cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18419/darus-2993/4
title: 3 µM DNMT3A R836A CD on MD_222 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/5
title: 2 µM DNMT3A S881A CD on MD_221 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/6
title: 4 µM DNMT3A R882H CD on MD_222 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/7
title: 0.5 µM DNMT3A WT CD 60 min on MD_222.txt

description: :unav
DOI: 10.18419/darus-2993/8
title: 4 µM DNMT3A R882A CD on PB_954 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/9
title: 4 µM DNMT3A R882H CD on PB_956 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/10
title: 4 µM DNMT3A R882H CD on MD_222 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/11
title: 0.5 µM DNMT3A L883A CD on MD_222 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/12
title: 3 µM DNMT3A R887A CD on PB_956 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/13
title: 0.5 µM DNMT3A WT CD 60 min on PB_956.txt

description: :unav
DOI: 10.18419/darus-2993/14
title: 3 µM DNMT3A R836A CD on PB_955 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/15
title: 2 µM DNMT3A S881A CD on PB_954 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/16
title: 2 µM DNMT3A S837A CD on PB_954 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/17
title: 4 µM DNMT3A R882A CD on MD_222 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/18
title: 3 µM DNMT3A R887A CD on MD_221 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/19
title: 0.5 µM DNMT3A L883A CD on PB_955 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/20
title: no enzyme control PB_954.txt

description: :unav
DOI: 10.18419/darus-2993/21
title: 2 µM DNMT3A S881A CD on PB_955 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/22
title: 2 µM DNMT3A S837A CD on MD_221 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/23
title: 4 µM DNMT3A R882A CD on MD_221 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/24
title: 4 µM DNMT3A R882H CD on PB_955 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/25
title: 4 µM DNMT3A R882A CD on PB_955 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/26
title: 4 µM DNMT3A R882A CD on PB_956 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/27
title: 3 µM DNMT3A R887A CD on MD_221 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/28
title: 3 µM DNMT3A R836A CD on PB_956 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/29
title: 3 µM DNMT3A R836A CD on PB_954 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/30
title: 4 µM DNMT3A R882H CD on PB_954 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/31
title: 3 µM DNMT3A R887A CD on PB_955 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/32
title: 3 µM DNMT3A R887A CD on PB_955 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/33
title: 3 µM DNMT3A R836A CD on PB_954 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/34
title: 4 µM DNMT3A R882H CD on PB_954 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/35
title: 4 µM DNMT3A R882H CD on PB_956 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/36
title: 1 µM DNMT3A WT CD 60 min on MD_222.txt

description: :unav
DOI: 10.18419/darus-2993/37
title: 3 µM DNMT3A R887A CD on MD_222 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/38
title: 2 µM DNMT3A S837A CD on PB_955 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/39
title: 3 µM DNMT3A R836A CD on PB_956 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/40
title: 2 µM DNMT3A S837A CD on PB_954 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/41
title: 2 µM DNMT3A S837A CD on PB_955 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/42
title: 4 µM DNMT3A R882H CD on MD_221 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/43
title: 2 µM DNMT3A S837A CD on MD_221 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/44
title: 3 µM DNMT3A R887A CD on MD_222 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/45
title: 2 µM DNMT3A S881A CD on MD_221 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/46
title: 0.5 µM DNMT3A L883A CD on PB_956 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/47
title: 4 µM DNMT3A R882A CD on PB_954 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/48
title: 3 µM DNMT3A R887A CD on PB_956 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/49
title: 2 µM DNMT3A S837A CD on MD_222 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/50
title: 4 µM DNMT3A R882H CD on PB_955 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/51
title: 2 µM DNMT3A S837A CD on PB_956 120 min.tabular

description: :unav
DOI: 10.5281/zenodo.10809442
title: HistoArtifacts

description: This dataset contains five notable histological artifacts: blur, blood (hemorrhage), air bubbles, folded tissue, and damaged tissue. This dataset is used in the following works, and a description of the dataset can be found at https://arxiv.org/abs/2403.07743.

The full dataset is explained and used in  the article, "Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs.". https://arxiv.org/abs/2403.07743

See the detailed video explanation behind the motivation of artifact detection in computational pathology. in the video paper: "Extract, detect, eliminate: Enhancing reliability and performance of computational pathology through artifact processing pipelines" https://www.sciencetalks-journal.com/article/S2772-5693(24)00013-6/fulltext

Please cite the following papers while using the dataset, in full or partially:. 

