cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10524873
title: AVID: Aalto Vocal Intensity Database

description: Data description:

AVID includes speech and EGG produced by 50 speakers (25 males, 25 females) who varied their vocal intensity in four categories (soft, normal, loud, and very loud). Recordings were conducted using a constant mouth-to-microphone distance and by recording a calibration tone. The speech data was labeled sentence-wise using a total of 19 labels that support the utilisation of the data in ML-based studies of vocal intensity based on supervised learning. Further information can be found in the 'readme.docx' file from the upload.

when collected the data:

Data is collected in 2021

Citation:

P. Alku, M. Kodali, L. Laaksonen, S.R. Kadiri, AVID: A speech database for machine learning studies on vocal intensity, Speech Communication, Vol. 157, Article 103039, 2024. https://doi.org/10.1016/j.specom.2024.103039
DOI: 10.5281/zenodo.8331897
title: AVID: Aalto Vocal Intensity Database

description: Data description:

AVID includes speech and EGG produced by 50 speakers (25 males, 25 females) who varied their vocal intensity in four categories (soft, normal, loud, and very loud). Recordings were conducted using a constant mouth-to-microphone distance and by recording a calibration tone. The speech data was labeled sentence-wise using a total of 19 labels that support the utilisation of the data in ML-based studies of vocal intensity based on supervised learning. Further information can be found in the 'readme.docx' file from the upload.

when collected the data:

Data is collected in 2021

Citation:

P. Alku, M. Kodali, L. Laaksonen, S.R. Kadiri, AVID: A speech database for machine learning studies on vocal intensity, Speech Communication, 2024 (Accepted/In press).
DOI: 10.5281/zenodo.7948299
title: AVID: Aalto Vocal Intensity Database

description: Data description:

AVID includes speech and EGG produced by 50 speakers (25 males, 25 females) who varied their vocal intensity in four categories (soft, normal, loud, and very loud). Recordings were conducted using a constant mouth-to-microphone distance and by recording a calibration tone. The speech data was labeled sentence-wise using a total of 19 labels that support the utilisation of the data in ML-based studies of vocal intensity based on supervised learning. Further information can be found in the 'readme.docx' file from the upload.

when collected the data:

Data is collected in 2021

Citation:

P. Alku, M. Kodali, L. Laaksonen, S.R. Kadiri, AVID: A speech database for machine learning studies on vocal intensity, Speech Communication, Vol. 157, Article 103039, 2024. https://doi.org/10.1016/j.specom.2024.103039
DOI: 10.5281/zenodo.7948300
title: AVID: Aalto Vocal Intensity Database

description: Data description:

AVID includes speech and EGG produced by 50 speakers (25 males, 25 females) who varied their vocal intensity in four categories (soft, normal, loud, and very loud). Recordings were conducted using a constant mouth-to-microphone distance and by recording a calibration tone. The speech data was labeled sentence-wise using a total of 19 labels that support the utilisation of the data in ML-based studies of vocal intensity based on supervised learning. Further information can be found in the 'readme.docx' file from the upload.

when collected the data:

Data is collected in 2021

Citation:

P. Alku, M. Kodali, L. Laaksonen, S.R. Kadiri, AVID: A speech database for machine learning studies on vocal intensity, Speech Communication, 2024 (Accepted/In press).
DOI: 10.5281/zenodo.8331878
title: AVID: Aalto Vocal Intensity Database

description: Data description:


AVID includes speech and EGG produced by 50 speakers (25 males, 25 females) who varied their vocal intensity in four categories (soft, normal, loud, and very loud). Recordings were conducted using a constant mouth-to-microphone distance and by recording a calibration tone. The speech data was labeled sentence-wise using a total of 19 labels that support the utilisation of the data in ML-based studies of vocal intensity based on supervised learning. Further information can be found in the 'readme.docx' file from the upload.


when collected the data:


Data is collected in 2021


Citation:


