cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10092108
title: Dataset de Terremotos en la Península Ibérica e Islas Canarias (Últimos 15 Días)

description: El "Dataset de Terremotos en la Península Ibérica e Islas Canarias (Últimos 15 Días)" es un conjunto de datos que recopila información detallada sobre terremotos registrados en la Península Ibérica e Islas Canarias durante un período de tiempo específico que abarca los últimos 15 días desde que se ejecutó la recolección de los datos. Este conjunto de datos se enfoca en eventos sísmicos con una magnitud igual o superior a 1.5 en la escala sismológica de Richter o que han sido percibidos por la población local.
Cada entrada en el dataset proporciona datos esenciales, incluyendo la descripción del evento, la fecha y hora en la que ocurrió en Coordinación Universal (UTC), la hora local ajustada según la ubicación geográfica precisa, las coordenadas de latitud y longitud donde se originó el terremoto, la profundidad del foco, la magnitud en la escala de Richter, el tipo de magnitud utilizada, la intensidad máxima registrada y detalles sobre la localización geográfica exacta del evento.
Este conjunto de datos se actualiza periódicamente para garantizar que los datos sean relevantes y actuales, lo que lo convierte en una valiosa fuente de información para el estudio de la actividad sísmica en la región. Los datos recopilados son esenciales para comprender la sismicidad en la Península Ibérica e Islas Canarias, así como para abordar temas relacionados con la seguridad, la mitigación de riesgos y la investigación en el campo de la sismología.
DOI: 10.5281/zenodo.10092101
title: STEM VAE weights by beta

description: Weights corresponding to VAE model training for vortex domain data.
DOI: 10.5281/zenodo.10092102
title: STEM VAE weights by beta

description: Weights corresponding to VAE model training for vortex domain data.
DOI: 10.5281/zenodo.10091147
title: Nordic trial reporting project: Raw data from EU Clinical Trials Registry (EUCTR) and ClinicalTrials.gov

description: Uploaded on behalf of the author team for the research project "Systematic evaluation of clinical trial reporting at medical universities and university hospitals in the Nordic countries".
Raw data from EU Clinical Trials Registry (EUCTR) and ClinicalTrials.gov:
EUCTR: We retrieved the latest dataset for the EU Trials Tracker of EUCTR trials on Nov 27, 2022, reflecting data from Nov 7, 2022 (1,2). We also used a custom web scraper that automatically extracts data from EUCTR country protocols and results sections (variables described in Appendix Table 2), developed by the EU Trials Tracker team (2).References: 1. Goldacre B, DeVito NJ, Heneghan C, Irving F, Bacon S, Fleminger J, Curtis H. Compliance with requirement to report results on the EU Clinical Trials Register: cohort study and web resource. BMJ. 2018 Sep 12;362:k3218.2. EU Trials Tracker — Who's not sharing clinical trial results? [Internet]. [cited 2022 Aug 30]. Available from: http://eu.trialstracker.net/
ClinicalTrials.gov: We downloaded the complete Aggregate Analysis of ClinicalTrials.gov dataset (AACT, http://aact.ctti-clinicaltrials.org/) on Nov 27, 2022, reflecting data from Nov 9, 2022. 
See our GitHub and preregistered protocol for more details:https://github.com/cathrineaxfors/nordic-trial-reportinghttps://osf.io/97qkv/
DOI: 10.5281/zenodo.10091146
title: Nordic trial reporting project: Raw data from EU Clinical Trials Registry (EUCTR) and ClinicalTrials.gov

description: Uploaded on behalf of the author team for the research project "Systematic evaluation of clinical trial reporting at medical universities and university hospitals in the Nordic countries".
Raw data from EU Clinical Trials Registry (EUCTR) and ClinicalTrials.gov:
EUCTR: We retrieved the latest dataset for the EU Trials Tracker of EUCTR trials on Nov 27, 2022, reflecting data from Nov 7, 2022 (1,2). We also used a custom web scraper that automatically extracts data from EUCTR country protocols and results sections (variables described in Appendix Table 2), developed by the EU Trials Tracker team (2).References: 1. Goldacre B, DeVito NJ, Heneghan C, Irving F, Bacon S, Fleminger J, Curtis H. Compliance with requirement to report results on the EU Clinical Trials Register: cohort study and web resource. BMJ. 2018 Sep 12;362:k3218.2. EU Trials Tracker — Who's not sharing clinical trial results? [Internet]. [cited 2022 Aug 30]. Available from: http://eu.trialstracker.net/
ClinicalTrials.gov: We downloaded the complete Aggregate Analysis of ClinicalTrials.gov dataset (AACT, http://aact.ctti-clinicaltrials.org/) on Nov 27, 2022, reflecting data from Nov 9, 2022. 
See our GitHub and preregistered protocol for more details:https://github.com/cathrineaxfors/nordic-trial-reportinghttps://osf.io/97qkv/
DOI: 10.5281/zenodo.10092085
title: dataset_google_reviews_maliciosa