A sub-dataset contains folded tissues extracted at 20x and blur class used in the paper "Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images".  https://www.sciencedirect.com/science/article/pii/S0895611123001398

A sub-dataset using air bubbles is used in the paper: "Vision transformers for small histological datasets learned through knowledge distillation" https://link.springer.com/chapter/10.1007/978-3-031-33380-4_13https://arxiv.org/abs/2305.17370

A sub-dataset using blood and damaged tissue is used in the paper: "Quantifying the effect of color processing on blood and damaged tissue detection in whole slide images" https://ieeexplore.ieee.org/abstract/document/9816283

"Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs.". https://arxiv.org/abs/2403.07743

"The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review" https://ieeexplore.ieee.org/document/9777677
DOI: 10.18419/darus-2993/52
title: 4 µM DNMT3A R882A CD on PB_956 120 min.tabular

description: :unav
DOI: 10.5281/zenodo.10809441
title: HistoArtifacts

description: This dataset contains five notable histological artifacts: blur, blood (hemorrhage), air bubbles, folded tissue, and damaged tissue. This dataset is used in the following works, and a description of the dataset can be found at https://arxiv.org/abs/2403.07743.

The full dataset is explained and used in  the article, "Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs.". https://arxiv.org/abs/2403.07743

See the detailed video explanation behind the motivation of artifact detection in computational pathology. in the video paper: "Extract, detect, eliminate: Enhancing reliability and performance of computational pathology through artifact processing pipelines" https://www.sciencetalks-journal.com/article/S2772-5693(24)00013-6/fulltext

Please cite the following papers while using the dataset, in full or partially:. 

A sub-dataset contains folded tissues extracted at 20x and blur class used in the paper "Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images".  https://www.sciencedirect.com/science/article/pii/S0895611123001398

A sub-dataset using air bubbles is used in the paper: "Vision transformers for small histological datasets learned through knowledge distillation" https://link.springer.com/chapter/10.1007/978-3-031-33380-4_13https://arxiv.org/abs/2305.17370

A sub-dataset using blood and damaged tissue is used in the paper: "Quantifying the effect of color processing on blood and damaged tissue detection in whole slide images" https://ieeexplore.ieee.org/abstract/document/9816283

"Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs.". https://arxiv.org/abs/2403.07743

"The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review" https://ieeexplore.ieee.org/document/9777677
DOI: 10.18419/darus-2993/53
title: 4 µM DNMT3A R882H CD on MD_221 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/54
title: no enzyme control PB_956.txt

description: :unav
DOI: 10.18419/darus-2993/55
title: 3 µM DNMT3A R836A CD on MD_221 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/56
title: 0.5 µM DNMT3A L883A CD on MD_222 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/57
title: 2 µM DNMT3A S881A CD on PB_954 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/58
title: 3 µM DNMT3A R887A CD on PB_954 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/59
title: 1 µM DNMT3A WT CD 60 min on PB_954.txt

description: :unav
DOI: 10.18419/darus-2993/60
title: 2 µM DNMT3A S837A CD on PB_956 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/61
title: 2 µM DNMT3A S881A CD on PB_955 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/62
title: 0.5 µM DNMT3A L883A CD on MD_221 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/63
title: 2 µM DNMT3A S881A CD on PB_956 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/64
title: no enzyme control PB_955.txt

description: :unav
DOI: 10.18419/darus-2993/65
title: no enzyme control MD_221.txt

description: :unav
DOI: 10.18419/darus-2993/66
title: no enzyme control MD_222.txt

description: :unav
DOI: 10.18419/darus-2993/67
title: 4 µM DNMT3A R882A CD on PB_955 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/68
title: 0.5 µM DNMT3A L883A CD on PB_956 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/69
title: 0.5 µM DNMT3A WT CD 60 min on PB_955.txt

description: :unav
DOI: 10.18419/darus-2993/70
title: 3 µM DNMT3A R836A CD on MD_222 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/71
title: 2 µM DNMT3A S837A CD on MD_222 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/72
title: 2 µM DNMT3A S881A CD on MD_222 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/73
title: 0.5 µM DNMT3A L883A CD on PB_955 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/74
title: 3 µM DNMT3A R836A CD on MD_221 30 min.tabular

description: :unav
DOI: 10.5281/zenodo.10822964
title: DEDICAT 6G Enhanced Experience Android streaming application delay measurements

