cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10847724
title: Data from Automatic Lidar and Ceilometer (ALC) measurements at Berlin – Plänterwald (BEPLAN) from 2021-09-23 to 2022-10-04 [RAW]

description: Original data files from ALC measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.10847723
title: Data from Automatic Lidar and Ceilometer (ALC) measurements at Berlin – Plänterwald (BEPLAN) from 2021-09-23 to 2022-10-04 [RAW]

description: Original data files from ALC measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.14561176
title: Global Metal Strips Market 2024 To 2033

description: Metal Strips Market Size, Trends and Insights By Type (Stainless Steel Strips, Copper Strips, Aluminum Strips, Iron Strips, Others), By Application (Automotive, Construction, Electronics, Aerospace, Industrial, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Global Metal Strips Market was valued at USD 225.15 Billion in 2024 and is expected to reach USD 336.83 Billion by 2033, at a CAGR of 4.9% during the forecast period 2024 – 2033.

Metal strips are narrow pieces of metal typically characterized by their elongated shape and relatively thin cross-section. These strips are commonly manufactured from various metals and alloys such as steel, aluminium, copper, brass, and stainless steel, among others.

 

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=52221
DOI: 10.5281/zenodo.14561177
title: Global Metal Strips Market 2024 To 2033

description: Metal Strips Market Size, Trends and Insights By Type (Stainless Steel Strips, Copper Strips, Aluminum Strips, Iron Strips, Others), By Application (Automotive, Construction, Electronics, Aerospace, Industrial, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Global Metal Strips Market was valued at USD 225.15 Billion in 2024 and is expected to reach USD 336.83 Billion by 2033, at a CAGR of 4.9% during the forecast period 2024 – 2033.

Metal strips are narrow pieces of metal typically characterized by their elongated shape and relatively thin cross-section. These strips are commonly manufactured from various metals and alloys such as steel, aluminium, copper, brass, and stainless steel, among others.

 

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=52221
DOI: 10.5281/zenodo.10847777
title: Data from Automatic Lidar and Ceilometer (ALC) measurements at Berlin–Weissensee (BEWEIS) from 2021-09-21 to 2022-10-01 [RAW]

description: Original data files from ALC measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.10847778
title: Data from Automatic Lidar and Ceilometer (ALC) measurements at Berlin–Weissensee (BEWEIS) from 2021-09-21 to 2022-10-01 [RAW]

description: Original data files from ALC measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.14219735
title: Dataset for "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction"

description: Datasets used in "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction".

The provided zip file contains:



Datasets 1–3: For each dataset, directories include input sequences (FASTA), multiple sequence alignments (MSAs in AFA format), and normalized predicted structures from six deep learning tools. Dataset 3 also contains references - RNA chains extracted from complexes (based on the original PDB files).

Dataset 4: A text file listing the PDB IDs of RNAs included in this dataset, which is a subset of Dataset 3.

Complex Predictions: RNA chains extracted from predicted complexes, where predictions are made by AlphaFold3 and, in some cases, RoseTTAFoldNA.


The structure of the dataset and details of each folder are explained in the included README file.

 
description: Several deep learning-based tools for RNA 3D structure prediction have recently emerged, including DRfold, DeepFoldRNA, RhoFold, RoseTTAFoldNA, trRosettaRNA, and AlphaFold3. In this study, we systematically evaluate these six models on three datasets: RNA Puzzles, CASP15 RNA targets, and a newly generated large dataset of sequentially distinct RNAs, which serves as a benchmark for generalization capabilities. To ensure a robust evaluation, we also introduce a fourth, more stringent dataset that contains both sequentially and structurally distinct RNAs. We observed that each model predicts the best structure for certain RNAs, and evaluated whether commonly used scoring functions, Rosetta score and ARES, can reliably identify the most accurate structure from the predictions. Finally, since many RNA chains in the Protein Data Bank are part of complexes, we compare the performance of RoseTTAFoldNA and AlphaFold3 in predicting RNA structures within complexes versus isolated RNA chains extracted from these complexes. This comprehensive evaluation highlights the strengths and limitations of current deep learning-based tools and provides valuable insights for advancing RNA 3D structure prediction.
DOI: 10.5281/zenodo.14219734
title: Dataset for "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction"

description: Datasets used in "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction".

The provided zip file contains:



Datasets 1–3: For each dataset, directories include input sequences (FASTA), multiple sequence alignments (MSAs in AFA format), and normalized predicted structures from six deep learning tools. Dataset 3 also contains references - RNA chains extracted from complexes. These folders also include a CSV file with all metrics for all RNAs and tools.

Dataset 4: A text file listing the PDB IDs of RNAs included in this dataset, which is a subset of Dataset 3.

Dataset complexes: RNA chains extracted from predicted complexes, where predictions are made by AlphaFold 3 web server and, in some cases, RoseTTAFoldNA. There are also job files for the AlphaFold 3 web server used to obtain these predictions. Same as for previous datasets, this folder also includes a CSV file with all metrics for these RNA chains.


The structure of the dataset and details of each folder are explained in the included README file.

 
description: Several deep learning-based tools for RNA 3D structure prediction have recently emerged, including DRfold, DeepFoldRNA, RhoFold, RoseTTAFoldNA, trRosettaRNA, and AlphaFold 3. In this study, we systematically evaluate these six models on three datasets: RNA Puzzles, CASP15 RNA targets, and a newly generated dataset of sequentially distinct RNAs, which serves as a benchmark for generalization capabilities. To ensure a robust evaluation, we also introduce a fourth, more stringent dataset that contains both sequentially and structurally distinct RNAs. We observed that each model predicts the best structure for certain RNAs, and evaluated whether commonly used scoring functions, Rosetta score and ARES, can reliably identify the most accurate structure from the predictions. Finally, since many RNA chains in the Protein Data Bank are part of complexes, we compare the performance of RoseTTAFoldNA and AlphaFold 3 in predicting RNA structures within complexes versus isolated RNA chains extracted from these complexes. This comprehensive evaluation highlights the strengths and limitations of current deep learning-based tools and provides valuable insights for advancing RNA 3D structure prediction.
DOI: 10.5281/zenodo.14561158
title: Dataset for "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction"

description: Datasets used in "A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction".

