cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc29m0zy
title: CCDC 2133450: Experimental Crystal Structure Determination

description: Related Article: Ludwig Hackl, Luong Phong Ho, Dustin Bockhardt, Thomas Bannenberg, Matthias Tamm|2022|Organometallics|41|836|doi:10.1021/acs.organomet.2c00075
DOI: 10.5517/ccdc.csd.cc29m100
title: CCDC 2133451: Experimental Crystal Structure Determination

description: Related Article: Ludwig Hackl, Luong Phong Ho, Dustin Bockhardt, Thomas Bannenberg, Matthias Tamm|2022|Organometallics|41|836|doi:10.1021/acs.organomet.2c00075
DOI: 10.5517/ccdc.csd.cc29m111
title: CCDC 2133452: Experimental Crystal Structure Determination

description: Related Article: Ludwig Hackl, Luong Phong Ho, Dustin Bockhardt, Thomas Bannenberg, Matthias Tamm|2022|Organometallics|41|836|doi:10.1021/acs.organomet.2c00075
DOI: 10.5517/ccdc.csd.cc29m122
title: CCDC 2133453: Experimental Crystal Structure Determination

description: Related Article: Ludwig Hackl, Luong Phong Ho, Dustin Bockhardt, Thomas Bannenberg, Matthias Tamm|2022|Organometallics|41|836|doi:10.1021/acs.organomet.2c00075
DOI: 10.5517/ccdc.csd.cc29m144
title: CCDC 2133455: Experimental Crystal Structure Determination

description: Related Article: Ludwig Hackl, Luong Phong Ho, Dustin Bockhardt, Thomas Bannenberg, Matthias Tamm|2022|Organometallics|41|836|doi:10.1021/acs.organomet.2c00075
DOI: 10.5517/ccdc.csd.cc29m155
title: CCDC 2133456: Experimental Crystal Structure Determination

description: Related Article: Ludwig Hackl, Luong Phong Ho, Dustin Bockhardt, Thomas Bannenberg, Matthias Tamm|2022|Organometallics|41|836|doi:10.1021/acs.organomet.2c00075
DOI: 10.5517/ccdc.csd.cc29m166
title: CCDC 2133457: Experimental Crystal Structure Determination

description: Related Article: Ludwig Hackl, Luong Phong Ho, Dustin Bockhardt, Thomas Bannenberg, Matthias Tamm|2022|Organometallics|41|836|doi:10.1021/acs.organomet.2c00075
DOI: 10.5517/ccdc.csd.cc28ym62
title: CCDC 2113834: Experimental Crystal Structure Determination

description: Related Article: Quentin Gaydon, D. Scott Bohle|2022|Inorg.Chem.|61|4660|doi:10.1021/acs.inorgchem.1c03789
DOI: 10.5517/ccdc.csd.cc29yh3w
title: CCDC 2143498: Experimental Crystal Structure Determination

description: Related Article: Todd N. Poe, Alyssa N. Gaiser, Thomas E. Albrecht-Schönzart|2022|Cryst.Growth Des.|22|2670|doi:10.1021/acs.cgd.2c00090
DOI: 10.5517/ccdc.csd.cc29yh4x
title: CCDC 2143499: Experimental Crystal Structure Determination

description: Related Article: Todd N. Poe, Alyssa N. Gaiser, Thomas E. Albrecht-Schönzart|2022|Cryst.Growth Des.|22|2670|doi:10.1021/acs.cgd.2c00090
DOI: 10.5517/ccdc.csd.cc29yh5y
title: CCDC 2143500: Experimental Crystal Structure Determination

description: Related Article: Todd N. Poe, Alyssa N. Gaiser, Thomas E. Albrecht-Schönzart|2022|Cryst.Growth Des.|22|2670|doi:10.1021/acs.cgd.2c00090
DOI: 10.5517/ccdc.csd.cc29yh70
title: CCDC 2143502: Experimental Crystal Structure Determination

description: Related Article: Todd N. Poe, Alyssa N. Gaiser, Thomas E. Albrecht-Schönzart|2022|Cryst.Growth Des.|22|2670|doi:10.1021/acs.cgd.2c00090
DOI: 10.5517/ccdc.csd.cc29z24j
title: CCDC 2144057: Experimental Crystal Structure Determination

description: Related Article: Saravanakumar Elangovan, Elisabeth Irran, Hendrik F. T. Klare, Martin Oestreich|2022|Organometallics|41|852|doi:10.1021/acs.organomet.2c00092
DOI: 10.5517/ccdc.csd.cc29z25k
title: CCDC 2144058: Experimental Crystal Structure Determination

