cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fgb4d
title: CCDC 2248093: Experimental Crystal Structure Determination

description: Related Article: Zheng Wang, Ning Ma, Xiaochi Lu, Ming Liu, Tian Liu, Qingbin Liu, Gregory A. Solan, Wen-Hua Sun|2023|Dalton Trans.|52|10574|doi:10.1039/D3DT02022C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fgnyj
title: CCDC 2248428: Experimental Crystal Structure Determination

description: Related Article: Shao-Xian Shi, Huan-Huan Zhang, Yi-Lin Wang, Lin-Hong Jiang, Peng-Fei Xu, Yong-Chun Luo|2023|Org.Lett.|25|5426|doi:10.1021/acs.orglett.3c01695
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fgp0m
title: CCDC 2248430: Experimental Crystal Structure Determination

description: Related Article: Shao-Xian Shi, Huan-Huan Zhang, Yi-Lin Wang, Lin-Hong Jiang, Peng-Fei Xu, Yong-Chun Luo|2023|Org.Lett.|25|5426|doi:10.1021/acs.orglett.3c01695
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fgp2p
title: CCDC 2248432: Experimental Crystal Structure Determination

description: Related Article: Shao-Xian Shi, Huan-Huan Zhang, Yi-Lin Wang, Lin-Hong Jiang, Peng-Fei Xu, Yong-Chun Luo|2023|Org.Lett.|25|5426|doi:10.1021/acs.orglett.3c01695
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25934/pr00002071.3
title: Available IPD datapackage for study 'A 24-week, Double-blind, Double-dummy, Randomized, Parallel-group Study to Investigate the Effects of Rosiglitazone (Extended Release Tablets), Donepezil, and Placebo as Monotherapy on Cognition and Overall Clinical Response in APOE ε4-stratified Subjects With Mild to Moderate Alzheimer's Disease. (REFLECT-1)'

description: Rosiglitazone (RSG) has been tested and is approved as a treatment for type II diabetes mellitus, a disease that occurs when the body ineffectively uses glucose. RSG XR, the investigational drug, is an extended-release form of RSG. This study tests whether RSG XR safely provides benefit to people with mild to moderate Alzheimer's disease (AD). RSG XR is a new approach to AD therapy and this study tests whether one's genes alter the effectiveness of RSG XR. Glucose is used by cells to make energy that they need to live. Changes in the ability of cells to use of glucose can lead to diseases like diabetes. Glucose levels may be lower in the brains of AD patients, and their brain cells may also use glucose less well than in unaffected people. The proper function of brain cells may be critical to memory and thought. If brain cells use glucose poorly, this might impact AD. Drugs that help brain cells properly use glucose may help a person maintain normal memory and thinking. Data suggesting that RSG may help AD patients was first seen in a small study at the Univ. of Washington and then from a larger international GSK study. In the first study, those receiving RSG once daily for 6 months scored better on 3 tests of memory and thought than those who did not receive RSG. In the GSK study, those that benefited most from therapy with RSG XR had a specific genetic pattern. They lacked the gene that caused them to produce apolipoprotein E e4 (APOE e4). Subjects who have the APOE e4 gene may have two copies, one from each parent, or they may have only one APOE e4 gene meaning that they inherited either the APOE e2 or APOE e3 version of the gene from one parent. Subjects with one copy of the APOE e4 gene remained fairly stable while those with two copies of APOE e4 continued to worsen during the 6-month treatment. This study will directly test the effect of RSG XR on people who either have or lack the APOE e4 gene.
DOI: 10.17909/8139-0772
title: Data from paper
DOI: 10.17632/v2wvfgzm53.1
title: Data for comparing the prediction of high precipitation among 7 layers in Mexico

description: The data are associated with the following article (please look there for all information): "Comparison among seven digital climate layers for estimating high annual precipitation in Mexico (≥ 1,500 mm)". Investigaciones Geográficas, Instituto de Geografía, UNAM. In preparation (August 2023).
DOI: 10.17632/k57c2mxr7b.1
title: Soot formation in methyl esters