description: Performance measurements for the developed streaming application and comparison with the latest smartphones.
DOI: 10.5281/zenodo.10822965
title: DEDICAT 6G Enhanced Experience Android streaming application delay measurements

description: Performance measurements for the developed streaming application and comparison with the latest smartphones.
DOI: 10.18419/darus-2993/75
title: 3 µM DNMT3A R887A CD on PB_954 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/76
title: 0.5 µM DNMT3A L883A CD on PB_954 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/77
title: 1 µM DNMT3A WT CD 60 min on PB_955.txt

description: :unav
DOI: 10.18419/darus-2993/78
title: 0.5 µM DNMT3A WT CD 60 min on PB_954.txt

description: :unav
DOI: 10.6084/m9.figshare.24721164
title: <i>TRPA5</i><i> </i>encodes a thermosensitive ankyrin ion channel receptor in a triatomine insect (Source data Fig. S6)

description: <i>TRPA5</i><i> </i>encodes a thermosensitive ankyrin ion channel receptor in a triatomine insectMarjorie A. Liénard, David Baez-Nieto, Cheng-Chia Tsai, Wendy A. Valencia-Montoya, Balder Werin, Urban Johanson, Jean-Marc Lassance, Jen Q. Pan, Nanfang Yu, Naomi E. PierceAs ectotherms, insects need heat-sensitive receptors to monitor environmental temperatures and facilitate thermoregulation. We show that <i>TRPA5,</i><i> </i>a class of ankyrin transient receptor potential channels absent in dipteran genomes, may function as insect heat receptors. In the triatomine bug <i>Rhodnius prolixus</i><i> </i>(Order: Hemiptera), a vector of Chagas disease, the channel RpTRPA5B displays a uniquely high thermosensitivity, with biophysical determinants including a large channel activation enthalpy change (72 kcal/mol), a high temperature coefficient (Q<sub>10</sub> = 25), and <i>in vitro</i>temperature-induced currents<i> </i>from 53 °C to 68 °C (T<sub>0.5</sub>= 58.6 °C)<i>,</i> similar to noxious TRPV receptors in mammals. Monomeric and tetrameric ion channel structure predictions show reliable parallels with fruit fly dTRPA1, with structural uniqueness in ankyrin repeat domains and the channel selectivity filter, potential TRP functional modulator regions. Overall, the finding of a member of <i>TRPA5</i> as a temperature-activated receptor illustrates the diversity of insect molecular heat detectors.
DOI: 10.6084/m9.figshare.24721164.v1
title: <i>TRPA5</i><i> </i>encodes a thermosensitive ankyrin ion channel receptor in a triatomine insect (Source data Fig. S6)

description: <i>TRPA5</i><i> </i>encodes a thermosensitive ankyrin ion channel receptor in a triatomine insectMarjorie A. Liénard, David Baez-Nieto, Cheng-Chia Tsai, Wendy A. Valencia-Montoya, Balder Werin, Urban Johanson, Jean-Marc Lassance, Jen Q. Pan, Nanfang Yu, Naomi E. PierceAs ectotherms, insects need heat-sensitive receptors to monitor environmental temperatures and facilitate thermoregulation. We show that <i>TRPA5,</i><i> </i>a class of ankyrin transient receptor potential channels absent in dipteran genomes, may function as insect heat receptors. In the triatomine bug <i>Rhodnius prolixus</i><i> </i>(Order: Hemiptera), a vector of Chagas disease, the channel RpTRPA5B displays a uniquely high thermosensitivity, with biophysical determinants including a large channel activation enthalpy change (72 kcal/mol), a high temperature coefficient (Q<sub>10</sub> = 25), and <i>in vitro</i>temperature-induced currents<i> </i>from 53 °C to 68 °C (T<sub>0.5</sub>= 58.6 °C)<i>,</i> similar to noxious TRPV receptors in mammals. Monomeric and tetrameric ion channel structure predictions show reliable parallels with fruit fly dTRPA1, with structural uniqueness in ankyrin repeat domains and the channel selectivity filter, potential TRP functional modulator regions. Overall, the finding of a member of <i>TRPA5</i> as a temperature-activated receptor illustrates the diversity of insect molecular heat detectors.
DOI: 10.18419/darus-2993/79
title: 2 µM DNMT3A S881A CD on MD_222 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/80
title: 1 µM DNMT3A WT CD 60 min on PB_956.txt

