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DOI: 10.57745/uu8mgy
title: RGBSCENE_2020-09-14_012.png
DOI: 10.57745/5mjwbw
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DOI: 10.57745/brgtpx
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DOI: 10.57745/wl7o9d
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DOI: 10.57745/csg9bf
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DOI: 10.57745/oq0l1w
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DOI: 10.57745/bcqg9l
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DOI: 10.57745/kotwyb
title: RGBBACKGROUND_2020-09-14_013.png
DOI: 10.57745/ozm2rt
title: RGBSCENE_2020-09-14_013.png
DOI: 10.57745/3jy3c5
title: RGBVIEWFINDER_2020-09-14_013.png
DOI: 10.57745/jxkhql
title: 2020-09-14_014.png
DOI: 10.57745/jaqwnr
title: 2020-09-14_014.hdr
DOI: 10.57745/c9p0kv
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DOI: 10.57745/jo1vmr
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DOI: 10.57745/gtfr1w
title: DARKREF_2020-09-14_014.raw
DOI: 10.57745/xuzkxn
title: WHITEREF_2020-09-14_014.hdr
DOI: 10.57745/omy6yf
title: WHITEREF_2020-09-14_014.raw
DOI: 10.57745/eretlj
title: 2020-09-14_014.xml
DOI: 10.57745/ep2rum
title: REFLECTANCE_2020-09-14_014.dat
DOI: 10.57745/dekaax
title: REFLECTANCE_2020-09-14_014.hdr
DOI: 10.57745/ytnwdt
title: REFLECTANCE_2020-09-14_014.png
DOI: 10.57745/c24jwl
title: RGBBACKGROUND_2020-09-14_014.png
DOI: 10.57745/kzdojc
title: RGBSCENE_2020-09-14_014.png
DOI: 10.57745/zumtvv
title: RGBVIEWFINDER_2020-09-14_014.png
DOI: 10.57745/zwqw7k
title: 2020-09-14_015.png
DOI: 10.57745/eembkb
title: 2020-09-14_015.hdr
DOI: 10.6092/unibo/amsacta/7289
title: ECLIPSE_WP1_Systems for signal amplification_Task1.6_Optimization of coreactant_Ir

description: The presented dataset contains data and images produced in the framework of the ECLipse project, regarding the electrochemiluminescence (ECL) of magnetic microbeads covalently functionalized with [Ru(bpy)3](2+) (Ru@Beads), in presence of Ir(III)-based redox mediators exhibiting a variety of photophysical and electrochemical properties. The Ir(III) mediators employed are: [Ir(bt)2(pt-TEG)]+, [Ir(dfppy)2(pt-TEG)]+ and [Ir(sppy)3](3-). Also, data related to [Fe(bpy)3](2+) redox mediator are included. The data are useful to decipher the interaction mechanism between the freely diffusing Ir(III) mediators with the anchored [Ru(bpy)3](2+) labels and determine the key properties of effective ECL enhancers.
DOI: 10.57745/0n2er3
title: 2020-09-14_015.raw
DOI: 10.57745/jikipn
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DOI: 10.57745/ec3qbs
title: DARKREF_2020-09-14_015.raw
DOI: 10.57745/nu3tja
title: WHITEREF_2020-09-14_015.hdr
DOI: 10.57745/psusif
title: WHITEREF_2020-09-14_015.raw
DOI: 10.57745/qtueb6
title: 2020-09-14_015.xml
DOI: 10.57745/ljqwrm
title: REFLECTANCE_2020-09-14_015.dat
DOI: 10.57745/mighf9
title: REFLECTANCE_2020-09-14_015.hdr
DOI: 10.57745/vfzo2e
title: REFLECTANCE_2020-09-14_015.png
DOI: 10.57745/lvphbt
title: RGBBACKGROUND_2020-09-14_015.png
DOI: 10.57745/0myt6o
title: RGBSCENE_2020-09-14_015.png
DOI: 10.57745/u8laaq
title: RGBVIEWFINDER_2020-09-14_015.png
DOI: 10.57745/bpgkrc
