cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7749133
title: Crypto Price Monitoring Dataset for On-chain Derivatives Research

description: # Crypto Price Monitoring Repository This repository contains two CSV data files that were created to support the research titled "Price Arbitrage for DeFi Derivatives." This research is to be presented at the IEEE International Conference on Blockchain and Cryptocurrencies, taking place on 5th May 2023 in Dubai, UAE. The data files include monitoring prices for various crypto assets from several sources. The data files are structured with five columns, providing information about the symbol, unified symbol, time, price, and source of the price. ## Data Files There are two CSV data files in this repository (one for each date): 1. `Pricemon_results_2023_01_13.csv`<br> 2. `Pricemon_results_2023_01_14.csv` ## Data Format Both data files have the same format and structure, with the following five columns: 1. `symbol`: The trading symbol for the crypto asset (e.g., BTC, ETH).<br> 2. `unified_symbol`: A standardized symbol used across different platforms.<br> 3. `time`: Timestamp for when the price data was recorded (in UTC format).<br> 4. `price`: The price of the crypto asset at the given time (in USD).<br> 5. `source`: The name of the price source for the data. ## Price Sources The `source` column in the data files refers to the provider of the price data for each record. The sources include: - `chainlink`: Chainlink Price Oracle<br> - `mycellium`: Built-in oracle of the Mycellium platform<br> - `bitfinex`: Bitfinex cryptocurrency exchange<br> - `ftx`: FTX cryptocurrency exchange<br> - `binance`: Binance cryptocurrency exchange ## Usage You can use these data files for various purposes, such as analyzing price discrepancies across different sources, identifying trends, or developing trading algorithms. To use the data, simply import the CSV files into your preferred data processing or analysis tool. ### Example Here's an example of how you can read and display the data using Python and the pandas library: import pandas as pd<br> <br> # Read the data from CSV file<br> data = pd.read_csv('Pricemon_results_2023_01_13.csv')<br> <br> # Display the first 5 rows of the data<br> print(data.head())` ## Acknowledgements These datasets were recorded and supported by Datamint company (value-added on-chain data provider) and its team. <br> ## Contributing If you have any suggestions or find any issues with the data, please feel free to contact authors.
DOI: 10.5281/zenodo.7749159
title: Crypto Price Monitoring Dataset for On-chain Derivatives Research

description: # Crypto Price Monitoring Repository This repository contains two CSV data files that were created to support the research titled "Price Arbitrage for DeFi Derivatives." This research is to be presented at the IEEE International Conference on Blockchain and Cryptocurrencies, taking place on 5th May 2023 in Dubai, UAE. The data files include monitoring prices for various crypto assets from several sources. The data files are structured with five columns, providing information about the symbol, unified symbol, time, price, and source of the price. ## Data Files There are two CSV data files in this repository (one for each date): 1. `Pricemon_results_2022_11_01.csv`<br> 2. `Pricemon_results_2022_11_08.csv` ## Data Format Both data files have the same format and structure, with the following five columns: 1. `symbol`: The trading symbol for the crypto asset (e.g., BTC, ETH).<br> 2. `unified_symbol`: A standardized symbol used across different platforms.<br> 3. `time`: Timestamp for when the price data was recorded (in UTC format).<br> 4. `price`: The price of the crypto asset at the given time (in USD).<br> 5. `source`: The name of the price source for the data. ## Price Sources The `source` column in the data files refers to the provider of the price data for each record. The sources include: - `chainlink`: Chainlink Price Oracle<br> - `mycellium`: Built-in oracle of the Mycellium platform<br> - `bitfinex`: Bitfinex cryptocurrency exchange<br> - `ftx`: FTX cryptocurrency exchange<br> - `binance`: Binance cryptocurrency exchange ## Usage You can use these data files for various purposes, such as analyzing price discrepancies across different sources, identifying trends, or developing trading algorithms. To use the data, simply import the CSV files into your preferred data processing or analysis tool. ### Example Here's an example of how you can read and display the data using Python and the pandas library: import pandas as pd<br> <br> # Read the data from CSV file<br> data = pd.read_csv('Pricemon_results_2022_11_01.csv')<br> <br> # Display the first 5 rows of the data<br> print(data.head())` ## Acknowledgements These datasets were recorded and supported by Datamint company (value-added on-chain data provider) and its team. <br> ## Contributing If you have any suggestions or find any issues with the data, please feel free to contact authors.
DOI: 10.6084/m9.figshare.22138016
title: Comparison of metabolic response to colonic fermentation in lean youth vs. youth with obesity

