in the context of various 360<sup>o</sup> vision research works. It comprises multi-modal omnidirectional stereo renders of scenes from realistic and synthetic large-scale 3D datasets (<em>Matterport3D</em>, <em>Stanford2D3D </em>and <em>SunCG</em>). Our dataset fills a very important gap in data-driven spherical 3D vision and, more specifically, for the monocular and stereo dense depth and surface estimation tasks. We originate by exploiting the efforts made in providing synthetic and real scanned 3D datasets of interior spaces and re-using them via ray-tracing in order to generate high quality, densely annotated spherical panoramas.
description: Instructions, code and data splits available @ https://vcl3d.github.io/3D60/
DOI: 10.7910/dvn/ddg8sw/b06teq
title: Anbinder EISB Household Database 9.13.2019.tab

description: Many corrections to highest depositor savings. Many corrections of household composition since last version was uploaded.
DOI: 10.7910/dvn/ddg8sw/lnoakn
title: Anbinder EISB Depositor Database 9.13.2019.xlsx

description: Many corrections to highest depositor savings. The birthplaces of hundreds more depositors were identified.
DOI: 10.7910/dvn/ddg8sw/xwbrp7
title: Anbinder Abridged EISB Depositor Database 9.13.2019.xlsx

description: Many corrections to highest depositor savings. The birthplaces of hundreds more depositors were identified.
DOI: 10.7910/dvn/ddg8sw/cp2goy
title: Anbinder EISB Transactions Database 9.13.2019.tab

description: No significant changes to this version since the one uploaded in March of 2019
DOI: 10.7910/dvn/txeydx/4c64rl
title: Data.tab

description: Ophthalmology randomized controlled trials
DOI: 10.17632/d698f2889k.3
title: Data for: Enhanced production of ethyl acetate using co-culture of Wickerhamomyces anomalus and Saccharomyces cerevisiae

description: Reasearch data for this study.
DOI: 10.21227/v59k-q317
title: 300-Dimensional Word Embeddings for Nepali Language

description: This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 100 million running words.Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.
DOI: 10.7910/dvn/jn4eyu/penq18
title: MeTooMMD-1.tab

description: :unav
DOI: 10.7910/dvn/pjomf1/3cscrj
title: 1930_142.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/5qzyea
title: 1920_Book30_180.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/zjuq03
title: 1930_154.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/1elvts
title: 1923-03-17 Paving Meet is Well Attended p6 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/4r3am8
title: 1930_120.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/eqhery
title: 1930_92.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/hw3t54
title: 1920_Book30_157-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/mgeztg
title: 1925_75.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/qq34nx
title: 1930_34.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/roxdkq
title: 1920_Book30_94-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/skj8fz
title: 1923-01-07 Students Build Cardboard p8 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/sqorxy
title: 1922-03-06 Electrolier Work Begins this Month p3 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/2qk4pn
title: 1925_104.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/4roxci
title: 1925_95.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/8jilbi
title: 1925_33.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/ihtnm5
title: 1920_Book30_149-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/iqcao4
title: 1921-10-10 Cleaning Bear Creak is Up Again (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/ju5fu8
title: 1920_Book30_71-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/nsqezl
title: 1925_66.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/odmjrq
title: 1920_Book30_15-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/rwp1qv
title: 1925_13.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/1e8jnd
title: 1930_99.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/c9eww8
title: 1925_26.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/f9f6wt
title: 1920_Book30_100-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/nvskod
title: 1920-04-14 Property Values and Rents A p2 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/ofowrl
title: 1920_Book30_69-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/osjorz
title: 1930_90.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/qbdkmw
title: 1930_136.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/qkrzgs
title: 1930_33.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/tfetbz
title: 1920-05-28 Civic Forum is to Ask City Father for Comm on Public Parks B p6 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/x1rpdv
title: 1920-06-08 City Plans for Installation of New and Modern Street Lighting p1 (MES).jpg

