cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48436/16vvs-8ew70
title: ORCAS-I

description: ORCAS-I is an annotated version of ORCAS dataset (Craswell et al., 2020) annotated with user intents using weak supervision. It allows you to train your algorithm on various types of user intents. Those intents are initially taken from Broder's (2002) classification: informational, navigational and transactional. We also refined this classification and added two subcategories inside the informational category: factual and instrumental. If the intent did not get any label inside the informational category it was classified as abstain. 
ORCAS-I consists of the following files:
a) ORCAS-I-18M.tsv
A complete ORCAS data set which contains 18 million unique query-urls pairs.

dataset size: 18,823,602unique queries: 10,405,339unique URLs: 1,422,029unique domains: 241,199
 
b) ORCAS-I-2M.tsv
A 2M subset of ORCAS-I-18M.tsv that we used for our experiments with different machine learning algorithms.

dataset size: 2,000,000unique queries: 1,796,652unique URLs: 618,679unique domains: 126,001
Both ORCAS-I-18M and ORCAS-I-2M contain the following columns:

qid: the id of the query
query: the text of the query
url: the url that the user clicked
did: the document from TREC deep learning track that the url leads to
level_1: first level of annotation which has three top level categories: informational, navigational and transactional
level_2: second level of annotation (only classifies according to factual and instrumental categories, so all the other intents in the column are classified as abstain)
label: final intent label. Provides the annotation for informational, navigational and transactional categories and also for factual, instrumental and abstain subcategories
data_split: either 'train' or 'validation' that corresponds to split used during the original experiments
You can train your classifier either on the 3 top level categories (column 'level_1') or on the full taxonomy (column 'label').
 
c) ORCAS-I-gold.tsv
This is a test file that contains 1000 randomly selected queries from the full dataset (they are excluded from the 2M sample). These queries were manually annotated by two IR specialists. 

dataset size: 1,000unique queries: 1,000unique URLs: 995unique domains: 700
ORCAS-I-gold contains the following columns:

qid: the id of the query
query: the text of the query
url: the url that the user clicked
did: the document from TREC deep learning track that the url leads to
label_manual - manually annotated intent
data_split: always equal to 'test'
DOI: 10.48436/5caar-3r468
title: Legal CaPER Benchmark

description: The Legal Case Passage Extraction and Retrieval benchmark is an information retrieval benchmark collection for court case passage retrieval. Specifically, it is a collection for evaluating Cited Case Passage Retrieval (CCPR) and contains case passages from the Austrian building regulations domain (Source: RIS). The following files are included in the dataset:

full_collection.tsv
A tab separated file containing the passage texts of court cases from the building regulations domain. Column 1 contains the ID of the passage, Column 2 contains the passage text and Column 3 contains the case ID (Geschäftszahl) of the origin case of the passage.

queries.tsv
A tab separated file containing the queries / topics for which relevance assessments exist in this collection. Column 1 contains the ID of the query, Column 2 contains the query passage text and Column 3 contains the case ID (Geschäftszahl) of the cited case. For the task of CCPR, it is intended that results are additionally filtered based on exact matches of the case ID. For each query, only relevance assessments exist for passages that match the case ID of column 3.

qrel.json
Contains relevance assessments for each query. In this dictionary, a passage from the full collection is relevant for a query if qrel[<query ID>][<passage ID>] == 1. If a passage ID is not in qrel[<query ID>], it is not relevant. Relevance assessments only exist for full collection passages that match the case ID of the query.

qrel.json.txt
A conversion of the qrel.json file to be compatible with trec eval.
DOI: 10.48436/eyjff-bv654
title: Legal CaPER Benchmark

description: The Legal Case Passage Extraction and Retrieval benchmark is an information retrieval benchmark collection for court case passage retrieval. Specifically, it is a collection for evaluating Cited Case Passage Retrieval (CCPR) and contains case passages from the Austrian building regulations domain (Source: RIS). The following files are included in the dataset:

full_collection.tsv
A tab separated file containing the passage texts of court cases from the building regulations domain. Column 1 contains the ID of the passage, Column 2 contains the passage text and Column 3 contains the case ID (Geschäftszahl) of the origin case of the passage.