description: The repository contians the raw data to generate the results shown in the paper: comparison of metabolic modifiers of the effect of colonic fermentation on human metabolism in youth <strong> </strong>
DOI: 10.6084/m9.figshare.22138016.v2
title: Comparison of metabolic response to colonic fermentation in lean youth vs. youth with obesity

description: The repository contians the raw data to generate the results shown in the paper: comparison of metabolic modifiers of the effect of colonic fermentation on human metabolism in youth <strong> </strong>
DOI: 10.6084/m9.figshare.22138016.v1
title: DATA FOR fermantion PAPER to be submitted.xlsx

description: The repository contians the raw data to generate the results shown in the paper: comparison of metabolic modifiers of the effect of colonic fermentation on human metabolism in youth <strong> </strong>
DOI: 10.6084/m9.figshare.22298938
title: final_db.RDS

description: Data used in Murphy et al. (2023) paper titled " Mule deer fawn recruitment dynamics in an energy disturbed landscape" used for the generalised additive modeling.
DOI: 10.5281/zenodo.7716871
title: Joule Heating calculation during St Patrick's day storm of March 2015

description: This dataset contains globally integrated, and height integrated Joule heating on St Patrick's day storm of March 2015. Calculation based on the outputs of GITM, TIEGCM and empirical formulations.
DOI: 10.5281/zenodo.7716870
title: Joule Heating calculation during St Patrick's day storm of March 2015

description: This dataset contains globally integrated, and height integrated Joule heating on St Patrick's day storm of March 2015. Calculation based on the outputs of GITM, TIEGCM and empirical formulations.
DOI: 10.5281/zenodo.7749056
title: amazonULC Data Package

description: The Amazon-ULC Data Package, available as an R package, provides Urban Land Cover (ULC) classifications for selected cities in the Brazilian Amazon. The study areas cover approximately 1,200 km², including the municipal seats of Altamira (153 km²), Cametá (44 km²), Marabá (164 km²), Santarém (143 km²), and part of the Metropolitan Area of Belém (614 km²), all located in the state of Pará.These land cover maps have significant value in urban planning for Amazonian cities, as they can aid in monitoring urban sprawl, restricting construction in environmental protection areas, assisting in urban zoning, and identifying high-density areas, among other uses. Our classification model used images from the WPM sensor of the CBERS-4A satellite, and combined the GEOBIA approach, data mining techniques, and the random machine learning algorithm.
DOI: 10.5281/zenodo.6656480
title: PALEODEM/ Supplementary materials of the manuscript "Unraveling Early Holocene occupation patterns at El Arenal de la Virgen (Alicante, Spain) open-air site: an integrated palimpsest analysis"