description: The data presented are numerical investigation of soot formation in five methyl esters. This include methyl linolenate (MLe), methyl stearate (MS), methyl oleate (MO), methyl linoleate (MLi) and methyl palmitate (MP). The study was done in co-flow jet flames. ANSYS software was used to carried out the simulation.
DOI: 10.17632/fybmpz3n7g.2
title: Neo Khesa & Jean Mulopo LTE-PtG results 

description: These are the comprehensive stream results of the entire LTE-PtG setup.
DOI: 10.17632/fybmpz3n7g.1
title: Neo Khesa & Jean Mulopo LTE-PtG results 

description: These are the comprehensive stream results of the entire LTE-PtG setup.
DOI: 10.17632/fybmpz3n7g.3
title: Neo Khesa & Jean Mulopo NGCC, CCS LTE PtG results 

description: These are the comprehensive stream results for all retrofitted and un-retrofitted setups.
DOI: 10.5281/zenodo.8221749
title: A Forward Modeling Approach To Analyzing Galaxy Clustering with SimBIG

description: Data sets accompanying the publication: "A Forward Modeling Approach to Analyzing Galaxy Clustering" (https://arxiv.org/abs/2211.00723). mcmc.v4.plk.cmass.npy : MCMC samples drawn from the SimBIG posterior of the cosmological and HOD parameters given galaxy power spectrum multipoles to k_max = 0.5 mcmc.v4.plk.kmax0.25.cmass.npy : MCMC samples drawn from the SimBIG posterior of the cosmological and HOD parameters given galaxy power spectrum multipoles to k_max = 0.5 obs.cmass_sgc.* : k, number density, and power spectrum measurements of galaxies in the BOSS CMASS-SGC sample. simbig.cmass_sgc.v3.test* : test data sets used to validate the accuracy of the SimBIG posteriors. The test data sets are described in detail in Hahn et al. (2023) simbig.cmass_sgc.v4.* : training data used to train the normalizing flows used to infer the posterior.
DOI: 10.5281/zenodo.8221748
title: A Forward Modeling Approach To Analyzing Galaxy Clustering with SimBIG

description: Data sets accompanying the publication: "A Forward Modeling Approach to Analyzing Galaxy Clustering" (https://arxiv.org/abs/2211.00723). mcmc.v4.plk.cmass.npy : MCMC samples drawn from the SimBIG posterior of the cosmological and HOD parameters given galaxy power spectrum multipoles to k_max = 0.5 mcmc.v4.plk.kmax0.25.cmass.npy : MCMC samples drawn from the SimBIG posterior of the cosmological and HOD parameters given galaxy power spectrum multipoles to k_max = 0.5 obs.cmass_sgc.* : k, number density, and power spectrum measurements of galaxies in the BOSS CMASS-SGC sample. simbig.cmass_sgc.v3.test* : test data sets used to validate the accuracy of the SimBIG posteriors. The test data sets are described in detail in Hahn et al. (2023) simbig.cmass_sgc.v4.* : training data used to train the normalizing flows used to infer the posterior.
DOI: 10.5281/zenodo.8221822
title: Data set of 1,275 images capturing interactions between flies and blooming flowers

description: The dataset presented is a collection of 1,275 images capturing interactions between flies and blooming flowers. The images were sourced from internet repositories through searches conducted between August 2016 and August 2020, using the Google Chrome v. 33.x web browser. Internet searches focused on Google Images and three major social media platforms: Flickr, Instagram, and agefotostock. Photographs were taken by photographers worldwide and uploaded to these platforms, forming the basis of the dataset. The data encompasses various taxonomic and ecological information for both the flies and the flowers depicted in the images. For each image, detailed taxonomic information was recorded for the flies, including their suborders (Nematocera and Brachycera, grouped as Higher/Lesser), Family (wherever possible, distinguishing between Syrphidae and non-Syrphidae), and Genus and species (if available). Further characterization of the flies included recording their sex (identified based on morphology), feeding status (identified by visible proboscis extension into the flower), and the presence or absence of pollen particles on their bodies. Similarly, for each image, taxonomic information was collected for the flowers, including their Family and Genus and species (when identifiable). Flowers were categorized by petal color, which was grouped into four main categories based on the visible spectrum wavelength: purple to blue (ranging from 380-520 nm), green to yellow (ranging from 520-590 nm), orange to red (ranging from 590-740 nm), and white. Additionally, flowers were classified based on their shape, with four main categories: elongate cluster, round cluster, composite-shaped, or simple-shaped. To complement the taxonomic and morphological information, the dataset includes additional data for each image, such as web links to the original sources, geographic locations where the images were captured, and the date of image acquisition. This dataset offers a valuable resource for studying fly-flower interactions on a broad scale, using photographs contributed by photographers from around the world.
DOI: 10.5281/zenodo.8221821
title: Data set of 1,275 images capturing interactions between flies and blooming flowers