queries.tsv
A tab separated file containing the queries / topics for which relevance assessments exist in this collection. Column 1 contains the ID of the query, Column 2 contains the query passage text and Column 3 contains the case ID (Geschäftszahl) of the cited case. For the task of CCPR, it is intended that results are additionally filtered based on exact matches of the case ID. For each query, only relevance assessments exist for passages that match the case ID of column 3.

qrel.json
Contains relevance assessments for each query. In this dictionary, a passage from the full collection is relevant for a query if qrel[<query ID>][<passage ID>] == 1. If a passage ID is not in qrel[<query ID>], it is not relevant. Relevance assessments only exist for full collection passages that match the case ID of the query.

qrel.json.txt
A conversion of the qrel.json file to be compatible with trec eval.
DOI: 10.48436/pp7xz-n9a06
title: ORCAS-I

description: ORCAS-I is an annotated version of ORCAS dataset (Craswell et al., 2020) annotated with user intents using weak supervision. It allows you to train your algorithm on various types of user intents. Those intents are initially taken from Broder's (2002) classification: informational, navigational and transactional. We also refined this classification and added two subcategories inside the informational category: factual and instrumental. If the intent did not get any label inside the informational category it was classified as abstain. ORCAS-I consists of the following files:a) ORCAS-I-18M.tsvA complete ORCAS data set which contains 18 million unique query-urls pairs.dataset size: 18,823,602unique queries: 10,405,339unique URLs: 1,422,029unique domains: 241,199 b) ORCAS-I-2M.tsvA 2M subset of ORCAS-I-18M.tsv that we used for our experiments with different machine learning algorithms.dataset size: 2,000,000unique queries: 1,796,652unique URLs: 618,679unique domains: 126,001Both ORCAS-I-18M and ORCAS-I-2M contain the following columns:qid: the id of the queryquery: the text of the queryurl: the url that the user clickeddid: the document from TREC deep learning track that the url leads tolevel_1: first level of annotation which has three top level categories: informational, navigational and transactionallevel_2: second level of annotation (only classifies according to factual and instrumental categories, so all the other intents in the column are classified as abstain)label: final intent label. Provides the annotation for informational, navigational and transactional categories and also for factual, instrumental and abstain subcategoriesdata_split: either 'train' or 'validation' that corresponds to split used during the original experimentsYou can train your classifier either on the 3 top level categories (column 'level_1') or on the full taxonomy (column 'label'). c) ORCAS-I-gold.tsvThis is a test file that contains 1000 randomly selected queries from the full dataset (they are excluded from the 2M sample). These queries were manually annotated by two IR specialists. dataset size: 1,000unique queries: 1,000unique URLs: 995unique domains: 700ORCAS-I-gold contains the following columns:qid: the id of the queryquery: the text of the queryurl: the url that the user clickeddid: the document from TREC deep learning track that the url leads tolabel_manual - manually annotated intentdata_split: always equal to 'test'
DOI: 10.48436/j698g-7nj93
title: arXiv:2305.19826  - Hidden covalent insulator and spin excitations in SrRu2O6

description: Data for Phys. Rev. B 108, 195137 (2023):
3-orbital model of Ru t2g orbitals, Hund's coulings J=0.16, 0.19 and 0.22 eV
w (soc) and w/o (su2) spin-orbit coupling
converged self-energies, 2-p irreducible vertices, dynamical susceptibilities before analytic continuation
DOI: 10.48436/qspn0-n8j23
title: Data supporting: Cracking behaviour of textile-reinforced concrete with varying concrete cover and textile surface finish

description: Full dataset behind the paper on the cracking behaviour of textile-reinforced concrete with varying concrete cover and textile surface finish (https://doi.org/10.1016/j.compstruct.2023.116859)
DOI: 10.48436/t1fzp-d2346
title: Data supporting: Cracking behaviour of textile-reinforced concrete with varying concrete cover and textile surface finish

description: Full dataset behind the paper on the cracking behaviour of textile-reinforced concrete with varying concrete cover and textile surface finish (https://doi.org/10.1016/j.compstruct.2023.116859)
DOI: 10.48436/wqkr6-02305
title: arXiv:2305.19826  - Hidden covalent insulator and spin excitations in SrRu2O6

description: Data for Phys. Rev. B 108, 195137 (2023):
3-orbital model of Ru t2g orbitals, Hund's coulings J=0.16, 0.19 and 0.22 eV
w (soc) and w/o (su2) spin-orbit coupling
converged self-energies, 2-p irreducible vertices, dynamical susceptibilities before analytic continuation
DOI: 10.48436/7qfnf-qpy18
title: CAD files (step-format) for inserts and stamps