description: This repository hosts the R code scripts and datasets that allow reproducibility and replicability of the intra-site spatial analyses implemented in the paper: Rabuñal, J.R., Gómez-Puche, M., Polo-Díaz, A., Fernández-López de Pablo, J., 2022. Unraveling Early Holocene occupation histories at open-air sites through integrated chronological, archaeostratigraphical, lithic refitting and spatial analyses: the Arenal de la Virgen (Villena, Alicante) study case. SocArXiv. Contents: AV_Spatial_database.xlsx: main dataset for the intra-site spatial analysis. AV_Lcross_database.xlsx: dataset for the implementation of the cross-type L function. AV_2clusters.rds: dataset for the calculation of the artifact metrics. AV_MovingWindow_Results.csv: dataset with the results of the calculation of the Burnt Microdebris Index and its spatial autocorrelation analysis. AV_DBSCAN_Separation.R: R file containing the code used for the separation of the lithic spatial distribution using the DBSCAN automated density-based clustering algorithm. AV_Spatial_analysis.R: R file containing the code used for the intra-site spatial analysis. AV_MWA_Moran.R: R file containing the code for implementing the calculation and spatial autocorrelation analysis of the Burnt Microdebris Index.
description: This work was developed within the PALEODEM project, funded by the European Research Council under the European Union's Horizon 2020 research and innovation program (ref. ERC-CoG 2015-683018), granted to JFLdP. JRR is currently supported by a Margarita Salas fellowship (ref. MARSALAS21-22) funded by the European Union-Next Generation EU, the Spanish Ministry of Universities and the University of Alicante. JFLdP is additionally supported by the Plan Gen-T program (ref. CIDEGENT-18/040) from the Generalitat Valenciana. APD is currently supported by a María Zambrano fellowship funded by the the European Union-Next Generation EU and the Spanish Ministry of Universities.
DOI: 10.5281/zenodo.6656479
title: PALEODEM/ Supplementary materials of the manuscript "Unraveling Early Holocene occupation patterns at El Arenal de la Virgen (Alicante, Spain) open-air site: an integrated palimpsest analysis"

description: This repository hosts the R code scripts and datasets that allow reproducibility and replicability of the intra-site spatial analyses implemented in the paper: Rabuñal, J.R., Gómez-Puche, M., Polo-Díaz, A., Fernández-López de Pablo, J., 2022. Unraveling Early Holocene occupation histories at open-air sites through integrated chronological, archaeostratigraphical, lithic refitting and spatial analyses: the Arenal de la Virgen (Villena, Alicante) study case. SocArXiv. Contents: AV_Spatial_database.xlsx: main dataset for the intra-site spatial analysis. AV_Lcross_database.xlsx: dataset for the implementation of the cross-type L function. AV_2clusters.rds: dataset for the calculation of the artifact metrics. AV_MovingWindow_Results.csv: dataset with the results of the calculation of the Burnt Microdebris Index and its spatial autocorrelation analysis. AV_DBSCAN_Separation.R: R file containing the code used for the separation of the lithic spatial distribution using the DBSCAN automated density-based clustering algorithm. AV_Spatial_analysis.R: R file containing the code used for the intra-site spatial analysis. AV_MWA_Moran.R: R file containing the code for implementing the calculation and spatial autocorrelation analysis of the Burnt Microdebris Index.
description: This work was developed within the PALEODEM project, funded by the European Research Council under the European Union's Horizon 2020 research and innovation program (ref. ERC-CoG 2015-683018), granted to JFLdP. JRR is currently supported by a Margarita Salas fellowship (ref. MARSALAS21-22) funded by the European Union-Next Generation EU, the Spanish Ministry of Universities and the University of Alicante. JFLdP is additionally supported by the Plan Gen-T program (ref. CIDEGENT-18/040) from the Generalitat Valenciana. APD is currently supported by a María Zambrano fellowship funded by the the European Union-Next Generation EU and the Spanish Ministry of Universities.
DOI: 10.5281/zenodo.7749260
title: Low-mass stellar models (MESA v15140 inlists)

description: Low-mass stellar models (MESA v15140 inlists)
DOI: 10.5281/zenodo.7749259
title: Low-mass stellar models (MESA v15140 inlists)

description: Low-mass stellar models (MESA v15140 inlists)
DOI: 10.34740/kaggle/dsv/5190554
title: Nigerian Presidential Election 2023 Tweets
DOI: 10.3886/e182785
title: Collaborative Research: AGEP Transformation Alliance: CIRTL AGEP – Inclusive Excellence and Improved Climate for Future Faculty