description: The dataset presented is a collection of 1,275 images capturing interactions between flies and blooming flowers. The images were sourced from internet repositories through searches conducted between August 2016 and August 2020, using the Google Chrome v. 33.x web browser. Internet searches focused on Google Images and three major social media platforms: Flickr, Instagram, and agefotostock. Photographs were taken by photographers worldwide and uploaded to these platforms, forming the basis of the dataset. The data encompasses various taxonomic and ecological information for both the flies and the flowers depicted in the images. For each image, detailed taxonomic information was recorded for the flies, including their suborders (Nematocera and Brachycera, grouped as Higher/Lesser), Family (wherever possible, distinguishing between Syrphidae and non-Syrphidae), and Genus and species (if available). Further characterization of the flies included recording their sex (identified based on morphology), feeding status (identified by visible proboscis extension into the flower), and the presence or absence of pollen particles on their bodies. Similarly, for each image, taxonomic information was collected for the flowers, including their Family and Genus and species (when identifiable). Flowers were categorized by petal color, which was grouped into four main categories based on the visible spectrum wavelength: purple to blue (ranging from 380-520 nm), green to yellow (ranging from 520-590 nm), orange to red (ranging from 590-740 nm), and white. Additionally, flowers were classified based on their shape, with four main categories: elongate cluster, round cluster, composite-shaped, or simple-shaped. To complement the taxonomic and morphological information, the dataset includes additional data for each image, such as web links to the original sources, geographic locations where the images were captured, and the date of image acquisition. This dataset offers a valuable resource for studying fly-flower interactions on a broad scale, using photographs contributed by photographers from around the world.
DOI: 10.6084/m9.figshare.23623812
title: Supplementary data and R script for "Human Differences" project.

description: Input and output CSV files, images, and R script.Edit on July 13, 2023: Calculated v-Loss in terms of raw age-structured deaths and total deaths instead of age-structured death rates and crude death rate. Corrected calculation of v by correcting SRB (division by 100).Edit on July 17, 2023: Removed alternative method of calculating v-Loss.Edit on August 7, 2023: Updated the calculation of reproductive value (v) so as to not divide by the neonatal age class (v0), to vary v0 and make v-Loss more comparable across populations.
DOI: 10.6084/m9.figshare.23623812.v4
title: Supplementary data and R script for "Human Differences" project.

description: Input and output CSV files, images, and R script.Edit on July 13, 2023: Calculated v-Loss in terms of raw age-structured deaths and total deaths instead of age-structured death rates and crude death rate. Corrected calculation of v by correcting SRB (division by 100).Edit on July 17, 2023: Removed alternative method of calculating v-Loss.Edit on August 7, 2023: Updated the calculation of reproductive value (v) so as to not divide by the neonatal age class (v0), to vary v0 and make v-Loss more comparable across populations.
DOI: 10.6084/m9.figshare.23623812.v3
title: Supplementary data and R script for Human Differences

description: Input and output CSV files, images, and R script. <br> Edit on July 13, 2023: Calculated v-Loss in terms of raw age-structured deaths and total deaths instead of age-structured death rates and crude death rate. Corrected calculation of v by correcting SRB (division by 100). Edit on July 17, 2023: Removed alternative method of calculating v-Loss.
DOI: 10.5066/p9zpgzur
title: Harvester ant (high abundance) probability of occurrence in the Wyoming Basins Ecoregional Assessment area