description: Related Article: Saravanakumar Elangovan, Elisabeth Irran, Hendrik F. T. Klare, Martin Oestreich|2022|Organometallics|41|852|doi:10.1021/acs.organomet.2c00092
DOI: 10.5517/ccdc.csd.cc29z26l
title: CCDC 2144059: Experimental Crystal Structure Determination

description: Related Article: Saravanakumar Elangovan, Elisabeth Irran, Hendrik F. T. Klare, Martin Oestreich|2022|Organometallics|41|852|doi:10.1021/acs.organomet.2c00092
DOI: 10.5517/ccdc.csd.cc29z27m
title: CCDC 2144060: Experimental Crystal Structure Determination

description: Related Article: Saravanakumar Elangovan, Elisabeth Irran, Hendrik F. T. Klare, Martin Oestreich|2022|Organometallics|41|852|doi:10.1021/acs.organomet.2c00092
DOI: 10.5517/ccdc.csd.cc29z28n
title: CCDC 2144061: Experimental Crystal Structure Determination

description: Related Article: Saravanakumar Elangovan, Elisabeth Irran, Hendrik F. T. Klare, Martin Oestreich|2022|Organometallics|41|852|doi:10.1021/acs.organomet.2c00092
DOI: 10.5517/ccdc.csd.cc29yh6z
title: CCDC 2143501: Experimental Crystal Structure Determination

description: Related Article: Todd N. Poe, Alyssa N. Gaiser, Thomas E. Albrecht-Schönzart|2022|Cryst.Growth Des.|22|2670|doi:10.1021/acs.cgd.2c00090
DOI: 10.5517/ccdc.csd.cc29zskn
title: CCDC 2144752: Experimental Crystal Structure Determination

description: Related Article: Shuyang Zhang, Maren Pink, Tobias Junghoefer, Wenchao Zhao, Sheng-Ning Hsu, Suchada Rajca, Arrigo Calzolari, Bryan W. Boudouris, Maria Benedetta Casu, Andrzej Rajca|2022|J.Am.Chem.Soc.|144|6059|doi:10.1021/jacs.2c01141
DOI: 10.5517/ccdc.csd.cc2b6jqt
title: CCDC 2151236: Experimental Crystal Structure Determination

description: Related Article: Dustin M. Sarnes, Peter G. Jones, Thomas Lindel|2022|Org.Lett.|24|2479|doi:10.1021/acs.orglett.2c00510
DOI: 10.5517/ccdc.csd.cc2381wb
title: CCDC 1944161: Experimental Crystal Structure Determination

description: Related Article: Shuhua Zhang, Zhonghang Chen, Tao Zhou, Guangzhao Li|2022|Organometallics|41|829|doi:10.1021/acs.organomet.2c00054
DOI: 10.5517/ccdc.csd.cc29jq5r
title: CCDC 2131224: Experimental Crystal Structure Determination

description: Related Article: Haiping Xu, Zhizhuan Zhang, Xuehua Dong, Ling Huang, Hongmei Zeng, Zhien Lin, Guohong Zou|2022|Inorg.Chem.|61|4752|doi:10.1021/acs.inorgchem.2c00169
DOI: 10.5517/ccdc.csd.cc2bb4mg
title: CCDC 2154705: Experimental Crystal Structure Determination

description: Related Article: Ning Xu, Yan-Xi Tan, EI-Sayed M. El-Sayed, Daqiang Yuan|2022|Cryst.Growth Des.|22|2768|doi:10.1021/acs.cgd.2c00243
DOI: 10.5281/zenodo.6431153
title: Bridge Load Posting Prediction

description: There are approximately 13,000 bridges in Louisiana facilitating movement of people, goods, and services. At present, about 12% of the bridges are load posted, i.e., they are deemed to lack the strength to safely carry all legal loads. With time bridges will age and deteriorate; at the same time, legal loads might increase. Load posted bridges disrupt the movement of goods and commerce. Therefore, objective of this research was to estimate the number of load posted bridges in Louisiana over the next 50 years. For this purpose, herein, a data based approach was used. For a given bridge type, the approach first developed three random forest models, to predict the future deck, sub-structure, and super-structure condition ratings of all the bridges of that type over the next 50 years. The inputs to these random forest models included a large number of bridge parameters obtained from the National Bridge Inventory (NBI). Herein, the bridge types were obtained from the Louisiana Department of Transportation and Development (LADOTD). Next, for a given bridge type, another random forest model was developed which used bridge parameters from NBI along with condition ratings to predict the load posting decision for a specific year in the future. The number of load posted bridge in each type were aggregated to obtain an estimate of the number of load posted bridge over the next 50 years. The random forest models identified several bridge parameters that significantly influence bridge load posting, e.g., condition ratings, age, span length, and roadway width among others. The results showed that while bridge types like light weight concrete pre-cast slab units and timber bridge had a large number of load posted bridges at present, concrete slab type bridge may be of concern in the future.
description: Tran-SET Project: 20STLSU01
DOI: 10.5281/zenodo.6431152
title: Bridge Load Posting Prediction