description: Iowa State University, Boston University, Cornell University, Howard University, Michigan State University, Northwestern University, University of Buffalo, University of Georgia, University of Maryland College Park, and University of Texas at Arlington will collaborate to develop, build and test the impact of a model of a "networked improvement community" focused on improving dissertator experiences at a variety of institutions with the goal of reducing the effect of negative climate on interest in faculty careers. The project was submitted in response to the NSF's Alliances for Graduate Education and the Professoriate (AGEP) program solicitation (NSF 16-552). The AGEP program seeks to advance knowledge about models to improve pathways to the professoriate and success of historically underrepresented minority (URM) students, postdoctoral fellows and faculty in specific STEM disciplines and/or STEM education research fields. AGEP Transformation Alliances develop or replicate/reproduce, implement and study, via integrated educational/social science research, models to transform the dissertator phase of doctoral education, postdoctoral training and/or faculty advancement, and the transitions within and across the pathway levels, of URMs in STEM and/or STEM education research careers.<br><br>The project includes activities to address faculty and postdoc behavior and knowledge about mentoring, advising, and mitigating implicit bias and micro-aggressions as well as activities to support dissertators through workshops similar to faculty workshops, building cohorts and peer mentoring. A super-structure of the ten alliance members will work together to produce and share information and disseminate change more broadly. Evaluation will be done by a team from Michigan State University led by Dr. Marilyn Amey. Integrated research is built into the collaborative process as the community will go through four plan-do-study-act cycles to share information about interventions, plan improvements, coordinate evaluation, and spread effective interventions through first the collaboration and then disseminate them to the 46 institution CIRTL network. Research questions will address 1) student descriptions of engagement with their departments and disciplines, 2) which experiences and relationships are most influential in strengthening students' interest in faculty careers, and 3) how peers and external supports influence sense of community for dissertators. Along with testing the impact of a variety of interventions at nine institutions on the transition from dissertators to faculty and on the climate experienced by dissertators, the project will evaluate the impact of the network itself through a social network analysis, semi-structured interviews, and qualitative analysis of local and community meeting records.
DOI: 10.3886/e182785v1
title: Collaborative Research: AGEP Transformation Alliance: CIRTL AGEP – Inclusive Excellence and Improved Climate for Future Faculty

description: Iowa State University, Boston University, Cornell University, Howard University, Michigan State University, Northwestern University, University of Buffalo, University of Georgia, University of Maryland College Park, and University of Texas at Arlington will collaborate to develop, build and test the impact of a model of a "networked improvement community" focused on improving dissertator experiences at a variety of institutions with the goal of reducing the effect of negative climate on interest in faculty careers. The project was submitted in response to the NSF's Alliances for Graduate Education and the Professoriate (AGEP) program solicitation (NSF 16-552). The AGEP program seeks to advance knowledge about models to improve pathways to the professoriate and success of historically underrepresented minority (URM) students, postdoctoral fellows and faculty in specific STEM disciplines and/or STEM education research fields. AGEP Transformation Alliances develop or replicate/reproduce, implement and study, via integrated educational/social science research, models to transform the dissertator phase of doctoral education, postdoctoral training and/or faculty advancement, and the transitions within and across the pathway levels, of URMs in STEM and/or STEM education research careers.<br><br>The project includes activities to address faculty and postdoc behavior and knowledge about mentoring, advising, and mitigating implicit bias and micro-aggressions as well as activities to support dissertators through workshops similar to faculty workshops, building cohorts and peer mentoring. A super-structure of the ten alliance members will work together to produce and share information and disseminate change more broadly. Evaluation will be done by a team from Michigan State University led by Dr. Marilyn Amey. Integrated research is built into the collaborative process as the community will go through four plan-do-study-act cycles to share information about interventions, plan improvements, coordinate evaluation, and spread effective interventions through first the collaboration and then disseminate them to the 46 institution CIRTL network. Research questions will address 1) student descriptions of engagement with their departments and disciplines, 2) which experiences and relationships are most influential in strengthening students' interest in faculty careers, and 3) how peers and external supports influence sense of community for dissertators. Along with testing the impact of a variety of interventions at nine institutions on the transition from dissertators to faculty and on the climate experienced by dissertators, the project will evaluate the impact of the network itself through a social network analysis, semi-structured interviews, and qualitative analysis of local and community meeting records.
DOI: 10.3886/e182785v2
title: Collaborative Research: AGEP Transformation Alliance: CIRTL AGEP – Inclusive Excellence and Improved Climate for Future Faculty