title: 2020-09-14_016.png
DOI: 10.57745/tzh1fu
title: 2020-09-14_016.hdr
DOI: 10.57745/263xfj
title: 2020-09-14_016.raw
DOI: 10.57745/pe6eh2
title: DARKREF_2020-09-14_016.hdr
DOI: 10.57745/nrn0e2
title: DARKREF_2020-09-14_016.raw
DOI: 10.57745/leu4ep
title: WHITEREF_2020-09-14_016.hdr
DOI: 10.57745/blnybe
title: WHITEREF_2020-09-14_016.raw
DOI: 10.57745/dnw0ai
title: 2020-09-14_016.xml
DOI: 10.57745/wjeyuy
title: REFLECTANCE_2020-09-14_016.dat
DOI: 10.57745/i3tsdt
title: REFLECTANCE_2020-09-14_016.hdr
DOI: 10.57745/wc3tap
title: REFLECTANCE_2020-09-14_016.png
DOI: 10.57745/a10ft7
title: RGBBACKGROUND_2020-09-14_016.png
DOI: 10.57745/nhohnv
title: RGBSCENE_2020-09-14_016.png
DOI: 10.57745/6x1dgq
title: RGBVIEWFINDER_2020-09-14_016.png
DOI: 10.57745/3kii3l
title: 2020-09-14_017.png
DOI: 10.57745/ycqnfo
title: 2020-09-14_017.hdr
DOI: 10.57745/diywyd
title: 2020-09-14_017.raw
DOI: 10.57745/tdbk75
title: DARKREF_2020-09-14_017.hdr
DOI: 10.57745/fak37v
title: DARKREF_2020-09-14_017.raw
DOI: 10.57745/kdx821
title: WHITEREF_2020-09-14_017.hdr
DOI: 10.57745/lpxhd2
title: WHITEREF_2020-09-14_017.raw
DOI: 10.57745/u6ia9j
title: 2020-09-14_017.xml
DOI: 10.57745/bbdonj
title: REFLECTANCE_2020-09-14_017.dat
DOI: 10.57745/pmhmeb
title: REFLECTANCE_2020-09-14_017.hdr
DOI: 10.57745/sqyvab
title: REFLECTANCE_2020-09-14_017.png
DOI: 10.57745/o3jkhf
title: RGBBACKGROUND_2020-09-14_017.png
DOI: 10.57745/gmvghy
title: RGBSCENE_2020-09-14_017.png
DOI: 10.57745/mppsye
title: RGBVIEWFINDER_2020-09-14_017.png
DOI: 10.57745/bs7lhb
title: 2020-09-14_018.png
DOI: 10.57745/dubelu
title: 2020-09-14_018.hdr
DOI: 10.11583/dtu.23376560
title: WineSensed. Learning to Taste: A Multimodal Wine Dataset

description: Abstract We present WineSensed, a large multimodal wine dataset for studying the relations between visual perception, language, and flavor. The dataset encompasses 897k images of wine labels and 824k reviews of wines curated from the Vivino platform. It has over 350k unique vintages, annotated with year, region, rating, alcohol percentage, price, and grape composition. We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment with 256 participants who were asked to rank wines based on their similarity in flavor, resulting in more than 5k pairwise flavor distances. We propose a low-dimensional concept embedding algorithm that combines human experience with automatic machine similarity kernels. We demonstrate that this shared concept embedding space improves upon separate embedding spaces for coarse flavor classification (alcohol percentage, country, grape, price, rating) and aligns with the intricate human perception of flavor. <br> Dataset structure See https://github.com/thoranna/learning-to-taste.github.io Read about the background: https://thoranna.github.io/learning_to_taste/ How to download Follow the instructions at https://github.com/thoranna/learning-to-taste.github.io
DOI: 10.57745/u7v8sf
title: 2020-09-14_018.raw
DOI: 10.57745/8meuj2
title: DARKREF_2020-09-14_018.hdr
DOI: 10.57745/9gdzoo
title: DARKREF_2020-09-14_018.raw
DOI: 10.57745/k6yn5j
title: WHITEREF_2020-09-14_018.hdr
DOI: 10.57745/mjj3hb
title: WHITEREF_2020-09-14_018.raw
DOI: 10.57745/uoumvh