description: CAD files for well plates used in high-content imaging-based phenotypic drug screening in zebrafish xenografts. Files are in STEP-format.
DOI: 10.48436/bevwn-qwg98
title: CAD files (step-format) for inserts and stamps

description: CAD files for well plates used in high-content imaging-based phenotypic drug screening in zebrafish xenografts. Files are in STEP-format.
DOI: 10.48436/bjhgw-a8236
title: Calculation models: The LT-bridge construction method for the material-saving and fast construction of post-tensioned concrete bridges

description: Calculation models behind the paper on the LT-bridge construction method for the material-saving and fast construction of post-tensioned concrete bridges (https://doi.org/10.1002/best.202400007). 
The calculation models on the basis of which the formwork, reinforcement, and tendon plans were created are provided here.The *.sofistik and *.dwg files can be opened with the Sofistik 2022 software.The *.inc files can be opened with the INCA2 software (https://www.u-pfeiffer.de/inca2/inca2-09.html).If any further information is needed please contact franz.untermarzoner@tuwien.ac.at
DOI: 10.48436/wmb10-pw598
title: Calculation models: The LT-bridge construction method for the material-saving and fast construction of post-tensioned concrete bridges

description: Calculation models behind the paper on the LT-bridge construction method for the material-saving and fast construction of post-tensioned concrete bridges (https://doi.org/10.1002/best.202400007). 
The calculation models on the basis of which the formwork, reinforcement, and tendon plans were created are provided here.The *.sofistik and *.dwg files can be opened with the Sofistik 2022 software.The *.inc files can be opened with the INCA2 software (https://www.u-pfeiffer.de/inca2/inca2-09.html).If any further information is needed please contact franz.untermarzoner@tuwien.ac.at
DOI: 10.48436/5f81w-b7m37
title: Numerical results for "Non-linear responses and three-particle correlators in correlated electron systems exemplified by the Anderson impurity model"

description: This dataset contains the numerical data and plot scripts to reproduce the figures from the manuscript "Non-linear responses and three-particle correlators in correlated electron systems exemplified by the Anderson impurity model" published in Physical Review B and also available as preprint on arXiv. See the README file for more details.
DOI: 10.48436/91gy5-ye029
title: Numerical results for "Non-linear responses and three-particle correlators in correlated electron systems exemplified by the Anderson impurity model"

description: ℹ️ Access to the files of this version is restricted since figures under APS copyright were uploaded accidentally.
This dataset contains the numerical data and plot scripts to reproduce the figures from the manuscript "Non-linear responses and three-particle correlators in correlated electron systems exemplified by the Anderson impurity model" submitted for publication to Physical Review B and already available as preprint on arXiv. See the README file for more details.
DOI: 10.48436/s8mva-42q75
title: Numerical results for "Non-linear responses and three-particle correlators in correlated electron systems exemplified by the Anderson impurity model"

description: This dataset contains the numerical data and plot scripts to reproduce the figures from the manuscript "Non-linear responses and three-particle correlators in correlated electron systems exemplified by the Anderson impurity model" published in Physical Review B and also available as preprint on arXiv. See the README file for more details.
DOI: 10.48436/5bnmm-w5z52
title: Brick wall - Wertheimstein Park, Wien
DOI: 10.48436/t0q4v-a2z90
title: Brick wall - Wertheimstein Park, Wien
DOI: 10.48436/69rb1-p5j60
title: Behavioral Correlates of Environmental Familiarity (Metadata)

description: Behavioral Correlates of Environmental Familiarity in Pedestrian Wayfinders
This repository contains all the data collected during my MSCA Global Fellowship (duration: 3/2022-8/2024).
Context and methodology

This research belongs to the domain of GIScience.
The dataset will contain online study, in-situ study, and systematic review data.
Technical details

The dataset is structured in a way such that the current record is used for metadata only (in Computer Science terms one can think of it as a root node in a graph).
DOI: 10.48436/bzpx1-vax74
title: Behavioral Correlates of Environmental Familiarity (Metadata)

description: Behavioral Correlates of Environmental Familiarity in Pedestrian Wayfinders
This repository contains all the data collected during my MSCA Global Fellowship (duration: 3/2022-8/2024).
Context and methodology