description: Probability map of Harvester Ant occurrence (high abundance) in relation to vegetation, abiotic, and anthropogenic features. These data were released prior to the October 1, 2016 effective date for the USGS&rsquo;s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release.
DOI: 10.6084/m9.figshare.23901942
title: 20230808_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.23901942.v1
title: 20230808_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.23901945
title: RGB Camera

description: Dataset created with RGB camera.The dataset is divided in the different diseased and in healthy. To use the dataset the dataset has to be reorganize into healthy and diseased (As in example).
DOI: 10.6084/m9.figshare.23901948
title: Extended Data File 6.xlsx

description: a spreadsheet in the MS Excel format used to capture GPX4 transcriptional profiles from multiple datasets. Each tab represents a different physiological state and dataset. Data was used to compute significance of difference in abundance observed between cases and controls.
DOI: 10.6084/m9.figshare.23901948.v1
title: Extended Data File 6.xlsx

description: a spreadsheet in the MS Excel format used to capture GPX4 transcriptional profiles from multiple datasets. Each tab represents a different physiological state and dataset. Data was used to compute significance of difference in abundance observed between cases and controls.
DOI: 10.5281/zenodo.8221880
title: Data associated with study on winter activity of crapemyrtle bark scale

description: These data files are associated with a study on the winter activity of crapemyrtle bark scale.
DOI: 10.7910/dvn/gpi3is/yacmhi
title: Per54.34so9887.robust-1.pbcor.fits
DOI: 10.7910/dvn/il0lue/0hwlio
title: product_process_patents_2_100_us_Civil engineering.png

description: :unav
DOI: 10.7910/dvn/il0lue/1hrddm
title: product_process_patents_2_100_us_Computer technology.png

description: :unav
DOI: 10.7910/dvn/il0lue/4b2wgh
title: product_process_patents_2_100_us_Electrical machinery, apparatus, energy.png

description: :unav
DOI: 10.7910/dvn/il0lue/sz5mgt
title: product_process_patents_2_100_us_Environmental technology.png

description: :unav
DOI: 10.7910/dvn/il0lue/toq0kr
title: product_process_patents_2_100_us_C05.png

description: :unav
DOI: 10.7910/dvn/il0lue/vt4t4a
title: product_process_patents_2_100_us_Digital communication.png

description: :unav
DOI: 10.7910/dvn/il0lue/vyafu0
title: product_process_patents_2_100_us_Control.png

description: :unav
DOI: 10.7910/dvn/il0lue/wnh3bf
title: product_process_patents_2_100_us_Engines, pumps, turbines.png

description: :unav
DOI: 10.7910/dvn/oonnyk/646vzl
title: Per65.H13COP43.robust1.pbcor.fits
DOI: 10.7910/dvn/oonnyk/8ol5ax
title: Per65.cont1.3mm.robust1.pbcor.fits
DOI: 10.7910/dvn/oonnyk/avidxy
title: Per65.13CO21.robust1.pbcor.fits
DOI: 10.7910/dvn/oonnyk/chojfh
title: Per65.cont850um.robust1.pbcor.fits
DOI: 10.7910/dvn/oonnyk/dviznq
title: Per65.H13COP43.robust-1.pbcor.fits
DOI: 10.7910/dvn/oonnyk/iszeis
title: Per65.cont850um.robust-1.pbcor.fits
DOI: 10.7910/dvn/oonnyk/n8ukb0
title: Per65.13CO21.robust-1.pbcor.fits
DOI: 10.7910/dvn/0o6vrk/42cyi8
title: claims_per_patents_ctry_us_Mexico-1.png

description: :unav
DOI: 10.7910/dvn/0o6vrk/5zarwl
title: claims_per_patents_ctry_us_Malta-1.png

description: :unav
DOI: 10.7910/dvn/0o6vrk/6ryq9t
title: claims_per_patents_ctry_us_Netherlands-1.png

description: :unav
DOI: 10.7910/dvn/0o6vrk/aijrhr
title: claims_per_patents_ctry_us_Luxembourg-1.png