description: :unav
DOI: 10.15468/cuxuth
title: Oreocharis tetrapterus (Gesneriaceae), a new species from East Guangxi, China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Pan, Bo, Tang, Guang-Da, Do, Truong Van, Maciejewski, Stephen, Deng, Chong-Lang, Wen, Fang (2019): Oreocharis tetrapterus (Gesneriaceae), a new species from East Guangxi, China. PhytoKeys 131: 83-89, DOI: http://dx.doi.org/10.3897/phytokeys.131.35434, URL: http://dx.doi.org/10.3897/phytokeys.131.35434
DOI: 10.21227/mzp5-x261
title: 300-Dimensional Word Embeddings for Nepali Language

description: This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 100 million running words.Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.
DOI: 10.21227/r5yv-ay34
title: 300-Dimensional Word Embeddings for Nepali Language

description: This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 100 million running words.Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.
DOI: 10.7910/dvn/pjomf1/bfybgs
title: 1930_144.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/cjozne
title: 1920-8-4 Bradley Addition Road to be Wide p1 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/e1k31d
title: 1930_2.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/fuhkt5
title: 1922-01-27 Merced Growth is Attracting Interest p6 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/maro4m
title: 1922-03-13 Build Now p4 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/orusnf
title: 1930_67.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/qyqlqd
title: 1920_Book30_28-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/v4vtjy
title: 1923-10-27 Four Story Annex to Be Added to Hotel El Capitan p1 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/cmldlu
title: 1920_Book30_77-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/dzrzvh
title: 1920_Book30_84-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/ikvgrj
title: 1920_Book30_40-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/n52ex4
title: 1920_Book30_139-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/qk7rg5
title: 1930_121.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/rulxct
title: 1921-10-13 Pottery Co Turns out First Tile at Merced Plant p8 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/tn0tsf
title: 1930_172.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/xld5u7
title: 1925_126.jpg

description: :unav
DOI: 10.5281/zenodo.3408410
title: Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development

description: This upload contains the expression prediction dataset discussed in the manuscript "Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development" and used by the webserver https://find.princeton.edu. Abstract: Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput <em>in situ</em> hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like <em>Drosophila</em>, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation. Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current <em>in situ</em> hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in &gt;200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver http://find.princeton.edu, which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles.
DOI: 10.5281/zenodo.3408411
title: Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development

description: This upload contains the expression prediction dataset discussed in the manuscript "Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development" and used by the webserver https://find.princeton.edu. Abstract: Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput <em>in situ</em> hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like <em>Drosophila</em>, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation. Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current <em>in situ</em> hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in &gt;200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver http://find.princeton.edu, which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles.
DOI: 10.7910/dvn/pjomf1/0aglil
title: 1930_68.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/au0jpm
title: 1930_147.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/ck3a9x
title: 1930_8.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/kkzfm2
title: 1920_Book30_36-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/kyaaos
title: 1920_Book30_99-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/phalzw
title: 1920_Book30_124-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/qesvhr
title: 1920_Book30_119-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/xdrkw5
title: 1923-01-06 AD You will be building in 1923 p6 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/y5vwuy
title: 1922-02-09 Realty Boards to Consolidate is Plan p8 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/ydrat8
title: 1923-02-28 Zoning Ordinances p2 (MES).jpg

description: :unav
DOI: 10.21227/jjwk-xr19
title: 300-Dimensional Word Embeddings for Nepali Language

description: This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 100 million running words.Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.
DOI: 10.7910/dvn/pjomf1/5ixbb9
title: 1920_Book30_49-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/7qu2w2
title: 1930_157.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/ad084x
title: 1930_40.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/c7tawo
title: 1920-12-1 More Houses for Merced is Cry p1 (MES).jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/cnodvt
title: 1920_Book30_193.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/csd2z8
title: 1920_Book30_38-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/ptfpsh
title: 1930_43.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/qwzqzz
title: 1925_2.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/shqxr9
title: 1925_77.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/u8tyxy
title: 1920_Book30_93-1.jpg

description: :unav
DOI: 10.21227/1y41-dg68
title: 300-Dimensional Word Embeddings for Nepali Language

description: This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 100 million running words.Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.
DOI: 10.7910/dvn/pjomf1/e5vktn
title: 1930_77.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/ebt5ov
title: 1930_63.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/eern89
title: 1920_Book30_57-1.jpg

description: :unav
DOI: 10.7910/dvn/pjomf1/lkffzj
title: 1930_115.jpg

description: :unav