cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15343708
title: AMIRIS Examples

description: Examples

Example configurations built for AMIRIS 3.5.0 See the individual licence information in the respective scenarios.

How-to

Please use AMIRIS-Py to run AMIRIS-Examples. Follow these very few instructions in its README to have AMIRIS running in no time.

Sample scenarios



Simple: simple scenario running for one day with dummy data

SimpleCoupled: simple scenario coupling two markets running for one day with dummy data

Austria2019: historical simulation of Austrian day-ahead market in 2019

Germany2015: historical simulation of German day-ahead market in 2015

Germany2016: historical simulation of German day-ahead market in 2016

Germany2017: historical simulation of German day-ahead market in 2017

Germany2018: historical simulation of German day-ahead market in 2018

Germany2019: historical simulation of German day-ahead market in 2019


Citing AMIRIS Examples

If you use AMIRIS Examples in your scientific work please cite:

Kristina Nienhaus, Christoph Schimeczek, Ulrich Frey, Evelyn Sperber, Seyedfarzad Sarfarazi, Felix Nitsch, Johannes Kochems & A. Achraf El Ghazi (2023). AMIRIS Examples. Zenodo. doi: 10.5281/zenodo.7789049

Acknowledgments

We thank those who contributed to enhancing the AMIRIS examples:



v1.1 Dr. Tom Bauermann, for pointing out errors in historical fuel prices
DOI: 10.5281/zenodo.7789050
title: AMIRIS Examples

description: AMIRIS Examples


Example configurations built for AMIRIS v1.3


See the individual licence information in the respective scenarios.


How-to


Follow these steps to start using AMIRIS:



	
Clone AMIRIS.
	
Clone this project to the input folder in your AMIRIS project repository.
	
Compile the binary input file by using FAME-Io (makeFameRunConfig -f <./path/to/scenario.yaml>). Alternatively, you may download pre-compiled .pb input files here.
	
Execute AMIRIS.
	
Convert the binary results file by using FAME-Io (convertFameResults -f <./path/to/protobuf_file.pb>).



Sample scenarios



	
Simple: simple scenario running for one day with dummy data
	
Austria2019: historical simulation of Austrian day-ahead market in 2019
	
Germany2019: historical simulation of German day-ahead market in 2019



Citing AMIRIS Examples


If you use AMIRIS Examples in your scientific work please cite:


Kristina Nienhaus, Christoph Schimeczek, Ulrich Frey, Evelyn Sperber, Seyedfarzad Sarfarazi, Felix Nitsch, Johannes Kochems & A. Achraf El Ghazi (2023). AMIRIS Examples. Zenodo. doi: 10.5281/zenodo.7789050
DOI: 10.15468/dl.7cdxpe
title: Occurrence Download

description: A dataset containing 24208 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is one of (Philippines, Malaysia, Indonesia, India, Thailand, Viet Nam, Timor-Leste, Myanmar, Singapore, Brunei Darussalam, Lao People’s Democratic Republic, Cambodia)",
 "TaxonKey is Piperaceae"
 ]
}

 The dataset includes 24208 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016060-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15013540
title: ToS;DR policies dataset (clean)

description: Overview

This dataset contains two CSV files derived from Terms of Service; Didn't Read (ToS;DR) data. These files contain analyzed and categorized terms of service snippets from various online services after the cleaning process. The privacy dataset is a subset of the full dataset, focusing exclusively on privacy-related terms.

File Descriptions

1. clean_tosdr_all_data.csv



This file contains a comprehensive collection of terms of service data.

Each row represents a statement (or "point") extracted from a service's terms.

Key columns:



point_quote_text: Extracted text from the terms of service.

case_id: Unique identifier for the case.

case_title: Brief description of the case.

topic_id: Unique identifier for the topic.

topic_title: Broad category the case falls under (e.g., Transparency, Copyright License).




2. clean_tosdr_privacy_data.csv



This file is a subset of clean_tosdr_all_data.csv containing only privacy-related entries.