description: There are approximately 13,000 bridges in Louisiana facilitating movement of people, goods, and services. At present, about 12% of the bridges are load posted, i.e., they are deemed to lack the strength to safely carry all legal loads. With time bridges will age and deteriorate; at the same time, legal loads might increase. Load posted bridges disrupt the movement of goods and commerce. Therefore, objective of this research was to estimate the number of load posted bridges in Louisiana over the next 50 years. For this purpose, herein, a data based approach was used. For a given bridge type, the approach first developed three random forest models, to predict the future deck, sub-structure, and super-structure condition ratings of all the bridges of that type over the next 50 years. The inputs to these random forest models included a large number of bridge parameters obtained from the National Bridge Inventory (NBI). Herein, the bridge types were obtained from the Louisiana Department of Transportation and Development (LADOTD). Next, for a given bridge type, another random forest model was developed which used bridge parameters from NBI along with condition ratings to predict the load posting decision for a specific year in the future. The number of load posted bridge in each type were aggregated to obtain an estimate of the number of load posted bridge over the next 50 years. The random forest models identified several bridge parameters that significantly influence bridge load posting, e.g., condition ratings, age, span length, and roadway width among others. The results showed that while bridge types like light weight concrete pre-cast slab units and timber bridge had a large number of load posted bridges at present, concrete slab type bridge may be of concern in the future.
description: Tran-SET Project: 20STLSU01
DOI: 10.5517/ccdc.csd.cc2bb4nh
title: CCDC 2154706: Experimental Crystal Structure Determination

description: Related Article: Ning Xu, Yan-Xi Tan, EI-Sayed M. El-Sayed, Daqiang Yuan|2022|Cryst.Growth Des.|22|2768|doi:10.1021/acs.cgd.2c00243
DOI: 10.25606/surf.ac47d265a15be602
title: The LOFAR Two-metre Sky Survey (LoTSS) V. Second data release (P219+30)

description: In this data release from the ongoing LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) we present 120-168 MHz images covering 27% of the northern sky. Our coverage is split into two regions centred at approximately 12h45m +44◦30′ and 1h00m +28◦ 00′ and spanning 4178 and 1457 square degrees respectively. The images were derived from 3,451 hrs (7.6 PB) of LOFAR High Band Antenna data which were corrected for the direction-independent instrumental properties as well as direction-dependent ionospheric distortions during extensive, but fully automated, data processing. A catalogue of 4,396,228 radio sources is derived from our total intensity (Stokes I) maps, where the majority of these have never been detected at radio wavelengths before. At 6′′ resolution, our full bandwidth Stokes I continuum maps with a central frequency of 144 MHz have: a median rms sensitivity of 83 μ Jy/beam; a flux density scale accuracy of approximately 10%; an astrometric accuracy of 0.2′′; and we estimate the point-source completeness to be 90% at a peak brightness of 0.8 mJy/beam. By creating three 16 MHz bandwidth images across the band we are able to measure the in-band spectral index of many sources, albeit with an error on the derived spectral index of &gt; ±0.2 which is a consequence of our flux-density scale accuracy and small fractional bandwidth. Our circular polarisation (Stokes V) 20′′ resolution 120-168 MHz continuum images have a median rms sensitivity of 95 μ Jy/beam, and we estimate a Stokes I to Stokes V leakage of 0.056%. Our linear polarisation (Stokes Q and Stokes U) image cubes consist of 480 97.6 kHz wide planes and have a median rms sensitivity per plane of 10.8mJy/beam at 4′ and 2.2mJy/beam at 20′′; we estimate the Stokes I to Stokes Q/U leakage to be approximately 0.2%. Here we characterise and publicly release our Stokes I, Q, U and V images in addition to the calibrated uv-data to facilitate the thorough scientific exploitation of this unique dataset. This is field P219+30.
DOI: 10.5281/zenodo.6431276
title: Study On Hybrid Model Combining Super Learner And Physics-based Models For SHM In Bridges Using Low-cost BWIM