description: :unav
DOI: 10.7910/dvn/0o6vrk/cbtoog
title: claims_per_patents_ctry_us_Liechtenstein-1.png

description: :unav
DOI: 10.7910/dvn/0o6vrk/ckpxik
title: claims_per_patents_ctry_us_Japan-1.png

description: :unav
DOI: 10.7910/dvn/0o6vrk/rq4crn
title: claims_per_patents_ctry_us_Latvia-1.png

description: :unav
DOI: 10.7910/dvn/0o6vrk/vu21fe
title: claims_per_patents_ctry_us_Italy-1.png

description: :unav
DOI: 10.7910/dvn/0o6vrk/xliyu6
title: claims_per_patents_ctry_us_Lithuania-1.png

description: :unav
DOI: 10.7910/dvn/aix1u1/2ujepi
title: Per64.C18O21.151205.uvfits
DOI: 10.7910/dvn/aix1u1/4cvaa1
title: Per64.13CO21.161103.uvfits
DOI: 10.7910/dvn/aix1u1/edweif
title: Per64.N2DP.161103.uvfits
DOI: 10.7910/dvn/aix1u1/odh7eo
title: Per64.13CO21.151205.uvfits
DOI: 10.7910/dvn/aix1u1/w54pdu
title: Per64.12CO21.161103.uvfits
DOI: 10.7910/dvn/aix1u1/wnp49d
title: Per64.12CO21.151205.uvfits
DOI: 10.7910/dvn/aix1u1/yspny0
title: Per64.N2DP.151205.uvfits
DOI: 10.7910/dvn/gpi3is/3n91xc
title: Per54.cn7252.robust1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/9vp39j
title: Per54.dcop54.robust-1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/gehksm
title: Per54.cn7252.robust-1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/gvumob
title: Per54.dcn54.robust-1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/jfavfx
title: Per54.cs76.robust1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/pf8tl4
title: Per54.cn5232.robust-1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/qok6d5
title: Per54.dcn54.robust1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/sxgwtl
title: Per54.cs76.robust-1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/z58kvn
title: Per54.cn5232.robust1.pbcor.fits
DOI: 10.7910/dvn/i2xnkn/bqqxzu
title: PerBolo58.12CO21.141125.uvfits
DOI: 10.7910/dvn/i2xnkn/c8nyvy
title: PerBolo58.12CO21.141214.uvfits
DOI: 10.7910/dvn/i2xnkn/ga61ek
title: PerBolo58.13CO21.141214.uvfits
DOI: 10.7910/dvn/i2xnkn/iicvti
title: PerBolo58.13CO21.150307.uvfits
DOI: 10.7910/dvn/i2xnkn/jdadki
title: PerBolo58.13CO21.141125.uvfits
DOI: 10.7910/dvn/i2xnkn/kelbrh
title: PerBolo58.12CO32.141214.uvfits
DOI: 10.7910/dvn/i2xnkn/zixgtw
title: PerBolo58.12CO21.150307.uvfits
DOI: 10.7910/dvn/il0lue/7odq6b
title: product_process_patents_2_100_us_Food chemistry.png

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DOI: 10.7910/dvn/il0lue/dhfpte
title: product_process_patents_2_100_us_IT methods for management.png

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DOI: 10.7910/dvn/il0lue/f5gz3e
title: product_process_patents_2_100_us_Materials, metallurgy.png

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DOI: 10.7910/dvn/il0lue/mccwak
title: product_process_patents_2_100_us_H01M.png

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DOI: 10.7910/dvn/il0lue/trvonm
title: product_process_patents_2_100_us_Macromolecular chemistry, polymers.png

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DOI: 10.7910/dvn/il0lue/vnhweu
title: product_process_patents_2_100_us_Machine tools.png

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DOI: 10.7910/dvn/il0lue/w8yhyo
title: product_process_patents_2_100_us_Handling.png

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DOI: 10.7910/dvn/il0lue/xordpj
title: product_process_patents_2_100_us_Furniture, games.png

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DOI: 10.7910/dvn/il0lue/ze1mq8
title: product_process_patents_2_100_us_F02B.png