Includes cases related to tracking, data collection, account deletion policies, and more.

Has the same structure as clean_tosdr_all_data.csv but filtered to include only privacy-related topics.


Usage



Use clean_tosdr_all_data.csv for a broad analysis of various terms of service aspects.

Use clean_tosdr_privacy_data.csv for focused studies on privacy-related clauses.
DOI: 10.5281/zenodo.15013541
title: ToS;DR policies dataset (clean)

description: Overview

This dataset contains two CSV files derived from Terms of Service; Didn't Read (ToS;DR) data. These files contain analyzed and categorized terms of service snippets from various online services after the cleaning process. The privacy dataset is a subset of the full dataset, focusing exclusively on privacy-related terms.

File Descriptions

1. clean_tosdr_all_data.csv



This file contains a comprehensive collection of terms of service data.

Each row represents a statement (or "point") extracted from a service's terms.

Key columns:



point_quote_text: Extracted text from the terms of service.

case_id: Unique identifier for the case.

case_title: Brief description of the case.

topic_id: Unique identifier for the topic.

topic_title: Broad category the case falls under (e.g., Transparency, Copyright License).




2. clean_tosdr_privacy_data.csv



This file is a subset of clean_tosdr_all_data.csv containing only privacy-related entries.

Includes cases related to tracking, data collection, account deletion policies, and more.

Has the same structure as clean_tosdr_all_data.csv but filtered to include only privacy-related topics.


Usage



Use clean_tosdr_all_data.csv for a broad analysis of various terms of service aspects.

Use clean_tosdr_privacy_data.csv for focused studies on privacy-related clauses.
DOI: 10.15161/oar.it/fk2k5-2ea58
title: Dataset related to article \"Validation of the Italian version of the Parkinson's Disease- Cognitive Functional Rating Scale\

description: -
DOI: 10.15161/oar.it/tb82a-x7g45
title: Dataset related to article \"Validation of the Italian version of the Parkinson's Disease- Cognitive Functional Rating Scale\

description: -
DOI: 10.5281/zenodo.11640299
title: Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI

description: The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in (Yu et al., 2023) by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts (Lei et al., 2018, Lei and Siqueira, 2022), this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.  This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (JAXA’s MOLI, NASA’s GEDI, China’s TECIS) and InSAR missions (NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.
DOI: 10.6084/m9.figshare.28931252
title: Dataset for Dynamic Token Encryption: Design and Implementation

description: <pre><pre>This dataset contains experimental data for our research on Dynamic Token Encryption (DTE), a novel security enhancement mechanism for API protection. The data includes:<br><br>1. Performance measurements comparing DTE with traditional encryption methods<br>2. Security test results demonstrating resistance against common attack vectors<br>3. Implementation metrics across different environments<br><br>The dataset supports the findings presented in our paper "Dynamic Token Encryption: Design and Implementation" submitted to PeerJ Computer Science.<br><br>This data repository complements our code implementation available at: https://github.com/anycodes/DynamicTokenEncryption</pre></pre>
DOI: 10.5281/zenodo.11142224
title: Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI

description: The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in (Yu et al., 2023) by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts (Lei et al., 2018, Lei and Siqueira, 2022), this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.  This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (JAXA’s MOLI, NASA’s GEDI, China’s TECIS) and InSAR missions (NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.
DOI: 10.5281/zenodo.11257565
title: Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI

description: The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in (Yu et al., 2023) by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts (Lei et al., 2018, Lei and Siqueira, 2022), this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.  This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (JAXA’s MOLI, NASA’s GEDI, China’s TECIS) and InSAR missions (NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.
DOI: 10.5281/zenodo.15343604
title: Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI

description: The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in (Yu et al., 2023) by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts (Lei et al., 2018, Lei and Siqueira, 2022), this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.  This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (JAXA’s MOLI, NASA’s GEDI, China’s TECIS) and InSAR missions (NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.
DOI: 10.15468/dl.hxhvu4
title: Occurrence Download

