cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc1zf9g5
title: CCDC 1830037: Experimental Crystal Structure Determination

description: Related Article: Sebastian Bestgen, Christoph Schoo, B. Lilli Neumeier, Thomas J. Feuerstein, Christina Zovko, Ralf Köppe, Claus Feldmann, and Peter W. Roesky|2018|Angew.Chem.,Int.Ed.|57|14265|doi:10.1002/anie.201806943
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.cc1zfd60
title: CCDC 1830122: Experimental Crystal Structure Determination

description: Related Article: Meghan B. Ward, Andreas Scheitler, Meng Yu, Laura Senft, Annika S. Zillmann, John D. Gorden, Dean D. Schwartz, Ivana Ivanović-Burmazović, Christian R. Goldsmith|2018|Nature Chemistry|10|1207|doi:10.1038/s41557-018-0137-1
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.cc202r2y
title: CCDC 1849679: Experimental Crystal Structure Determination

description: Related Article: Ipsita Chatterjee,  Mou Chakraborty,  Pradip Ghosh,  and Sreebrata Goswami|2018|J.Indian Chem.Soc.|95|771|
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.cc20dbf6
title: CCDC 1858897: Experimental Crystal Structure Determination

description: Related Article: Debasish Samanta, Pinaki Saha, Prasanta Ghosh|2019|Inorg.Chem.|58|15060|doi:10.1021/acs.inorgchem.9b01568
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.cc1zfd71
title: CCDC 1830123: Experimental Crystal Structure Determination

description: Related Article: Meghan B. Ward, Andreas Scheitler, Meng Yu, Laura Senft, Annika S. Zillmann, John D. Gorden, Dean D. Schwartz, Ivana Ivanović-Burmazović, Christian R. Goldsmith|2018|Nature Chemistry|10|1207|doi:10.1038/s41557-018-0137-1
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.cc1zt958
title: CCDC 1841560: Experimental Crystal Structure Determination

description: Related Article: Munendra Pal Singh, Krapa Shankar, Jubaraj B. Baruah|2019|Inorg.Chim.Acta|489|204|doi:10.1016/j.ica.2019.02.029
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.cc20hzxf
title: CCDC 1862415: Experimental Crystal Structure Determination

description: Related Article: Xing Liang, Jinzhang Jiang, Xingyong Xue, Ling Huang, Xuanxuan Ding, Dongmei Nong, Hailan Chen, Lixia Pan, Zhen Ma|2019|Dalton Trans.|48|10488|doi:10.1039/C8DT04924F
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.cc20hzyg
title: CCDC 1862416: Experimental Crystal Structure Determination

description: Related Article: Xing Liang, Jinzhang Jiang, Xingyong Xue, Ling Huang, Xuanxuan Ding, Dongmei Nong, Hailan Chen, Lixia Pan, Zhen Ma|2019|Dalton Trans.|48|10488|doi:10.1039/C8DT04924F
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.cc20hzzh
title: CCDC 1862417: Experimental Crystal Structure Determination

description: Related Article: Xing Liang, Jinzhang Jiang, Xingyong Xue, Ling Huang, Xuanxuan Ding, Dongmei Nong, Hailan Chen, Lixia Pan, Zhen Ma|2019|Dalton Trans.|48|10488|doi:10.1039/C8DT04924F
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: 10.25345/c5k06xc3m
title: MassIVE MSV000095361 - Karlodinium armiger data repository
title: Crude extracts of MD-D6, MD-D7, K-0668 and a pooled quality control (QC) were measured with a Vanquish, Thermo Scientific UHPLC system coupled to a timsTOF flex, Bruker HRMS with electro-spray ionization source.
DOI: 10.5281/zenodo.12760951
title: Reproducibility data for a study of regulatory statements in EU legislation

description: Reproducibility data for a quantitative study on EU legislation

The files in this repository were generated or used in a pipeline of analysis operations on EU legislation published between 1971 and 2022. The project is called the Nature of EU Rules which seeks to analyse the "strictness" and density of EU regulations over time and by legal policy area. The data has been made available to help make the results of our study reproducible by other researchers. The underlying data used in the study has also been published in this repository.

File descriptions



complete_training_data.csv



This file is training data for binary classification of specific sentences in EU legislation as either regulatory in nature (constituting a legal obligation for some agent) or not (called a constitutive statement). The sentences have been labelled by EU law professors from Aarhus University in Denmark and Radboud University in the Netherlands

Note: The file also contains columns for identifying the specific agent being regulated (to which the legal obligation applies) in each sentence. However, this information has not been used in the study



extracted_sentences_classified_1971_2022.csv



List of sentences extracted from EU legislation documents

Classification results for individual sentences whether each is regulatory or not. There are two columns recording the classification results, one for a rule-based approach (using grammatical dependency parsing) and one for a LegalBERT classification approach.



inlegal_bert_xgboost_classifier.json



Trained binary classification model for classifying sentences as regulatory or not (based on InlegalBERT).