P. Alku, M. Kodali, L. Laaksonen, S.R. Kadiri, AVID: A speech database for machine learning studies on vocal intensity, Speech Communication (in review).
DOI: 10.6084/m9.figshare.24967716
title: The effect of cooperative learning strategies on students' learning motivation and alienation in project-based learning

description: Questionnaire responses for the study on;There are three questinaire results. A correlation studies and a comparative study that includes experimental pre - and post-tests
DOI: 10.6084/m9.figshare.24967716.v2
title: The effect of cooperative learning strategies on students' learning motivation and alienation in project-based learning

description: Questionnaire responses for the study on;There are three questinaire results. A correlation studies and a comparative study that includes experimental pre - and post-tests
DOI: 10.6084/m9.figshare.24967716.v1
title: The effect of cooperative learning strategies on students' learning motivation and alienation in project-based learning

description: Questionnaire responses for the study on;There are three questinaire results. A correlation studies and a comparative study that includes experimental pre - and post-tests
DOI: 10.5281/zenodo.10556325
title: Supplemental Material to Integrated Visual Software Analytics on the GitHub Platform
DOI: 10.5281/zenodo.10556326
title: Supplemental Material to Integrated Visual Software Analytics on the GitHub Platform
DOI: 10.5281/zenodo.7440489
title: Data set from Fischertechnik Smart Factory Model at University of St.Gallen

description: This is the data set of IoT data from the Fischertechnik Smart Factory Model deployed at the Institute of Computer Science at the University of St.Gallen. It is used as basis for the interactive identification of process activity executions from the IoT data. The corresponding publication can be found here:


Seiger, R., Franceschetti, M., & Weber, B. (2023). An Interactive Method for Detection of Process Activity Executions from IoT Data. Future Internet, 15(2), 77.
https://doi.org/10.3390/fi15020077


The data set contains:



	
cps_log.txt: A file of all sensor and actuator readings (in JSON format) from the smart factory during the execution of 3 instances of the storage process and 3 instances of the production process. For visualization, it can be fed line-by-line into an Influx database and Grafana can then be used to create visualizations of the data.
	
wfms_log.txt: A file containing the corresponding event log (in JSON format) recorded and extracted from the Camunda Platform workflow management system during the execution of the process instances. For visualization, it can be fed line-by-line into an Influx database and Grafana can then be used to create visualizations of the data.
	
storage_process.bpmn: Executable BPMN 2.0 model of the storage process executed in the smart factory model.
	
production_process.bpmn: Executable BPMN 2.0 model of the storage process executed in the smart factory model.



More details on the systems architecture used to execute the processes and record the data from the smart factory can be found in the follow publication:


Ronny Seiger, Lukas Malburg, Barbara Weber, Ralph Bergmann,
Integrating process management and event processing in smart factories: A systems architecture and use cases,
Journal of Manufacturing Systems, Volume 63, 2022, Pages 575-592, ISSN 0278-6125,
https://doi.org/10.1016/j.jmsy.2022.05.012
DOI: 10.5281/zenodo.7440490
title: Data set from Fischertechnik Smart Factory Model at University of St.Gallen

description: This is the data set of IoT data from the Fischertechnik Smart Factory Model deployed at the Institute of Computer Science at the University of St.Gallen. It is used as basis for the interactive identification of process activity executions from the IoT data. The corresponding publication can be found here:


Seiger, R., Franceschetti, M., & Weber, B. (2023). An Interactive Method for Detection of Process Activity Executions from IoT Data. Future Internet, 15(2), 77.
https://doi.org/10.3390/fi15020077


The data set contains:



	
cps_log.txt: A file of all sensor and actuator readings (in JSON format) from the smart factory during the execution of 3 instances of the storage process and 3 instances of the production process. For visualization, it can be fed line-by-line into an Influx database and Grafana can then be used to create visualizations of the data.
	
wfms_log.txt: A file containing the corresponding event log (in JSON format) recorded and extracted from the Camunda Platform workflow management system during the execution of the process instances. For visualization, it can be fed line-by-line into an Influx database and Grafana can then be used to create visualizations of the data.
	
storage_process.bpmn: Executable BPMN 2.0 model of the storage process executed in the smart factory model.
	
production_process.bpmn: Executable BPMN 2.0 model of the storage process executed in the smart factory model.