description: A dataset containing 97186 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Colombia",
 "DatasetKey is one of (Tropicos MO Specimen Data, Tropicos Specimens Non-MO)",
 "Elevation 1000.0-6000.0m",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 97186 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016058-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15329084
title: Eddy Covariance Dataset from a Nebraska Research Farm, Jan-Dec 2024

description: Dataset from an eddy covariance tower situated in an irrigated field at the University of Nebraska-Lincoln Eastern Nebraska Research and Extension Center near Mead, Nebraska.  Dataset includes 1-hr interval carbon flux and energy balance data from January through December 2024, as well as methane and nitrous oxide fluxes.  Dataset will also be published on the Ameriflux website at https://ameriflux.lbl.gov/.
description: This work is funded in part through the U.S. Department of Energy ARPA-E program under Cooperative Agreement DE-AR0001226, and is led by the Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska.

Restricted Access description:

Data file is password-protected. Users can request the password via email to dwfiresearch@nebraska.edu, but must accompany the request with written (1) justification to access the data, (2) agreement to acknowledge the Daugherty Water for Food Global Institute at the University of Nebraska (DWFI) and the Department of Energy ARPA-E program in any resulting publications, and (3) confirmation that the data will be held confidentially and will not be shared or published without written consent of DWFI, until the data is published through Ameriflux.
DOI: 10.5281/zenodo.15329085
title: Eddy Covariance Dataset from a Nebraska Research Farm, Jan-Dec 2024

description: Dataset from an eddy covariance tower situated in an irrigated field at the University of Nebraska-Lincoln Eastern Nebraska Research and Extension Center near Mead, Nebraska.  Dataset includes 1-hr interval carbon flux and energy balance data from January through December 2024, as well as methane and nitrous oxide fluxes.  Dataset will also be published on the Ameriflux website at https://ameriflux.lbl.gov/.
description: This work is funded in part through the U.S. Department of Energy ARPA-E program under Cooperative Agreement DE-AR0001226, and is led by the Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska.

Restricted Access description:

Data file is password-protected. Users can request the password via email to dwfiresearch@nebraska.edu, but must accompany the request with written (1) justification to access the data, (2) agreement to acknowledge the Daugherty Water for Food Global Institute at the University of Nebraska (DWFI) and the Department of Energy ARPA-E program in any resulting publications, and (3) confirmation that the data will be held confidentially and will not be shared or published without written consent of DWFI, until the data is published through Ameriflux.
DOI: 10.6073/pasta/a0ac536a973420f2e27bc73cfa0d505e
title: Water Dawgs STEM Confidence Survey Results, 2023

description: This dataset originates from a STEM confidence survey conducted during the Water Dawgs program—a paid summer initiative hosted at the University of Georgia (UGA) in Athens, Georgia, USA. Held over 10 days in Summer 2023, the program engaged 16 high school students in a hands-on experience in freshwater science. Water Dawgs was designed to support students’ academic and professional development, with an emphasis on increasing access for individuals from populations historically excluded from STEM fields. The initiative was part of the broader impacts of two National Science Foundation-funded research projects and was shaped by three main objectives: (1) to expand access to university-led STEM opportunities by collaborating with a local public high school to recruit students from underrepresented backgrounds; (2) to highlight the connections between environmental science and students’ everyday lives and future career options, including non-STEM pathways; and (3) to foster greater self-efficacy in engaging with STEM subjects, particularly environmental science.
 
 The survey was administered at both the beginning and conclusion of the 10-day program to assess changes in participants’ confidence related to STEM. The survey measured STEM confidence using a Likert scale ranging from 1 to 5, with 1 indicating "not at all confident" and 5 indicating "totally confident."
 
 The included R code contains a script to generate a figure and summary statistics for questions related to general STEM confidence, specifically Questions 2, 6, 7, 9, and 12. One participant who only submitted a post-program survey was excluded from the dataset and analysis to ensure consistency across responses.