description: Recolección de los reviews que ha tenido el restaurante La maliciosa de madrid, desde su origen hasta el 5 de noviembre de 2023
DOI: 10.5281/zenodo.10092084
title: dataset_google_reviews_maliciosa

description: Recolección de los reviews que ha tenido el restaurante La maliciosa de madrid, desde su origen hasta el 5 de noviembre de 2023
DOI: 10.25934/pr00009414
title: Assessment of an AI Model to Predict Antibiotic Effectiveness Against Bacterial Pathogens

description: Antibiotic resistance (AMR) is an increasingly prevalent threat in today’s world of interconnectivity. More quickly than ever, bacterial infections spread and adapt. Our current methodology for synthesizing antibiotic agents is too slow to keep pace with these developments. AI/ML models have recently been a subject of interest to expedite the process of predicting effective antibiotics. Already, promising research has been conducted that proves the efficacy of these models. The goal of this project is to create a model with better data and combine this data with an interpolated set that will allow this model to predict antibiotic resistance in the future. This new model will then be compared against itself to reveal whether or not this route of interpolated data is as successful in its predictions. This experiment will help to improve AI/ML models' ability to predict AMR and, in doing so, accelerate the rate at which effective antibiotics can be made. This acceleration will help to more quickly develop treatments for patients, improving their outcomes. Antimicrobial stewardship (AMS) will be strengthened by contributing to the development of a low-cost approach to identifying effective antibiotics. This research will also inform public health practices in its prediction of future AMR. This future prediction could help shift practices from reactive to proactive. Savings from this low-cost method of AMR prediction and antibiotic detection could be redirected to the fortification of health systems.
DOI: 10.5281/zenodo.10092281
title: Research trends around the sustainability of the food industry: A Bibliometric Approach and Research Agenda
DOI: 10.5281/zenodo.10092280
title: Research trends around the sustainability of the food industry: A Bibliometric Approach and Research Agenda
DOI: 10.5281/zenodo.10086337
title: ResORR
DOI: 10.5281/zenodo.10086338
title: ResORR
DOI: 10.5281/zenodo.10092333
title: The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Software Teams: Dataset and Supplementary Materials

description: This bundle contains supplementary materials for an upcoming academic publication The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Software Teams, by Christiaan Verwijs and Daniel Russo. Included in the bundle are the dataset, SPSS syntaxes, and model definitions (AMOS). This replication package is made available by C. Verwijs under a "Creative Commons Attribution Non-Commercial Share-Alike 4.0 International"-license (CC-BY-NC-SA 4.0).
About the dataset
The dataset (SPSS) contains anonymized response data from 1.118 respondents aggregated into 161 (Agile) software teams that participated from the https://scrumteamsurvey.org. Data was gathered between September 2021, and January 2022. We cleaned the individual response data from careless responses and removed all data that could potentially identify teams, individuals, or their parent organizations. Because we wanted to analyze our measures at the team level, we calculated a team-level mean for each item in the survey. Such aggregation is only justified when at least 10% of the variance exists at the team level (Hair, 2019), which was the case (ICC = 35-45%). No data was missing on the team level.
The dataset contains question labels and answer option definitions. To conform to the privacy statement of [scrumteamsurvey.org](https://scrumteamsurvey.org), the bundle does not include response data from before the team-level aggregation.
About the model definitions
The bundle includes definitions for Structural Equation Models (SEM) for AMOS. We added the iterations of the measurement model, four models used to perform a test for common method bias, and the path model that includes interactions. Indirect effects were calculated with the "Indirect Effects" plugin for AMOS by James Gaskin.
About the SPSS syntaxes
The bundle includes the syntaxes we used to prepare the dataset from the raw import, as well as the syntax we used to generate descriptives. This is mostly there for other researchers to verify our procedure.
DOI: 10.5281/zenodo.7537784
title: The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Agile Software Teams: Dataset and Supplementary Materials