description: Iowa State University, Boston University, Cornell University, Howard University, Michigan State University, Northwestern University, University of Buffalo, University of Georgia, University of Maryland College Park, and University of Texas at Arlington will collaborate to develop, build and test the impact of a model of a "networked improvement community" focused on improving dissertator experiences at a variety of institutions with the goal of reducing the effect of negative climate on interest in faculty careers. The project was submitted in response to the NSF's Alliances for Graduate Education and the Professoriate (AGEP) program solicitation (NSF 16-552). The AGEP program seeks to advance knowledge about models to improve pathways to the professoriate and success of historically underrepresented minority (URM) students, postdoctoral fellows and faculty in specific STEM disciplines and/or STEM education research fields. AGEP Transformation Alliances develop or replicate/reproduce, implement and study, via integrated educational/social science research, models to transform the dissertator phase of doctoral education, postdoctoral training and/or faculty advancement, and the transitions within and across the pathway levels, of URMs in STEM and/or STEM education research careers.<br><br>The project includes activities to address faculty and postdoc behavior and knowledge about mentoring, advising, and mitigating implicit bias and micro-aggressions as well as activities to support dissertators through workshops similar to faculty workshops, building cohorts and peer mentoring. A super-structure of the ten alliance members will work together to produce and share information and disseminate change more broadly. Evaluation will be done by a team from Michigan State University led by Dr. Marilyn Amey. Integrated research is built into the collaborative process as the community will go through four plan-do-study-act cycles to share information about interventions, plan improvements, coordinate evaluation, and spread effective interventions through first the collaboration and then disseminate them to the 46 institution CIRTL network. Research questions will address 1) student descriptions of engagement with their departments and disciplines, 2) which experiences and relationships are most influential in strengthening students' interest in faculty careers, and 3) how peers and external supports influence sense of community for dissertators. Along with testing the impact of a variety of interventions at nine institutions on the transition from dissertators to faculty and on the climate experienced by dissertators, the project will evaluate the impact of the network itself through a social network analysis, semi-structured interviews, and qualitative analysis of local and community meeting records.
DOI: 10.5281/zenodo.6511346
title: How mature Systematic Literature Reviews are?

description: This file contains 84 mitigation actions (practices) to improve SLR process summarized from Ampatzoglou (2019) and Zhou et al (2016). In addition, it contains the results from summarization of these practices.
DOI: 10.5281/zenodo.6511347
title: Towards a Maturity Model for Systematic Literature Review Process

description: This file contains 84 mitigation actions (practices) to improve SLR process summarized from Ampatzoglou (2019) and Zhou et al (2016). In addition, it contains the results from summarization of these practices.
DOI: 10.5281/zenodo.7749269
title: How mature Systematic Literature Reviews are?

description: This file contains 84 mitigation actions (practices) to improve SLR process summarized from Ampatzoglou (2019) and Zhou et al (2016). In addition, it contains the results from summarization of these practices.
DOI: 10.5281/zenodo.7749150
title: Virtual Energy Storage - Wind power

description: Wind power data of 20,020 wind farms and generalization to Thiessen polygons
DOI: 10.5281/zenodo.7749149
title: Virtual Energy Storage - Wind power

description: Wind power data of 20,020 wind farms and generalization to Thiessen polygons
DOI: 10.5281/zenodo.7749324
title: balearica_pavonina_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7749323
title: balearica_pavonina_spades_01.fasta.gz

description: .
DOI: 10.5517/ccdc.csd.cc2dfn0j
title: CCDC 2217647: Experimental Crystal Structure Determination

description: Related Article: Zhenhua Zhang, Wenxu Chang, Zizhen Lei, Yi Yang, Sibo Dai, Jiyao Feng, Jun Yang|2023|Org.Lett.|25|1392|doi:10.1021/acs.orglett.3c00125
description: Related Article: Wenxu Chang|2022|CSD Communication|||
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.