Note: this model is trained on the file 'complete_training_data.csv' in this Zenodo repo

Model was trained using this script and used by these scripts: one, two



metadata_enriched.csv



Metadata file from this repository but enriched with additional columns one of which is the count of regulatory sentences in each individual document

File is generated by this script

File is used by this script



classification_results_all_algorithms_test_set.csv



classification results of each sentence in the test set containing 1451 sentences (20% of training set)

according to both the fine-tuned Legal-BERT model and the dependency parsing (rule-based) algorithm

also contains the ground truth labels




Github repositories relevant to this analysis

The Python scripts in the following Github repositories were responsible for generating the data files in this Zenodo repository. The first repository listed is the core one for running the pipeline to classify and quantitatively analyse legal obligations in EU legislation. The other listed Github repositories represent components or steps of the pipeline.



http://github.com/nature-of-eu-rules/eu-legislation-strictness-analysis



http://github.com/nature-of-eu-rules/data-extraction

http://github.com/nature-of-eu-rules/data-preprocessing

http://github.com/nature-of-eu-rules/regulatory-statement-classification
DOI: 10.5281/zenodo.8200000
title: Reproducibility data for a study of regulatory statements in EU legislation

description: Reproducibility data for a quantitative study on EU legislation

The files in this repository were generated or used in a pipeline of analysis operations on EU legislation published between 1971 and 2022. The project is called the Nature of EU Rules which seeks to analyse the "strictness" and density of EU regulations over time and by legal policy area. The data has been made available to help make the results of our study reproducible by other researchers. The underlying data used in the study has also been published in this repository.

File descriptions



complete_training_data.csv



This file is training data for binary classification of specific sentences in EU legislation as either regulatory in nature (constituting a legal obligation for some agent) or not (called a constitutive statement). The sentences have been labelled by EU law professors from Aarhus University in Denmark and Radboud University in the Netherlands

Note: The file also contains columns for identifying the specific agent being regulated (to which the legal obligation applies) in each sentence. However, this information has not been used in the study



extracted_sentences_classified_1971_2022.csv



List of sentences extracted from EU legislation documents

Classification results for individual sentences whether each is regulatory or not. There are two columns recording the classification results, one for a rule-based approach (using grammatical dependency parsing) and one for a LegalBERT classification approach.



inlegal_bert_xgboost_classifier.json



Trained binary classification model for classifying sentences as regulatory or not (based on InlegalBERT).

Note: this model is trained on the file 'complete_training_data.csv' in this Zenodo repo

Model was trained using this script and used by these scripts: one, two



metadata_enriched.csv



Metadata file from this repository but enriched with additional columns one of which is the count of regulatory sentences in each individual document

File is generated by this script

File is used by this script



classification_results_all_algorithms_test_set.csv



classification results of each sentence in the test set containing 1451 sentences (20% of training set)

according to both the fine-tuned Legal-BERT model and the dependency parsing (rule-based) algorithm

also contains the ground truth labels




Github repositories relevant to this analysis

The Python scripts in the following Github repositories were responsible for generating the data files in this Zenodo repository. The first repository listed is the core one for running the pipeline to classify and quantitatively analyse legal obligations in EU legislation. The other listed Github repositories represent components or steps of the pipeline.



http://github.com/nature-of-eu-rules/eu-legislation-strictness-analysis



http://github.com/nature-of-eu-rules/data-extraction

http://github.com/nature-of-eu-rules/data-preprocessing

http://github.com/nature-of-eu-rules/regulatory-statement-classification
DOI: 10.5281/zenodo.8200001
title: Reproducibility data for a study of regulatory statements in EU legislation

description: Reproducibility data for a quantitative study on EU legislation
The files in this repository were generated or used in a pipeline of analysis operations on EU legislation published between 1971 and 2022. The project is called the Nature of EU Rules which seeks to analyse the "strictness" and density of EU regulations over time and by legal policy area. The data has been made available to help make the results of our study reproducible by other researchers. The underlying data used in the study has also been published in this repository.
File descriptions

complete_training_data.csv

This file is training data for binary classification of specific sentences in EU legislation as either regulatory in nature (constituting a legal obligation for some agent) or not (called a constitutive statement). The sentences have been labelled by EU law professors from Aarhus University in Denmark and Radboud University in the Netherlands
Note: The file also contains columns for identifying the specific agent being regulated (to which the legal obligation applies) in each sentence. However, this information has not been used in the study
extracted_sentences_classified_1971_2022.csv