description: This bundle contains supplementary materials for an upcoming academic publication The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Agile Software Teams, by Christiaan Verwijs and Daniel Russo. Included in the bundle are the dataset, SPSS syntaxes, and model definitions (AMOS). This replication package is made available by C. Verwijs under a "Creative Commons Attribution Non-Commercial Share-Alike 4.0 International"-license (CC-BY-NC-SA 4.0). <strong>About the dataset</strong> The dataset (SPSS) contains anonymized response data from 1.118 respondents aggregated into 161 Agile software teams that participated from the https://scrumteamsurvey.org. Data was gathered between September 2021, and January 2022. We cleaned the individual response data from careless responses and removed all data that could potentially identify teams, individuals, or their parent organizations. Because we wanted to analyze our measures at the team level, we calculated a team-level mean for each item in the survey. Such aggregation is only justified when at least 10% of the variance exists at the team level (Hair, 2019), which was the case (ICC = 35-45%). No data was missing on the team level. The dataset contains question labels and answer option definitions. To conform to the privacy statement of [scrumteamsurvey.org](https://scrumteamsurvey.org), the bundle does not include response data from before the team-level aggregation. <strong>About the model definitions</strong> The bundle includes definitions for Structural Equation Models (SEM) for AMOS. We added the iterations of the measurement model, four models used to perform a test for common method bias, and the path model that includes interactions. Indirect effects were calculated with the "Indirect Effects" plugin for AMOS by James Gaskin. <strong>About the SPSS syntaxes</strong> The bundle includes the syntaxes we used to prepare the dataset from the raw import, as well as the syntax we used to generate descriptives. This is mostly there for other researchers to verify our procedure.
DOI: 10.5281/zenodo.10076384
title: The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Agile Software Teams: Dataset and Supplementary Materials

description: This bundle contains supplementary materials for an upcoming academic publication The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Agile Software Teams, by Christiaan Verwijs and Daniel Russo. Included in the bundle are the dataset, SPSS syntaxes, and model definitions (AMOS). This replication package is made available by C. Verwijs under a "Creative Commons Attribution Non-Commercial Share-Alike 4.0 International"-license (CC-BY-NC-SA 4.0).


About the dataset


The dataset (SPSS) contains anonymized response data from 1.118 respondents aggregated into 161 Agile software teams that participated from the https://scrumteamsurvey.org. Data was gathered between September 2021, and January 2022. We cleaned the individual response data from careless responses and removed all data that could potentially identify teams, individuals, or their parent organizations. Because we wanted to analyze our measures at the team level, we calculated a team-level mean for each item in the survey. Such aggregation is only justified when at least 10% of the variance exists at the team level (Hair, 2019), which was the case (ICC = 35-45%). No data was missing on the team level.


The dataset contains question labels and answer option definitions. To conform to the privacy statement of [scrumteamsurvey.org](https://scrumteamsurvey.org), the bundle does not include response data from before the team-level aggregation.


About the model definitions


The bundle includes definitions for Structural Equation Models (SEM) for AMOS. We added the iterations of the measurement model, four models used to perform a test for common method bias, and the path model that includes interactions. Indirect effects were calculated with the "Indirect Effects" plugin for AMOS by James Gaskin.


About the SPSS syntaxes


The bundle includes the syntaxes we used to prepare the dataset from the raw import, as well as the syntax we used to generate descriptives. This is mostly there for other researchers to verify our procedure.
DOI: 10.5281/zenodo.7537783
title: The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Software Teams: Dataset and Supplementary Materials