The provided zip file contains:



Datasets 1–3: For each dataset, directories include input sequences (FASTA), multiple sequence alignments (MSAs in AFA format), and normalized predicted structures from six deep learning tools. Dataset 3 also contains references - RNA chains extracted from complexes. These folders also include a CSV file with all metrics for all RNAs and tools.

Dataset 4: A text file listing the PDB IDs of RNAs included in this dataset, which is a subset of Dataset 3.

Dataset complexes: RNA chains extracted from predicted complexes, where predictions are made by AlphaFold 3 web server and, in some cases, RoseTTAFoldNA. There are also job files for the AlphaFold 3 web server used to obtain these predictions. Same as for previous datasets, this folder also includes a CSV file with all metrics for these RNA chains.


The structure of the dataset and details of each folder are explained in the included README file.

 
description: Several deep learning-based tools for RNA 3D structure prediction have recently emerged, including DRfold, DeepFoldRNA, RhoFold, RoseTTAFoldNA, trRosettaRNA, and AlphaFold 3. In this study, we systematically evaluate these six models on three datasets: RNA Puzzles, CASP15 RNA targets, and a newly generated dataset of sequentially distinct RNAs, which serves as a benchmark for generalization capabilities. To ensure a robust evaluation, we also introduce a fourth, more stringent dataset that contains both sequentially and structurally distinct RNAs. We observed that each model predicts the best structure for certain RNAs, and evaluated whether commonly used scoring functions, Rosetta score and ARES, can reliably identify the most accurate structure from the predictions. Finally, since many RNA chains in the Protein Data Bank are part of complexes, we compare the performance of RoseTTAFoldNA and AlphaFold 3 in predicting RNA structures within complexes versus isolated RNA chains extracted from these complexes. This comprehensive evaluation highlights the strengths and limitations of current deep learning-based tools and provides valuable insights for advancing RNA 3D structure prediction.
DOI: 10.5281/zenodo.10848433
title: Data from Automatic Lidar and Ceilometer (ALC) measurements at Berlin – Gross Glienicke (BEGRGL) from 2022-07-12 to 2022-10-10 [RAW]

description: Original data files from ALC measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.10848434
title: Data from Automatic Lidar and Ceilometer (ALC) measurements at Berlin – Gross Glienicke (BEGRGL) from 2022-07-12 to 2022-10-10 [RAW]

description: Original data files from ALC measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.14561199
title: Global Pollution Absorbing Bricks Market 2024 To 2033

description: Pollution Absorbing Bricks Market Size, Trends and Insights By Material (Concrete, Clay, Silica Gel), By Application (Residential Buildings, Commercial Buildings, Industrial Areas, Urban Infrastructures), By End User (Residential Sector, Non-Residential Sector) and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Global Pollution Absorbing Bricks Market was valued at USD 7.9 Billion in 2024 and is expected to reach USD 17.6 Billion by 2033, at a CAGR of 5.7% during the forecast period 2024 – 2033.

Pollution-absorbing bricks, also known as smog-eating bricks or photocatalytic bricks, are innovative building materials designed to combat urban air pollution. These bricks are embedded with photocatalytic particles, typically titanium dioxide (TiO2), which undergo a chemical reaction when exposed to sunlight.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=52331
DOI: 10.5281/zenodo.14561200
title: Global Pollution Absorbing Bricks Market 2024 To 2033

description: Pollution Absorbing Bricks Market Size, Trends and Insights By Material (Concrete, Clay, Silica Gel), By Application (Residential Buildings, Commercial Buildings, Industrial Areas, Urban Infrastructures), By End User (Residential Sector, Non-Residential Sector) and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Global Pollution Absorbing Bricks Market was valued at USD 7.9 Billion in 2024 and is expected to reach USD 17.6 Billion by 2033, at a CAGR of 5.7% during the forecast period 2024 – 2033.

Pollution-absorbing bricks, also known as smog-eating bricks or photocatalytic bricks, are innovative building materials designed to combat urban air pollution. These bricks are embedded with photocatalytic particles, typically titanium dioxide (TiO2), which undergo a chemical reaction when exposed to sunlight.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=52331
DOI: 10.15468/dl.yg792y
title: Occurrence Download

description: A dataset listing the 30 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is ESP.4.5_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Narcissus L."
 ]
}

 The dataset's 30 records were derived from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049823-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/9912685
title: ShakesPeare Works
DOI: 10.6084/m9.figshare.28099469
title: Figshare.rar

description: This data is clinically relevant.
DOI: 10.6084/m9.figshare.28099469.v1
title: Figshare.rar

description: This data is clinically relevant.
DOI: 10.15468/dl.hhsd9z
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-39.98509 26.42789,-3.27969 26.42789,-3.27969 44.98451,-39.98509 44.98451,-39.98509 26.42789))",
 "TaxonKey is Myctophum nitidulum Garman, 1899"
 ]
}

 The dataset includes 60 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049825-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hcvett
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Korea, Republic of",
 "TaxonKey is Allium microdictyon Prokh."
 ]
}

 The dataset includes 24 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049826-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.