List of sentences extracted from EU legislation documents
Classification results for individual sentences whether each is regulatory or not. There are two columns recording the classification results, one for a rule-based approach (using grammatical dependency parsing) and one for a LegalBERT classification approach.
inlegal_bert_xgboost_classifier.json

Trained binary classification model for classifying sentences as regulatory or not (based on InlegalBERT).
Note: this model is trained on the file 'complete_training_data.csv' in this Zenodo repo
Model was trained using this script and used by these scripts: one, two
metadata_enriched.csv

Metadata file from this repository but enriched with additional columns one of which is the count of regulatory sentences in each individual document
File is generated by this script
File is used by this script
Github repositories relevant to this analysis
The Python scripts in the following Github repositories were responsible for generating the data files in this Zenodo repository. The first repository listed is the core one for running the pipeline to classify and quantitatively analyse legal obligations in EU legislation. The other listed Github repositories represent components or steps of the pipeline.

http://github.com/nature-of-eu-rules/eu-legislation-strictness-analysis

http://github.com/nature-of-eu-rules/data-extraction
http://github.com/nature-of-eu-rules/data-preprocessing
http://github.com/nature-of-eu-rules/regulatory-statement-classification
DOI: 10.5281/zenodo.12734178
title: Evaluation of the saturator efficiency of the low frost-point generator INRIM 03 Mark 1

description: These datasets refers to the evaluation of the saturator efficiency of the low frost-point generator INRIM 03 developed at the Istituto Nazionale di Ricerca Metrologica. Alternating the inlet gas between a dry source and a moist gas source is it possible to  test the capability of the generator to saturate the carrier gas (or condensate the excess water) at the corresponding saturationtemperature.

This work has been carried out within the European Metrology Programme for Innovation and Research (EMPIR) Project ‘PROMETH2O—Metrology for trace water in ultra-pure process gases’.This project (Grant No. 20IND06 PROMETH2O) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.
DOI: 10.5281/zenodo.12734179
title: Evaluation of the saturator efficiency of the low frost-point generator INRIM 03 Mark 1

description: These datasets refers to the evaluation of the saturator efficiency of the low frost-point generator INRIM 03 developed at the Istituto Nazionale di Ricerca Metrologica. Alternating the inlet gas between a dry source and a moist gas source is it possible to  test the capability of the generator to saturate the carrier gas (or condensate the excess water) at the corresponding saturationtemperature.

This work has been carried out within the European Metrology Programme for Innovation and Research (EMPIR) Project ‘PROMETH2O—Metrology for trace water in ultra-pure process gases’.This project (Grant No. 20IND06 PROMETH2O) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.
DOI: 10.5281/zenodo.12743499
title: Optical Constants of a single AlN layer on Si

description: Spectroscopic ellipsometry was used to determine the thickness and dielectric function of a Aluminium Nitride (AlN) layer on a Si wafer. The layer was determined to be 170 nm thick. The layer was provided by AIXTRON and manufactured by means of MOVPE.

The data was created using a M2000DI spectroscopic ellipsometer from Woollam Co. Inc. Analysis was done using the CompleteEASE software. The model used is a multi-peak oscillator model for the AlN layer.

The data resembles common database values for the material AlN.
DOI: 10.5281/zenodo.12743500
title: Optical Constants of a single AlN layer on Si

description: Spectroscopic ellipsometry was used to determine the thickness and dielectric function of a Aluminium Nitride (AlN) layer on a Si wafer. The layer was determined to be 170 nm thick. The layer was provided by AIXTRON and manufactured by means of MOVPE.

The data was created using a M2000DI spectroscopic ellipsometer from Woollam Co. Inc. Analysis was done using the CompleteEASE software. The model used is a multi-peak oscillator model for the AlN layer.

The data resembles common database values for the material AlN.
DOI: 10.5281/zenodo.12760994
title: ORGANIZATION OF TAX ACCOUNTING IN ACCORDANCE WITH INTERNATIONAL FINANCIAL REPORTING STANDARDS
DOI: 10.5281/zenodo.12760995
title: ORGANIZATION OF TAX ACCOUNTING IN ACCORDANCE WITH INTERNATIONAL FINANCIAL REPORTING STANDARDS
DOI: 10.5281/zenodo.10090259
title: Dataset related to the publication E. Fasci et al., Sensors and Actuators: A. Physical 362 (2023) 114632 (https://doi.org/10.1016/j.sna.2023.114632)