description: This bundle contains supplementary materials for an upcoming academic publication The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Software Teams, by Christiaan Verwijs and Daniel Russo. Included in the bundle are the dataset, SPSS syntaxes, and model definitions (AMOS). This replication package is made available by C. Verwijs under a "Creative Commons Attribution Non-Commercial Share-Alike 4.0 International"-license (CC-BY-NC-SA 4.0).
About the dataset
The dataset (SPSS) contains anonymized response data from 1.118 respondents aggregated into 161 (Agile) software teams that participated from the https://scrumteamsurvey.org. Data was gathered between September 2021, and January 2022. We cleaned the individual response data from careless responses and removed all data that could potentially identify teams, individuals, or their parent organizations. Because we wanted to analyze our measures at the team level, we calculated a team-level mean for each item in the survey. Such aggregation is only justified when at least 10% of the variance exists at the team level (Hair, 2019), which was the case (ICC = 35-45%). No data was missing on the team level.
The dataset contains question labels and answer option definitions. To conform to the privacy statement of [scrumteamsurvey.org](https://scrumteamsurvey.org), the bundle does not include response data from before the team-level aggregation.
About the model definitions
The bundle includes definitions for Structural Equation Models (SEM) for AMOS. We added the iterations of the measurement model, four models used to perform a test for common method bias, and the path model that includes interactions. Indirect effects were calculated with the "Indirect Effects" plugin for AMOS by James Gaskin.
About the SPSS syntaxes
The bundle includes the syntaxes we used to prepare the dataset from the raw import, as well as the syntax we used to generate descriptives. This is mostly there for other researchers to verify our procedure.
DOI: 10.6084/m9.figshare.20315394
title: Instances of "The Capacitated Family Traveling Salesperson Problem"

description: Instances of the "The Capacitated Family Traveling Salesperson Problem". Abstract: This paper addresses the Capacitated Family Traveling Salesperson Problem (CFTSP), a variant of the Family Traveling Salesperson Problem. In the CFTSP, the nodes of a graph are partitioned into disjoint families; the problem is to visit a given quantity of nodes of each family by using a set of capacitated agents to minimize the total distance traveled by the agents. No agent exceeds its capacity in a feasible solution, and the required number of nodes for each family is visited. We propose two integer linear programming formulations for the CFTSP and show that commercial solvers achieve optimal solutions for small instances. As an alternative, we propose a Biased Random-Key Genetic Algorithm with four decodification algorithms that find high-quality, optimal, or near-optimal solutions in short computational times. Experimental results obtained by exact and non-exact methods are presented and compared.
DOI: 10.6084/m9.figshare.20315394.v3
title: Instances of "The Capacitated Family Traveling Salesperson Problem"

description: Instances of the "The Capacitated Family Traveling Salesperson Problem". Abstract: This paper addresses the Capacitated Family Traveling Salesperson Problem (CFTSP), a variant of the Family Traveling Salesperson Problem. In the CFTSP, the nodes of a graph are partitioned into disjoint families; the problem is to visit a given quantity of nodes of each family by using a set of capacitated agents to minimize the total distance traveled by the agents. No agent exceeds its capacity in a feasible solution, and the required number of nodes for each family is visited. We propose two integer linear programming formulations for the CFTSP and show that commercial solvers achieve optimal solutions for small instances. As an alternative, we propose a Biased Random-Key Genetic Algorithm with four decodification algorithms that find high-quality, optimal, or near-optimal solutions in short computational times. Experimental results obtained by exact and non-exact methods are presented and compared.
DOI: 10.6084/m9.figshare.24526651
title: polygons

description: This directory contains the polygons to cut layers for the analysis.
DOI: 10.6084/m9.figshare.24526651.v1
title: polygons

description: This directory contains the polygons to cut layers for the analysis.
DOI: 10.6073/pasta/d372664bd22cbe0dd9ebf76d445b9411
title: Effects of climate and exurban development on bird species in the Southern Appalachians

description: Climate variability and land-use dynamics have important effects on many terrestrial systems, yet very few empirical studies examine both of these. We studied the impacts of building density, forest canopy cover, and mean temperature on bird species occurrence at 140 study sites in the Southern Appalachian Mountains (North Carolina, USA). Point counts conducted during both 2009 and 2011 were used to document the presence of bird species. Building density and forest canopy cover were quantified with remote sensing techniques, and mean temperature was measured with iButtons in the field.
DOI: 10.6084/m9.figshare.24538942
title: Subset reference genome (chr1)

description: Reference genome subset to chr1
DOI: 10.6084/m9.figshare.24538942.v1
title: Subset reference genome (chr1)

description: Reference genome subset to chr1
DOI: 10.5281/zenodo.7637655
title: labsyspharm/ORION-CRC

description: This is a companion release for the manuscript, "High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers." 