More details on the systems architecture used to execute the processes and record the data from the smart factory can be found in the follow publication:


Ronny Seiger, Lukas Malburg, Barbara Weber, Ralph Bergmann,
Integrating process management and event processing in smart factories: A systems architecture and use cases,
Journal of Manufacturing Systems, Volume 63, 2022, Pages 575-592, ISSN 0278-6125,
https://doi.org/10.1016/j.jmsy.2022.05.012
DOI: 10.5281/zenodo.10563471
title: High-resolution throughfall measurement design since 2019, Hainich, Germany, project AquaDiva

description: This dataset contains the sampling design for throughfall data used for the analysis published in (Demir et al., 2023). It gives spatially distributed throughfall measurement points and their forest structural properties in the field site since 2019. The measurement points are grouped into randomly distributed “kernel” points and “transect” points which are not part of the random design. 

The field site and sampling design are described in Demir et al. (2023). The throughfall data is given in an associated published dataset (Demir, Hildebrandt, 2023). The associated soil moisture measurement design is described in Metzger et al. (2017) and Demir et al. (2023). The associated soil moisture design, published (Metzger et al., 2023).
description: Data were collected by project B02 of the Collaborative Research Centre AquaDiva of the Friedrich Schiller University Jena, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1076 – Project Number 218627073
DOI: 10.5281/zenodo.10563566
title: Weekly cumulated throughfall and rain data, April-August 2019, Hainich, Germany, project AquaDiva

description: This dataset contains throughfall and gross precipitation data used for the analysis published in Demir et al., (2023). It gives spatially distributed throughfall collected at weekly intervals and covers following dates April 3 - Aug 21, 2019.

 

Missing data are marked with „na“.
description: Data were collected by project B02 of the Collaborative Research Centre AquaDiva of the Friedrich Schiller University Jena, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1076 – Project Number 218627073
DOI: 10.5281/zenodo.10523090
title: Spatial Data

description: Spatial data from existing databases about demografic, environmental and economic parameters, collected both at the city and at the living lab district level in Dortmund (Huckarde district), Ningbo (Moon Lake district), Turin (Mirafiori Sud district) and Zagreb (Sesvete district) from 2008 to 2022.
DOI: 10.5281/zenodo.10564734
title: Root water uptake, May-July 2019, Hainich, Germany, project AquaDiva

description: This dataset contains root water uptake at two different soil depth (7.5cm and 27.5cm) data used for the analysis published in Demir et al., (2023). It gives spatially distributed root water uptake data and covering the following periods:25-26 May 201901-02 June 201923 June – 03 July 201924-27 July 2019Root water uptake was estimated via MSML (Guderle and Hildebrandt, 2015) 
description: Data were collected by project B02 of the Collaborative Research Centre AquaDiva of the Friedrich Schiller University Jena, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1076 – Project Number 218627073
DOI: 10.5281/zenodo.10523091
title: Spatial Data

description: Spatial data from existing databases about demografic, environmental and economic parameters, collected both at the city and at the living lab district level in Dortmund (Huckarde district), Ningbo (Moon Lake district), Turin (Mirafiori Sud district) and Zagreb (Sesvete district) from 2008 to 2022.
DOI: 10.5281/zenodo.10563870
title: High-resolution soil water content, March-August 2019, Hainich, Germany, project AquaDiva

description: This dataset contains soil water content data used for the analysis published in Demir et al., (2023). It gives spatially distributed soil water contents and covering the following periods:March - Aug 2019The enclosed files cover two different soil depth as follows:topsoil: soil depth 7 cm below surface in „swc_top_depth 7.5 cm March_Aug_2019.csv “subsoil: soil depth 27 cm below surface in „swc_bot_depth 27.5 cm March_Aug_2019.csv “
description: Data were collected by project B02 of the Collaborative Research Centre AquaDiva of the Friedrich Schiller University Jena, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1076 – Project Number 218627073
DOI: 10.5281/zenodo.10523506
title: District level economic data

description: Self-reported economic data collected in the Living Labs district and in a control district of the Front Runner Cities of proGIreg project (Dortmund: Huckarde and Mengede districts; Turin: Mirafiori Sud and Barriera di Milano districts; Zagreb: Sesvete and Špansko-Jug districts), before and after the project NBS implementations (2019 and 2022).