description: The main objective of this project is to study and develop hybrid models of BWIM physics-based models incorporates with SHM inspection used by artificial intelligence (AI) techniques to predict structural damage. This study presents a comprehensive assessment of FE simulations leveraging contact method verified by vehicle-bridge interaction(VBI) theory and uses machine learning (ML) techniques to identify and predict structural damages from the structural response automatically. Bridges are a fundamental part of infrastructure management. The main challenge that we face is the aging of these transportation infrastructures without a tool to perform accurate structural assessments in a real-time manner. Unfortunately, this topic is still not completely developed due to the lack of study upon BWIM simulations designed with several different severity damages. The Contact method, a new approach to simulating moving-vehicle motion in BWIM simulation, is to carry out actual structural response verified by the VBI with comprehensive parameter studies. In order to simulation the reality complex condition of the bridge, the FE model is designed by four different classes of damages with three different damage locations applied with two different load conditions (e.g., static load and moving load). The responses collected from FE simulation are used for structural damage prediction leveraging the ML damage prediction model. Among ML methods, the XGBoost with assembly decision tree shows the most reliable results. The results in this project indicate that structural damage prediction can be achieved by using the ML technique and BWIM structural response, which provides high accuracy of damage prediction.
description: Tran-SET Project: 20STUTA26
DOI: 10.5281/zenodo.6431275
title: Study On Hybrid Model Combining Super Learner And Physics-based Models For SHM In Bridges Using Low-cost BWIM

description: The main objective of this project is to study and develop hybrid models of BWIM physics-based models incorporates with SHM inspection used by artificial intelligence (AI) techniques to predict structural damage. This study presents a comprehensive assessment of FE simulations leveraging contact method verified by vehicle-bridge interaction(VBI) theory and uses machine learning (ML) techniques to identify and predict structural damages from the structural response automatically. Bridges are a fundamental part of infrastructure management. The main challenge that we face is the aging of these transportation infrastructures without a tool to perform accurate structural assessments in a real-time manner. Unfortunately, this topic is still not completely developed due to the lack of study upon BWIM simulations designed with several different severity damages. The Contact method, a new approach to simulating moving-vehicle motion in BWIM simulation, is to carry out actual structural response verified by the VBI with comprehensive parameter studies. In order to simulation the reality complex condition of the bridge, the FE model is designed by four different classes of damages with three different damage locations applied with two different load conditions (e.g., static load and moving load). The responses collected from FE simulation are used for structural damage prediction leveraging the ML damage prediction model. Among ML methods, the XGBoost with assembly decision tree shows the most reliable results. The results in this project indicate that structural damage prediction can be achieved by using the ML technique and BWIM structural response, which provides high accuracy of damage prediction.
description: Tran-SET Project: 20STUTA26
DOI: 10.5281/zenodo.6431345
title: Data Availability Statement

description: The file named Coulped Fe-P-S cycling in crab burrows is the original data for the paper Xiao et al "<strong>Crab bioturbation drives coupled iron-phosphate-sulfide cycling in mangrove and saltmarsh porewater</strong>".
DOI: 10.5281/zenodo.6431344
title: Data Availability Statement

description: The file named Coulped Fe-P-S cycling in crab burrows is the original data for the paper Xiao et al "<strong>Crab bioturbation drives coupled iron-phosphate-sulfide cycling in mangrove and saltmarsh porewater</strong>".
DOI: 10.34740/kaggle/dsv/3434830
title: Michelin Guide Restaurants 2021
DOI: 10.6084/m9.figshare.19561618
title: supplementary tables

description: the supplementary tables
DOI: 10.6084/m9.figshare.19561629.v1
title: Additional file 2 of Interaction between photoperiod and variation in circadian rhythms in tomato

description: Additional file 2: Table S1. primers used to generate near isogenic lines with wild alleles of EID1 and LNK2. Table S2. GO enrichment analysis. Table S3. List of transcripts whose expression oscillate with ~24h cycles in tomato and their phase estimates.
DOI: 10.6084/m9.figshare.19561629
title: Additional file 2 of Interaction between photoperiod and variation in circadian rhythms in tomato

description: Additional file 2: Table S1. primers used to generate near isogenic lines with wild alleles of EID1 and LNK2. Table S2. GO enrichment analysis. Table S3. List of transcripts whose expression oscillate with ~24h cycles in tomato and their phase estimates.
DOI: 10.6084/m9.figshare.19561634
title: Additional file 2 of Trophic niche separation of two non-spinose planktonic foraminifers Neogloboquadrina dutertrei and Pulleniatina obliquiloculata