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DOI: 10.7910/dvn/oonnyk/rf6b0l
title: 1README_Per65_images.txt
DOI: 10.7910/dvn/0o6vrk/1h0yby
title: claims_per_patents_ctry_us_Slovakia-1.png

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DOI: 10.7910/dvn/0o6vrk/bdqh0n
title: claims_per_patents_ctry_us_Norway-1.png

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DOI: 10.7910/dvn/0o6vrk/fxlgpk
title: claims_per_patents_ctry_us_Russia-1.png

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DOI: 10.7910/dvn/0o6vrk/jaf7un
title: claims_per_patents_ctry_us_New Zealand-1.png

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DOI: 10.7910/dvn/0o6vrk/pu1jy6
title: claims_per_patents_ctry_us_Portugal-1.png

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DOI: 10.7910/dvn/0o6vrk/taxitj
title: claims_per_patents_ctry_us_Romania-1.png

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DOI: 10.7910/dvn/0o6vrk/vqafa6
title: claims_per_patents_ctry_us_Poland-1.png

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DOI: 10.7910/dvn/0o6vrk/xclpan
title: claims_per_patents_ctry_us_Slovenia-1.png

description: :unav
DOI: 10.7910/dvn/aix1u1/5uvj9v
title: Per64.cont1.3mm.usb.151205.uvfits
DOI: 10.7910/dvn/aix1u1/9j10p4
title: Per64.cont1.3mm.lsb.161103.uvfits
DOI: 10.7910/dvn/aix1u1/fojewe
title: Per64.cont1.3mm.usb.161103.uvfits
DOI: 10.7910/dvn/aix1u1/fqfu7o
title: Per64.cont870um.usb.151205.uvfits
DOI: 10.7910/dvn/aix1u1/glhbkl
title: Per64.cont1.3mm.lsb.151205.uvfits
DOI: 10.7910/dvn/aix1u1/ihmiew
title: Per64.H13COP43.151205.uvfits
DOI: 10.7910/dvn/aix1u1/kdxzb6
title: Per64.C18O21.161103.uvfits
DOI: 10.7910/dvn/aix1u1/l8jj3y
title: Per64.SWARM.1.3mm.s1.lsb.1.161103.uvfits
DOI: 10.7910/dvn/aix1u1/zjcsu1
title: Per64.cont870um.lsb.151205.uvfits
DOI: 10.7910/dvn/gpi3is/1lxnru
title: Per54.hc15n43.robust-1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/3rlvgi
title: Per54.hc15n43.robust1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/a8f85g
title: Per54.ocs2827.robust1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/azmyfn
title: Per54.dcop54.robust1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/i5vxuq
title: Per54.hnc43.robust1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/pvnxjw
title: Per54.h13cn43.robust1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/rebimj
title: Per54.hc18op43.robust-1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/rtp3i9
title: Per54.hc18op43.robust1.pbcor.fits
DOI: 10.7910/dvn/gpi3is/tgdkoo
title: Per54.h13cn43.robust-1.pbcor.fits
DOI: 10.7910/dvn/i2xnkn/3pcdob
title: PerBolo58.C18O21.141125.uvfits
DOI: 10.7910/dvn/i2xnkn/5hsxfm
title: PerBolo58.N2DP.141214.uvfits
DOI: 10.7910/dvn/i2xnkn/9p8po4
title: PerBolo58.N2DP.140908.uvfits
DOI: 10.7910/dvn/i2xnkn/f3gi4c
title: PerBolo58.C18O21.140908.uvfits
DOI: 10.7910/dvn/i2xnkn/sgf5nv
title: PerBolo58.N2DP.150307.uvfits
DOI: 10.7910/dvn/i2xnkn/socjse
title: PerBolo58.13CO21.140908.uvfits
DOI: 10.7910/dvn/i2xnkn/ugn6jn
title: PerBolo58.C18O21.150307.uvfits
DOI: 10.7910/dvn/i2xnkn/vus1cm
title: PerBolo58.C18O21.141214.uvfits
DOI: 10.7910/dvn/i2xnkn/yytsii
title: PerBolo58.N2DP.141125.uvfits