description: :unav
DOI: 10.18419/darus-2993/81
title: 0.5 µM DNMT3A L883A CD on PB_954 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/82
title: 1 µM DNMT3A WT CD 60 min on MD_221.txt

description: :unav
DOI: 10.18419/darus-2993/83
title: 3 µM DNMT3A R836A CD on PB_955 120 min.tabular

description: :unav
DOI: 10.18419/darus-2993/84
title: 2 µM DNMT3A S881A CD on PB_956 30 min.tabular

description: :unav
DOI: 10.18419/darus-2993/85
title: 0.5 µM DNMT3A WT CD 60 min on MD_221.txt

description: :unav
DOI: 10.18419/darus-2993/86
title: 2 µM DNMT3A R887A 120 min CN sub.txt

description: :unav
DOI: 10.18419/darus-2993/87
title: 4 µM DNMT3A R836A 30 min CN sub.txt

description: :unav
DOI: 10.18419/darus-2993/88
title: 4 µM DNMT3A S881A 120 min CN sub.txt

description: :unav
DOI: 10.5281/zenodo.10786751
title: Supporting data for "The benefit of in silico predicted spectral libraries in data-independent acquisition data analysis workflows"
DOI: 10.5281/zenodo.10786752
title: Supporting data for "The benefit of in silico predicted spectral libraries in data-independent acquisition data analysis workflows"
DOI: 10.14473/csda/t5qchg/ynpim0
title: TI_CSDA00181-CSDA00194.pdf

description: Technická informace_CSDA00181-CSDA00194
DOI: 10.14473/csda/t5qchg/knqik0
title: D_CSDA00181-CSDA00194.pdf

description: Dotazník_CSDA00181-CSDA00194
DOI: 10.18419/darus-2158/1
title: OmpF_033.nd2

description: E.coli OmpF reconstituted in non-modified agarose supported droplet interface lipid bilayer membrane
DOI: 10.14473/csda/t5qchg/n35sar
title: 03_PARD kraj - volby do PS 2013_data_opr.tab

description: Data_CSDA00183
DOI: 10.14473/csda/t5qchg/exwssk
title: D_CSDA00183_pardubicky.pdf

description: Dotazník_CSDA00183_pardubicky
DOI: 10.18419/darus-2158/2
title: OmpF_002.nd2

description: E.coli OmpF reconstituted in non-modified agarose supported droplet interface lipid bilayer membrane
DOI: 10.14473/csda/t5qchg/zxiteb
title: OM_CSDA00181-CSDA00194.pdf

description: Ostatní materiály_CSDA00181-CSDA00194
DOI: 10.18419/darus-2158/3
title: OmpF_004.nd2

description: E.coli OmpF reconstituted in non-modified agarose supported droplet interface lipid bilayer membrane
DOI: 10.18419/darus-2158/4
title: OmpF_003.nd2

description: E.coli OmpF reconstituted in non-modified agarose supported droplet interface lipid bilayer membrane
DOI: 10.18419/darus-2158/5
title: OmpF_005.nd2

description: E.coli OmpF reconstituted in non-modified agarose supported droplet interface lipid bilayer membrane
DOI: 10.5281/zenodo.10829961
title: Seshat-NLP Dataset Pre-Release

description: This is a pre-release of Seshat-NLP, a dataset of labelled text segments derived from the Seshat Databank. These text segments were originally used in the Seshat Databank to justify the coding of historical "facts".  A data point in the Seshat Databank would describe a property of a past society at a certain time (-range). We use these data points with their textual justifications to extract a NLP dataset of text segments accompanied by topic labels.

General Overview

The Dataset is organised around unique text segments (i.e.: each row one unique segment), these segments are connected with labels that designate the historical information that is contained within the text. Each segment has at least one 4-tuple of labels associated with it but can have more. The labels are ("variable_name", "variable_id", "value", and "polity_id").

Below is a simplified example row in our dataset (exemplary data!):




Description
Labels ("variable", "var_id", "value", "polity")
Reference 




Thebes was the capital …
[("Capital", "…","Thebes", "Egypt Middle Kingdom"),…]


{"Title" : "The Oxford Encyclopedia of …", "Author" : "…", "DOI" : "…", …}





Note on Source Literature Text Segments

Our dataset partially consists of segments taken from scientific literature on history, we also pair these segments with labels that denote their content. We are currently looking into the legal considerations of releasing such data. In the meanwhile, we have added information to our dataset that allows the identification of the pertaining documents for each description. 