description: Additional file 2. Table S1. Hydrographic data in the upper 200 m at the plankton tow site (Suruga Bay, Japan). Table S2. Standing stock data (individuals m–3) of living planktonic foraminifers at four depth intervals (0–20, 20–50, 50–100, and 100–200 m; two replicates) in Suruga Bay, Japan. Table S3. Standing stock data (individuals m–3) of living zooplankton and wet weight of particulate organic matters (POM) at four depth intervals (0–20, 20–50, 50–100, and 100–200 m; two replicates) in Suruga Bay, Japan. Table S4. Correlation results of two non-spinose planktonic foraminiferal species (Neogloboquadrina dutertrei and Pulleniatina obliquiloculata) with environmental and biological variables. Table S5. Nitrogen isotopic composition of amino acids in two non-spinose species of planktonic foraminifers. Abbreviations: alanine (Ala), glycine (Gly), valine (Val), leucine (Leu), isoleucine (Ile), proline (Pro), aspartic acid (Asp), threonine (Thr), serine (Ser), methionine (Met), glutamic acid (Glu), phenylalanine (Phe), and Hydroxyproline (Hyp).
DOI: 10.6084/m9.figshare.19561634.v1
title: Additional file 2 of Trophic niche separation of two non-spinose planktonic foraminifers Neogloboquadrina dutertrei and Pulleniatina obliquiloculata

description: Additional file 2. Table S1. Hydrographic data in the upper 200 m at the plankton tow site (Suruga Bay, Japan). Table S2. Standing stock data (individuals m–3) of living planktonic foraminifers at four depth intervals (0–20, 20–50, 50–100, and 100–200 m; two replicates) in Suruga Bay, Japan. Table S3. Standing stock data (individuals m–3) of living zooplankton and wet weight of particulate organic matters (POM) at four depth intervals (0–20, 20–50, 50–100, and 100–200 m; two replicates) in Suruga Bay, Japan. Table S4. Correlation results of two non-spinose planktonic foraminiferal species (Neogloboquadrina dutertrei and Pulleniatina obliquiloculata) with environmental and biological variables. Table S5. Nitrogen isotopic composition of amino acids in two non-spinose species of planktonic foraminifers. Abbreviations: alanine (Ala), glycine (Gly), valine (Val), leucine (Leu), isoleucine (Ile), proline (Pro), aspartic acid (Asp), threonine (Thr), serine (Ser), methionine (Met), glutamic acid (Glu), phenylalanine (Phe), and Hydroxyproline (Hyp).
DOI: 10.6084/m9.figshare.19561637
title: Additional file 1 of SETMAR, a case of primate co-opted genes: towards new perspectives

description: Additional file 1: Table S1. List of genes mentioned in the main text.
DOI: 10.6084/m9.figshare.19561637.v1
title: Additional file 1 of SETMAR, a case of primate co-opted genes: towards new perspectives

description: Additional file 1: Table S1. List of genes mentioned in the main text.
DOI: 10.17876/plate/dr.3/plates/203_33004
title: Digital object identifier (DOI) for the plate 33004 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33005
title: Digital object identifier (DOI) for the plate 33005 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33006
title: Digital object identifier (DOI) for the plate 33006 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33007
title: Digital object identifier (DOI) for the plate 33007 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33008
title: Digital object identifier (DOI) for the plate 33008 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33009
title: Digital object identifier (DOI) for the plate 33009 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33010
title: Digital object identifier (DOI) for the plate 33010 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33011
title: Digital object identifier (DOI) for the plate 33011 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33012
title: Digital object identifier (DOI) for the plate 33012 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33013
title: Digital object identifier (DOI) for the plate 33013 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33014
title: Digital object identifier (DOI) for the plate 33014 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33015
title: Digital object identifier (DOI) for the plate 33015 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33016
title: Digital object identifier (DOI) for the plate 33016 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33017
title: Digital object identifier (DOI) for the plate 33017 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33018
title: Digital object identifier (DOI) for the plate 33018 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33019
title: Digital object identifier (DOI) for the plate 33019 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33020
title: Digital object identifier (DOI) for the plate 33020 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33021
title: Digital object identifier (DOI) for the plate 33021 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33022
title: Digital object identifier (DOI) for the plate 33022 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33023
title: Digital object identifier (DOI) for the plate 33023 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33024
title: Digital object identifier (DOI) for the plate 33024 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33025
title: Digital object identifier (DOI) for the plate 33025 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33026
title: Digital object identifier (DOI) for the plate 33026 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33027
title: Digital object identifier (DOI) for the plate 33027 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33028
title: Digital object identifier (DOI) for the plate 33028 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33029
title: Digital object identifier (DOI) for the plate 33029 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_33030
title: Digital object identifier (DOI) for the plate 33030 in archive 203 from the APPLAUSE DR3