description: file Allan analysis.xlsx: Allan-Werle deviation analysis of the measured ring down times. From these data a minimum detectable absorption coefficient of 6.5x10^-12 cm^-1 can be inferred, being the ring-down time under vacuum conditions about 127.5 us.
file Water mole fraction VS time.xlsx : Time development of the CRDS measured water mole fraction with a N_2 flow rate of 2 l/min. The gas pressure was set to the constant value of 2666 Pa. Each data point was retrieved from the recording of a full absorption spectrum of the H_2O 3_(2,2)→2_(2,1) transition, belonging to nu_1 + nu_3 vibrational band.
This work was done within the project PROMETH2O (EMPIR 20IND06), which received funding from the EMPIR programme cofinanced by the Participating States and from the European Union's Horizon 2020 research and innovation programme.
DOI: 10.5281/zenodo.10090258
title: Dataset related to the publication E. Fasci et al., Sensors and Actuators: A. Physical 362 (2023) 114632 (https://doi.org/10.1016/j.sna.2023.114632)

description: file Allan analysis.xlsx: Allan-Werle deviation analysis of the measured ring down times. From these data a minimum detectable absorption coefficient of 6.5x10^-12 cm^-1 can be inferred, being the ring-down time under vacuum conditions about 127.5 us.
file Water mole fraction VS time.xlsx : Time development of the CRDS measured water mole fraction with a N_2 flow rate of 2 l/min. The gas pressure was set to the constant value of 2666 Pa. Each data point was retrieved from the recording of a full absorption spectrum of the H_2O 3_(2,2)→2_(2,1) transition, belonging to nu_1 + nu_3 vibrational band.
This work was done within the project PROMETH2O (EMPIR 20IND06), which received funding from the EMPIR programme cofinanced by the Participating States and from the European Union's Horizon 2020 research and innovation programme.
DOI: 10.5281/zenodo.12761013
title: O`ZBEKISTONDA LANDSHAFT ARXITEKTURASI
DOI: 10.5281/zenodo.12761014
title: O`ZBEKISTONDA LANDSHAFT ARXITEKTURASI
DOI: 10.5281/zenodo.12549610
title: Exploring the structural dynamics of the vesicle priming machinery

description: Here are the pdb files for the simulations with only 6 central SNAREs and 6 central and 6 peripheral SNAREs with C2B of Syt1 and residue 51-75 of Cpx1 shown in figure 3.

File for figure 3a: ic_6c_snares.pdb is the initial condition for simulations with only 6 central SNAREs, and the tcl file figure_3a.tcl is to show this initial condition.

File for figure 3b: ic_6c6p_snares_c2b.pdb is the initial condition for simulations with 6 central and 6 peripheral SNAREs with a vesicle, and the tcl file figure_3b_top_view.tcl and figure_3b_side_view.tcl are to show this initial condition.

File for figure 3c: ic_6c6p_snares_c2b_nd.pdb is the initial condition for simulations with 6 central and 6 peripheral SNAREs with a nanodisc and ic_6c6p_snares_c2b_nd.xtc file is the corresponding trajectory of this simulation. The tcl file movie_for_figure_3c.tcl is to show the movie of this simulation. If you want to look at all proteins in this simulation, the tcl file movie_for_figure_3c_all.tcl can do that.

To view these snapshots by VMD, load the corresponding .tcl file or run "vmd -e name.tcl" in your terminal.
DOI: 10.5281/zenodo.12549611
title: Exploring the structural dynamics of the vesicle priming machinery

description: Here are the pdb files for the simulations with only 6 central SNAREs and 6 central and 6 peripheral SNAREs with C2B of Syt1 and residue 51-75 of Cpx1 shown in figure 3.

File for figure 3a: ic_6c_snares.pdb is the initial condition for simulations with only 6 central SNAREs, and the tcl file figure_3a.tcl is to show this initial condition.

File for figure 3b: ic_6c6p_snares_c2b.pdb is the initial condition for simulations with 6 central and 6 peripheral SNAREs with a vesicle, and the tcl file figure_3b_top_view.tcl and figure_3b_side_view.tcl are to show this initial condition.

File for figure 3c: ic_6c6p_snares_c2b_nd.pdb is the initial condition for simulations with 6 central and 6 peripheral SNAREs with a nanodisc and ic_6c6p_snares_c2b_nd.xtc file is the corresponding trajectory of this simulation. The tcl file movie_for_figure_3c.tcl is to show the movie of this simulation. If you want to look at all proteins in this simulation, the tcl file movie_for_figure_3c_all.tcl can do that.

To view these snapshots by VMD, load the corresponding .tcl file or run "vmd -e name.tcl" in your terminal.