This research belongs to the domain of GIScience.
The dataset will contain online study, in-situ study, and systematic review data.
Technical details

The dataset is structured in a way such that the current record is used for metadata only (in Computer Science terms one can think of it as a root node in a graph).
DOI: 10.48436/gc1n8-14v35
title: Historical Air Quality Data Austria Umweltbundesamt

description: This digital record contains historical air pollution and air quality data from approximately 20 air monitoring stations in Vienna, spanning the years from 1980 to 2021. The data was provided by the Umweltbundesamt and is stored in its original form in this record. This record forms the basis of an analysis carried out in a bachelor's thesis at the TU Wien.
The record is composed of three files:

Messdaten-Wien-1986-2021-für-TU.tar.gz: This .tar file contains the unmodified, raw historical air quality measurements gathered in Vienna.
Datenformat-für-TU.txt: This text file explains the structure of the data in the .tar file. It should be noted that the format rules defined in this file are those given to us by the Umweltbundesamt. However, these formats are not consistently adhered to throughout the dataset. Older data, in particular, exhibit different formats in terms of time notation, data separation characters, and other formatting elements.
Messstellen-Koordinaten-für-TU.xlsx: This Excel file provides additional information about each of the monitoring stations included in the data. For each station, you'll find details such as its name and physical location.
DOI: 10.48436/mtha8-w2406
title: Historical Air Quality Data Austria Umweltbundesamt

description: This digital record contains historical air pollution and air quality data from approximately 20 air monitoring stations in Vienna, spanning the years from 1980 to 2021. The data was provided by the Umweltbundesamt and is stored in its original form in this record. This record forms the basis of an analysis carried out in a bachelor's thesis at the TU Wien.
The record is composed of three files:

Messdaten-Wien-1986-2021-für-TU.tar.gz: This .tar file contains the unmodified, raw historical air quality measurements gathered in Vienna.
Datenformat-für-TU.txt: This text file explains the structure of the data in the .tar file. It should be noted that the format rules defined in this file are those given to us by the Umweltbundesamt. However, these formats are not consistently adhered to throughout the dataset. Older data, in particular, exhibit different formats in terms of time notation, data separation characters, and other formatting elements.
Messstellen-Koordinaten-für-TU.xlsx: This Excel file provides additional information about each of the monitoring stations included in the data. For each station, you'll find details such as its name and physical location.
DOI: 10.48436/jdbam-01y88
title: Data for "Conductivity at Varying Frequencies as a method for differentiating Strawberry Ripeness and association with Colour Acceptance of Strawberry Nectars"

description: Description
This data set contains the raw conductivity data used to produce the paper "Conductivity at Varying Frequencies as a method for differentiating Strawberry Ripeness and association with Colour Acceptance of Strawberry Nectars" as a part of the HiStabJuice Project. 
Context and methodology

This csv file contains the conductivity data used to derive the HLR, and produce the statistics, tables and figures for the paper.
Each column heading refers to a different frequency in Hz, and the Conductivity is in mS/cm
DOI: 10.48436/jgvdr-01v58
title: Data for "Conductivity at Varying Frequencies as a method for differentiating Strawberry Ripeness and association with Colour Acceptance of Strawberry Nectars"

description: Description
This data set contains the raw conductivity data used to produce the paper "Conductivity at Varying Frequencies as a method for differentiating Strawberry Ripeness and association with Colour Acceptance of Strawberry Nectars" as a part of the HiStabJuice Project. 
Context and methodology

This csv file contains the conductivity data used to derive the HLR, and produce the statistics, tables and figures for the paper.
Each column heading refers to a different frequency in Hz, and the Conductivity is in mS/cm
DOI: 10.48436/a2acd-nbs26
title: arXiv-1901.10256:Chemical pressure effect on the optical conductivity of the nodal-line semimetals ZrSiY(Y=S,Se,Te) and ZrGeY(Y=S,Te)

description: This is a repository for the theory part of the above paper published in Phys. Rev. B 99, 125154 (2019).
zrsis-manuscript.zip: contains manuscript and figures
ZrXY.tgz: research data obtained with Wien2k and wannier90: basic scf calculation, optical conductivities, wannierization, tight-binding fits for various hopping ranges
DOI: 10.48436/jvzvx-zb191
title: arXiv-1901.10256:Chemical pressure effect on the optical conductivity of the nodal-line semimetals ZrSiY(Y=S,Se,Te) and ZrGeY(Y=S,Te)