Nature Cancer: 10.1038/s43018-023-00576-1
bioRxiv: 10.1101/2022.09.28.509927
Visit tissue-atlas.org/atlas-datasets/lin-chen-campton-2023 to view image data (no download required!) and learn more about related work!
The dataset index and scripts for processing and analysis are deposited in the latest release. The same files can also be found on GitHub at github.com/labsyspharm/orion-crc or by following the link under "Related identifiers".
Funding
This work was supported by NCI grants U54-CA225088 and U2C-CA233262 (P.K.S. and S.S.), an NCI SBIR small business grant R41-CA224503 (RareCyte and P.K.S.) and commercial investment from RareCyte; image processing software and data science methods were developed with support from the Bill and Melinda Gates Foundation grant INV-027106 (P.K.S.), a Team Science Grant from the Gray Foundation (P.K.S. and S.S.), the David Liposarcoma Research Initiative (P.K.S. and S.S.), Emerson Collective (P.K.S.) and Ludwig Cancer Research (P.K.S. and S.S.). J.-R.L. is supported by an NCI Research Specialist Award (R50-CA274277), and S.C. by training grants T32-GM007748 from the NIGMS and T32-CA009216 from the NCI. S.S. is also supported by the BWH President's Scholars Award.
Access the full Orion CRC dataset
All images at full resolution, derived image data (e.g., segmentation masks), and single-cell tables are stored and can be accessed through Amazon Web Services (AWS) S3.
AWS S3 bucket location
s3://lin-2023-orion-crc/data
To browse and download the data use either a graphical file transfer application that supports S3 such as CyberDuck, or the AWS CLI tools. A graphical tool may be more convenient but the CLI tools will likely offer higher download speeds. For users who wish to perform processing within AWS, note that the bucket is located in the us-east-1 region so any other resources must be instantiated in this same region.
Cyberduck or Graphical File Transfer Instructions
Make sure anonymous login is selected, either through using the following server information: s3://anonymous@lin-2023-orion-crc.s3.amazonaws.com/ or make sure the anonymous login button is selected.
AWS CLI Tip
Review your IAM permissions and add additional in-line policies, as needed. For example, use: aws s3 ls --no-sign-request s3://lin-2023-crc/data/. If you continue to experience issues using the CLI, please follow the instructions for using a GUI above or refer to AWS existing documentation.
Email tissue-atlas(at)hms.harvard.edu with the subject line "Orion-CRC: Data Access" if you experience issues accessing the above S3 buckets. Please include the steps you have already tried to help us troubleshoot.
File organization
A detailed description of the file organization can be found at github.com/labsyspharm/orion-crc/blob/main/datarelease-README.md
DOI: 10.5281/zenodo.7637656
title: labsyspharm/ORION-CRC-2023

description: This is a companion release for the manuscript titled "Multi-modal digital pathology for colorectal cancer diagnosis by high-plex immunofluorescence imaging and traditional histology of the same tissue section" (biorxiv doi: 10.1101/2022.09.28.509927) The dataset index and scripts for processing and analysis are deposited in the latest release. The same files can also be found by following the link under "Related identifiers". This work was supported by NCI grants U54-CA225088 and U2C-CA233262 (PKS, SS), an NCI SBIR small business grant to RareCyte and PKS (R41-CA224503), and commercial investment from RareCyte; data processing software was developed with support from a Team Science Grant from the Gray Foundation and Ludwig Cancer Research (PKS, SS). SS is supported by the BWH President’s Scholars Award.
DOI: 10.5281/zenodo.10092320
title: Data and training checkpoints of BTO

description: Bright field images and training checkpoints of freestanding BTO in various temperatures and chemical environments
DOI: 10.5281/zenodo.10092321
title: Data and training checkpoints of BTO

description: Bright field images and training checkpoints of freestanding BTO in various temperatures and chemical environments
DOI: 10.5281/zenodo.10092383
title: Data and training checkpoints of BTO

description: Bright field images and training checkpoints of freestanding BTO in various temperatures and chemical environments
DOI: 10.5281/zenodo.8404172
title: adata

description: adata