More details about methodology are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124. More details about results are reported in: Baldacchini, C., Calfapietra, C. (2023): Living Labs impact at the district level, Deliverable No.4.8, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, pp 93.
DOI: 10.5281/zenodo.10532045
title: Phytoplankton Monitoring

description: The Moon Lake Phytoplankton Monitoring dataset was sampled from 2019 to 2021, with quarterly sampling. Due to the impact of the COVID-19, no sampling was conducted  in the first half of 2020. The dataset primarily focuses on the abundance of different phytoplankton genera at the genus level, with the unit of measurement being cells per liter (cells/L). Moon Lake has a total of 53 genera of phytoplankton, distributed among 7 phyla.
DOI: 10.5281/zenodo.10523507
title: District level economic data

description: Self-reported economic data collected in the Living Labs district and in a control district of the Front Runner Cities of proGIreg project (Dortmund: Huckarde and Mengede districts; Turin: Mirafiori Sud and Barriera di Milano districts; Zagreb: Sesvete and Špansko-Jug districts), before and after the project NBS implementations (2019 and 2022).

More details about methodology are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124. More details about results are reported in: Baldacchini, C., Calfapietra, C. (2023): Living Labs impact at the district level, Deliverable No.4.8, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, pp 93.
DOI: 10.5281/zenodo.10532046
title: Phytoplankton Monitoring

description: The Moon Lake Phytoplankton Monitoring dataset was sampled from 2019 to 2021, with quarterly sampling. Due to the impact of the COVID-19, no sampling was conducted  in the first half of 2020. The dataset primarily focuses on the abundance of different phytoplankton genera at the genus level, with the unit of measurement being cells per liter (cells/L). Moon Lake has a total of 53 genera of phytoplankton, distributed among 7 phyla.
DOI: 10.5281/zenodo.10532233
title: Water Quality

description: Water quality of Moon Lake in Ningbo measured every week from 2019 to 2021, before and after the implementation of a nature-based solution consisting in the planting of aquatic plants along the lake shores, within the context of the proGIReg project.

For more details, Baldacchini, C., Calfapietra, C. (2023): Living Labs impact at the NBS level, Deliverable No.4.9, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 86 pp.
DOI: 10.5281/zenodo.10532232
title: Water Quality

description: Water quality of Moon Lake in Ningbo measured every week from 2019 to 2021, before and after the implementation of a nature-based solution consisting in the planting of aquatic plants along the lake shores, within the context of the proGIReg project.

For more details, Baldacchini, C., Calfapietra, C. (2023): Living Labs impact at the NBS level, Deliverable No.4.9, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 86 pp.
DOI: 10.5281/zenodo.10566447
title: Dataset for "Isolation of Fidaxomicin and Shunt Metabolites from Actinoplanes deccanensis"

description: ZIP File:

Characterisation data (such as e.g. NMR, IR, MS spectra)

NMR raw data, .mnova files

HPLC Raw Data

PDF file:

Supporting information for

Isolation of Fidaxomicin and Shunt Metabolites from Actinoplanes deccanensis

 

Abstract of the corresponding publication:

A protocol for the isolation of the antibiotic fidaxomicin (Fdx) from Actinoplanes deccanensis and the isolation of shunt metabolites from A. deccanensis fdxG2- is reported. We constructed the mutant strain A. deccanensis fdxG2- by genetic manipulation which enabled the isolation of shunt metabolites as useful starting points for semisynthetic analogues of Fdx. Furthermore, a synthetic protocol for the conversion of complex A. deccanensis fdxG2- extracts into the single compound FdxG2-OH via methanolysis is presented. This synthetic procedure is complemented by images and practical notes. Full structure assignment is given in the SI and the characterization data files are published to aid experimentalists. The protocol is also suitable as an undergraduate laboratory project. We hope to facilitate research into new Fdx derivatives through the availability of this procedure.Natural Products, Antibiotics, Metabolic Engineering, Natural Product Isolation, Chemical Education
DOI: 10.5281/zenodo.10566448
title: Dataset for "Isolation of Fidaxomicin and Shunt Metabolites from Actinoplanes deccanensis"

description: ZIP File:

Characterisation data (such as e.g. NMR, IR, MS spectra)

NMR raw data, .mnova files

HPLC Raw Data

PDF file:

Supporting information for

Isolation of Fidaxomicin and Shunt Metabolites from Actinoplanes deccanensis

 

Abstract of the corresponding publication:

A protocol for the isolation of the antibiotic fidaxomicin (Fdx) from Actinoplanes deccanensis and the isolation of shunt metabolites from A. deccanensis fdxG2- is reported. We constructed the mutant strain A. deccanensis fdxG2- by genetic manipulation which enabled the isolation of shunt metabolites as useful starting points for semisynthetic analogues of Fdx. Furthermore, a synthetic protocol for the conversion of complex A. deccanensis fdxG2- extracts into the single compound FdxG2-OH via methanolysis is presented. This synthetic procedure is complemented by images and practical notes. Full structure assignment is given in the SI and the characterization data files are published to aid experimentalists. The protocol is also suitable as an undergraduate laboratory project. We hope to facilitate research into new Fdx derivatives through the availability of this procedure.Natural Products, Antibiotics, Metabolic Engineering, Natural Product Isolation, Chemical Education
DOI: 10.5281/zenodo.10566430
title: Supplementary data for Higgins & Sheldrake (2024), G-cubed

description: This release including all data and results to support the analysis performed in Higgins & Sheldrake (2024), published in Geochemistry, Geophysics, Geosystems.
DOI: 10.5281/zenodo.10566429
title: Supplementary data for Higgins & Sheldrake (2024), G-cubed

description: This release including all data and results to support the analysis performed in Higgins & Sheldrake (2024), published in Geochemistry, Geophysics, Geosystems.
DOI: 10.5281/zenodo.10566244
title: Color polymorphism and mating trends in a population of the alpine leaf beetle Oreina gloriosa

description: Raw data of the colour values (Euclidean distances) of Oreina gloriosa
DOI: 10.5281/zenodo.10566245
title: Color polymorphism and mating trends in a population of the alpine leaf beetle Oreina gloriosa

description: Raw data of the colour values (Euclidean distances) of Oreina gloriosa
DOI: 10.5517/ccdc.csd.cc2bz7td
title: CCDC 2174024: Experimental Crystal Structure Determination

description: Related Article: Xiangquan Hu, Meirong Han, Leicheng Wang, Li Shao, Yadav Peeyush, Jialei Du, Steven P. Kelley, Scott J. Dalgarno, David A. Atwood, Sisi Feng, Jerry L. Atwood|2023|Chemical Science|14|4532|doi:10.1039/D3SC00318C
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: 10.18465/2627d6e1-47cd-4972-ac1f-5a4a8659101d
title: MNS Saint-Gilles-Croix-de-Vie 2016

description: Modèle Numérique de Surface obtenu par LiDAR sur la côte vendéenne au niveau de Saint-Gilles-Croix-de-Vie
DOI: 10.18465/b60fe994-5a5a-43a0-9692-00c3dbf76caf
title: MNT Saint-Gilles-Croix-de-Vie 2016

description: Modèle Numérique de Terrain obtenu par LiDAR sur la côte vendéenne au niveau de Saint-Gilles-Croix-de-Vie
DOI: 10.18465/efa46926-cca8-4f71-99e0-ba25bb3a6f9f
title: MNS Olonne-sur-Mer 2016

description: Modèle Numérique de Surface obtenu par LiDAR sur la côte vendéenne au niveau d'Olonne-sur-Mer
DOI: 10.18465/a8b5ad30-47c4-49af-b1a2-f6187c138fed
title: MNT Olonne-sur-Mer 2016

description: Modèle Numérique de Terrain obtenu par LiDAR sur la côte vendéenne au niveau d'Olonne-sur-Mer
DOI: 10.18465/69a5bf45-e5de-47d9-955e-93f9dfc2a27e
title: MNS Jard-sur-Mer 2013

description: Modèle Numérique de Surface obtenu par LiDAR sur la côte vendéenne au niveau de Jard-sur-Mer