description: This is a repository for the theory part of the above paper published in Phys. Rev. B 99, 125154 (2019).
zrsis-manuscript.zip: contains manuscript and figures
ZrXY.tgz: research data obtained with Wien2k and wannier90: basic scf calculation, optical conductivities, wannierization, tight-binding fits for various hopping ranges
DOI: 10.48436/4at98-as884
title: arXiv-1612.07576: Excitonic magnetism in d6 perovskites

description: Data used for Phys. Rev. B 95, 115131 (2017).
Wien2k input and output data for various interaction parameters (U, J), phases and structures (experimental and ideal cubic perovskite). 
DMFT calculations (w2dynamics) for ideal cubic structure - unpublished.
DOI: 10.48436/ake2x-dqf70
title: Mobility Market Indicators and Macroeconomic Indicators for the MaaS Status Index (MSI) in Austria (2017-2028)

description: Dataset description
The datasets include mobility market indicators and macroeconomic indicators for Austria, which were used to calculate the Mobility as a Service (MaaS) Status Index (MSI). The MSI evaluates the readiness and potential for implementing Mobility as a Service (MaaS) in Austria. The datasets cover two distinct periods: 2017-2022 (T1) and 2023-2028 (T2). The indicators include annual revenues, vehicle costs, number of users, market shares, GDP per capita, urbanization rates, and investments in transportation infrastructure, among others.
Context and methodology
Each indicator is represented by the average annual growth rate, a mean value, and a normalized mean value (min-max-normalization) for period T1 and T2. The data were sourced from Statista (2024)
Technical details
The dataset contains two Microsoft Excel files (one for mobility market indicators, one for macroeconomic indicators). Other than Microsoft Excel, there is no additional software needed to investigate the data.
DOI: 10.48436/s7xg9-2hh50
title: Mobility Market Indicators and Macroeconomic Indicators for the MaaS Status Index (MSI) in Austria (2017-2028)

description: Dataset description
The datasets include mobility market indicators and macroeconomic indicators for Austria, which were used to calculate the Mobility as a Service (MaaS) Status Index (MSI). The MSI evaluates the readiness and potential for implementing Mobility as a Service (MaaS) in Austria. The datasets cover two distinct periods: 2017-2022 (T1) and 2023-2028 (T2). The indicators include annual revenues, vehicle costs, number of users, market shares, GDP per capita, urbanization rates, and investments in transportation infrastructure, among others.
Context and methodology
Each indicator is represented by the average annual growth rate, a mean value, and a normalized mean value (min-max-normalization) for period T1 and T2. The data were sourced from Statista (2024)
Technical details
The dataset contains two Microsoft Excel files (one for mobility market indicators, one for macroeconomic indicators). Other than Microsoft Excel, there is no additional software needed to investigate the data.
DOI: 10.48436/v858f-jnx45
title: arXiv-1612.07576: Excitonic magnetism in d6 perovskites

description: Data used for Phys. Rev. B 95, 115131 (2017).
Wien2k input and output data for various interaction parameters (U, J), phases and structures (experimental and ideal cubic perovskite). 
DMFT calculations (w2dynamics) for ideal cubic structure - unpublished.
DOI: 10.48436/82nbf-k4r84
title: 3D Reconstruction Model of the Former Synagogue in the Holzmeistergasse 12, Vienna  (1875–1877 | Architect: Andreas Streit)

description: This record contains model data originally created in the framework of the masters' thesis of Friedrich Schmidt at TU Wien (2008).
Facts: Synagogue of the Israelitische Kultusgemeinde Wien (Jewish Community Vienna) | Erected 1875–1877; foundation stone laid on October 13, 1875 | Architect: Andreas Streit (with Johannes Schäffer) | Capacity: seating for 234 on the ground floor, 156 on the gallery; extended in 1905 by the addition of a winter prayer hall | Appearance: triple-aisled building with women's galleries, tall façade windows decorated with column capitals
See also:
* City Guide --> https://www.lit-verlag.de/isbn/978-3-643-90170-5
 
description: The data set includes
- Modelling software: ArchiCAD *.PLA - Archive
- Rendering Software: ArtLantis *.ATLA - Archive
- Panoramic representation: *.HTML & *.PNO