title: 2020-09-14_018.xml
DOI: 10.57745/spkbgq
title: REFLECTANCE_2020-09-14_018.dat
DOI: 10.57745/vj6h8f
title: REFLECTANCE_2020-09-14_018.hdr
DOI: 10.57745/pqnimi
title: REFLECTANCE_2020-09-14_018.png
DOI: 10.57745/h5qpbe
title: RGBBACKGROUND_2020-09-14_018.png
DOI: 10.57745/mblg4f
title: RGBSCENE_2020-09-14_018.png
DOI: 10.57745/ueubiq
title: RGBVIEWFINDER_2020-09-14_018.png
DOI: 10.57745/kzor64
title: 2020-09-14_019.png
DOI: 10.57745/35u55l
title: 2020-09-14_019.hdr
DOI: 10.57745/1ckdzz
title: 2020-09-14_019.raw
DOI: 10.57745/otxawy
title: DARKREF_2020-09-14_019.hdr
DOI: 10.57745/gwdxqe
title: DARKREF_2020-09-14_019.raw
DOI: 10.57745/lzxwvk
title: WHITEREF_2020-09-14_019.hdr
DOI: 10.5281/zenodo.5776751
title: Corpus and knowledge graphs of the KOnPoTe experiments

description: There are 11 files, used in the evaluation of the KOnPoTe (Knowledge graph/ONtology POpulation from TExts) approach. Inputs : - a French knowledge graph describing the house sales domain (biens_immobiliers.owl) - a French corpus of 78 house sales classified ads (corpus_78_annonces.xml) Gold Standard : - a gold standard knowledge graph describing the populated version of the ontology with the ads from the corpus (gold_standard.owl) Outputs : - 8 files of outputs : output_nameOfLemmatizer_nameOfApproach.owl with 2 lemmatizers : Aker and TreeTagger, and 4 approaches: KOnPoTe, Text-based analysis, Baseline+next, Baseline.
DOI: 10.5281/zenodo.5776752
title: Corpus and knowledge graphs of the KOnPoTe experiments

description: There are 11 files, used in the evaluation of the KOnPoTe (Knowledge graph/ONtology POpulation from TExts) approach. Inputs : - a French knowledge graph describing the house sales domain (biens_immobiliers.owl) - a French corpus of 78 house sales classified ads (corpus_78_annonces.xml) Gold Standard : - a gold standard knowledge graph describing the populated version of the ontology with the ads from the corpus (gold_standard.owl) Outputs : - 8 files of outputs : output_nameOfLemmatizer_nameOfApproach.owl with 2 lemmatizers : Aker and TreeTagger, and 4 approaches: KOnPoTe, Text-based analysis, Baseline+next, Baseline.
DOI: 10.57745/axicic
title: WHITEREF_2020-09-14_019.raw
DOI: 10.57745/bfmust
title: 2020-09-14_019.xml
DOI: 10.57745/dgxhl6
title: REFLECTANCE_2020-09-14_019.dat
DOI: 10.57745/lipipn
title: REFLECTANCE_2020-09-14_019.hdr
DOI: 10.57745/gqechm
title: REFLECTANCE_2020-09-14_019.png
DOI: 10.57745/0nprgj
title: RGBBACKGROUND_2020-09-14_019.png
DOI: 10.57745/5ycutn
title: RGBSCENE_2020-09-14_019.png
DOI: 10.57745/uaqmve
title: RGBVIEWFINDER_2020-09-14_019.png
DOI: 10.57745/lzp6fb
title: 2020-09-14_020.png
DOI: 10.57745/tlfarr
title: 2020-09-14_020.hdr
DOI: 10.57745/gvhvx6
title: 2020-09-14_020.raw
DOI: 10.57745/a8vzai
title: DARKREF_2020-09-14_020.hdr
DOI: 10.57745/8aceay
title: DARKREF_2020-09-14_020.raw
DOI: 10.57745/bxkymo
title: WHITEREF_2020-09-14_020.hdr
DOI: 10.57745/1eimmx
title: WHITEREF_2020-09-14_020.raw
DOI: 10.57745/xzo0lu
title: 2020-09-14_020.xml
DOI: 10.57745/wucjib
title: REFLECTANCE_2020-09-14_020.dat
DOI: 10.57745/jo7e0a
title: REFLECTANCE_2020-09-14_020.hdr
DOI: 10.57745/q9ltdj
title: REFLECTANCE_2020-09-14_020.png