In Depth Explanation of the Dataset

List of files in the release:

Seshat_NLP.sql

This file is a PostgreSQL dump that can be used to instantiate the PostgreSQL table with all the data.The table zenodoexport has the following columns:




Column Name
Column Description


id
row identifier


description
textual justification of coded value


labels
labels for description


reference_information
information required to retrieve documents


description_hash
utility column


zodero_id 
utility column




Hierarchy_graph.gexf

The hierarchy_graph.gexf file is a xml based export of the hierarchy graph that can be used to tie variables to their hierarchical position in the Seshat codebook.  

Explanation of  Labels Column

The labels column contains a list of 4-tuples which in order denote "variable_name", "variable_id", "value", and "polity_id".We use this structure to allow for a single segment/description to have multiple 4-tuples of labels, this is useful when the same of description has been used to justify multiple "facts" in the original Seshat Databank.The variable_ids can be used to tie variable labels to nodes in the hierarchy of the Seshat codebook.
DOI: 10.5281/zenodo.10829962
title: Seshat-NLP Dataset Pre-Release

description: This is a pre-release of Seshat-NLP, a dataset of labelled text segments derived from the Seshat Databank. These text segments were originally used in the Seshat Databank to justify the coding of historical "facts".  A data point in the Seshat Databank would describe a property of a past society at a certain time (-range). We use these data points with their textual justifications to extract a NLP dataset of text segments accompanied by topic labels.

General Overview

The Dataset is organised around unique text segments (i.e.: each row one unique segment), these segments are connected with labels that designate the historical information that is contained within the text. Each segment has at least one 4-tuple of labels associated with it but can have more. The labels are ("variable_name", "variable_id", "value", and "polity_id").

Below is a simplified example row in our dataset (exemplary data!):




Description
Labels ("variable", "var_id", "value", "polity")
Reference 




Thebes was the capital …
[("Capital", "…","Thebes", "Egypt Middle Kingdom"),…]


{"Title" : "The Oxford Encyclopedia of …", "Author" : "…", "DOI" : "…", …}





Note on Source Literature Text Segments

Our dataset partially consists of segments taken from scientific literature on history, we also pair these segments with labels that denote their content. We are currently looking into the legal considerations of releasing such data. In the meanwhile, we have added information to our dataset that allows the identification of the pertaining documents for each description. 

In Depth Explanation of the Dataset

List of files in the release:

Seshat_NLP.sql

This file is a PostgreSQL dump that can be used to instantiate the PostgreSQL table with all the data.The table zenodoexport has the following columns:




Column Name
Column Description


id
row identifier


description
textual justification of coded value


labels
labels for description


reference_information
information required to retrieve documents


description_hash
utility column


zodero_id 
utility column




Hierarchy_graph.gexf

The hierarchy_graph.gexf file is a xml based export of the hierarchy graph that can be used to tie variables to their hierarchical position in the Seshat codebook.  

Explanation of  Labels Column

The labels column contains a list of 4-tuples which in order denote "variable_name", "variable_id", "value", and "polity_id".We use this structure to allow for a single segment/description to have multiple 4-tuples of labels, this is useful when the same of description has been used to justify multiple "facts" in the original Seshat Databank.The variable_ids can be used to tie variable labels to nodes in the hierarchy of the Seshat codebook.
DOI: 10.18419/darus-2158/6
title: OmpF_020.nd2

description: E.coli OmpF reconstituted in non-modified agarose supported droplet interface lipid bilayer membrane
DOI: 10.18419/darus-2158/7
title: OmpF_023.nd2

description: E.coli OmpF reconstituted in non-modified agarose supported droplet interface lipid bilayer membrane
DOI: 10.18419/darus-2158/8
title: OmpF_015.nd2

description: E.coli OmpF reconstituted in non-modified agarose supported droplet interface lipid bilayer membrane
DOI: 10.18419/darus-2158/9
title: OmpF_013.nd2

description: E.coli OmpF reconstituted in non-modified agarose supported droplet interface lipid bilayer membrane
DOI: 10.18419/darus-2158/10
title: OmpF_012.nd2

description: E.coli OmpF reconstituted in non-modified agarose supported droplet interface lipid bilayer membrane