cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/jtp6dcyp78.4
title: GHG Mitigation beyond the NDC in Chile: An assessment of alternatives - Pyplan Models

description: Models of Chilean GHG emissions of the transportation, industry and mining, buildings, IPPU, forestry, agriculture, waste and electricity generation sectors, used to explore mitigation opportunities beyond Chile's NDC . Data includes projection of emisions and mitigation actions.  We considered a baseline scenario where the Chilean NDC commitments are met. The mitigation actions considered in this scenario are what the team considered to be more plausible of occurring given the current policies in Chile, regardless of their cost.
The modelling was carried out based on the combination of scenarios and futures, where these two concepts are defined as follows:
● 	Futures: They represent a trajectory of exogenous parameters that represent a possible set of conditions that could facilitate (or difficult) the mitigation strategies.
●	Mitigation Scenarios: They represent different mitigation strategies implemented at a national level. Each scenario considers a set of mitigation actions and their specific level of implementation. 
 We analyse two mitigation scenarios:
●	NDC Mitigation Scenario (NDC): Considers the achievement of all the mitigation commitments for 2030 done by Chile in its last NDC.
●	Beyond NDC Mitigation (NDC+): Considers enhanced mitigation actions to overachieve Chile's NDC commitments for 2030.
DOI: 10.17632/jtp6dcyp78.1
title: GHG Mitigation beyond the NDC in Chile: An assessment of alternatives - Pyplan Models

description: Models of Chilean GHG emissions of the transportation, industry and mining, buildings, IPPU, forestry, agriculture and waste sectors, used to explore mitigation opportunities beyond Chile's NDC . Data includes projection of emisions and mitigation actions.  We considered a baseline scenario where the Chilean NDC commitments are met. The mitigation actions considered in this scenario are what the team considered to be more plausible of occurring given the current policies in Chile, regardless of their cost.
The modelling was carried out based on the combination of scenarios and futures, where these two concepts are defined as follows:
● 	Futures: They represent a trajectory of exogenous parameters that represent a possible set of conditions that could facilitate (or difficult) the mitigation strategies.
●	Mitigation Scenarios: They represent different mitigation strategies implemented at a national level. Each scenario considers a set of mitigation actions and their specific level of implementation. 
 We analyse two mitigation scenarios:
●	NDC Mitigation Scenario (NDC): Considers the achievement of all the mitigation commitments for 2030 done by Chile in its last NDC.
●	Beyond NDC Mitigation (NDC+): Considers enhanced mitigation actions to overachieve Chile's NDC commitments for 2030.
DOI: 10.17632/jtp6dcyp78.5
title: GHG Mitigation beyond the NDC in Chile: An assessment of alternatives - Pyplan Models

description: Models of Chilean GHG emissions of the transportation, industry and mining, buildings, IPPU, forestry, agriculture, waste and electricity generation sectors, used to explore mitigation opportunities beyond Chile's NDC . Data includes projection of emisions and mitigation actions.  We considered a baseline scenario where the Chilean NDC commitments are met. The mitigation actions considered in this scenario are what the team considered to be more plausible of occurring given the current policies in Chile, regardless of their cost.
The modelling was carried out based on the combination of scenarios and futures, where these two concepts are defined as follows:
● 	Futures: They represent a trajectory of exogenous parameters that represent a possible set of conditions that could facilitate (or difficult) the mitigation strategies.
●	Mitigation Scenarios: They represent different mitigation strategies implemented at a national level. Each scenario considers a set of mitigation actions and their specific level of implementation. 
 We analyse two mitigation scenarios:
●	NDC Mitigation Scenario (NDC): Considers the achievement of all the mitigation commitments for 2030 done by Chile in its last NDC.
●	Beyond NDC Mitigation (NDC+): Considers enhanced mitigation actions to overachieve Chile's NDC commitments for 2030.
DOI: 10.17632/gfjmbmg23j.1
title: Raw Data Floater98 with Fix-MRP in Towing Tank

description: Dataset of raw data catamaran floater 9.8 meters with Fix-Moment Reference Point in Towing Tank
DOI: 10.17632/6z45pr5jwr.1
title: Incidence and Time to Development of Malignancies Arising on the Scalp of Patients with Erosive Pustular Dermatosis Based on Sex: A Retrospective Analysis

description: Supplementary Figure 1. The electronic medical record at Penn State Hershey Medical Center was queried for patients with EPD (ICD-10 L98.8) from January 2000 to December 2021. Since the ICD-10 code is not specific for EPD, each chart was reviewed by the authors to confirm the diagnosis of EPD of the scalp.

Supplementary Table 1 
Association of immunocompromised status and time to malignancy. No statistically significant differences in time to malignancy development between immunocompromised and non-immunocompromised patients. 

Supplementary Table 2 
Association of EPD treatment type and development of malignancy. No differences were noted between treatment type and development of malignancy. 

Supplementary Table 3 
EPD treatment modalities based on sex. No statistically significant differences between treatment and sex were identified.
DOI: 10.17632/98nvk3ybn9.1
title: IMPROVEMENT OF SERUM FERRITIN LEVELS WITH INOTROPIC ADMINISTRATION IN EARLY PHASE COMPARED TO REFRACTORY PHASE IN PEDIATRIC PATIENTS WITH SEPTIC SHOCK: A PRELIMINARY RANDOMIZED CONTROLLED TRIAL STUDY

description: This study is a randomized trial without blinding (before and after with control design). Study participants are pediatric septic shock patient who present to the pediatric emergency department of RS Prof Ngoerah in December 2019 – December 2020. Participants of this study will be recruited through simple random sampling method (simple computer generated random numbers). The participant must personally sign and date the latest approved version of the Informed Consent form before any study specific procedures are performed.
DOI: 10.17632/82bmr5shtv.1
title: VLFData

description: These folder contain row data of three European VLF transmitters recorded at Algiers station
DOI: 10.5281/zenodo.7669379
title: Instances of Orbit Slot Allocation Problems modeled as Directed Path Allocation Problems (DPAP)

description: <strong>Contents</strong> This repository contains instances of Orbit Slot Allocation Problems modeled as Directed Path Allocation Problems (DPAP), defined in [1]. These instances represent orbit slot allocation problems encoded as V-DPAP (Vertex-Constrained Directed Path Allocation Problem) or R-DPAP (Resource-Constrained Directed Path Allocation Problem). Each instance is provided as set of files, following the provided schema: One <code>instance_&lt;i&gt;_Graph_Request_&lt;u&gt;_&lt;r&gt;.dot</code> file per request, representing a graph, using the dot language, where <code>&lt;i&gt;</code> is the instance number, <code>&lt;u&gt;</code> is the id of the user emitting the request, and <code>&lt;r&gt;</code> is the unique id of the request/graph. Each such file lists a set of vertices and and set of weighted edges (using the <code>label</code> property of the dot language to set the weights). In the current set of instances, there are 8 requests per instance (2 requests per user, with 4 users). One <code>instance_&lt;i&gt;.inc</code> file, listing the incompabilities between vertices, with one set of incompatible vertices per row. Files are contained in subdirectories following the <code>&lt;n&gt;_&lt;m&gt;_&lt;mode&gt;_&lt;model&gt;</code> pattern, where <code>&lt;n&gt;</code> is the number of satellites per orbit plane (2, 4, 8, 16), <code>&lt;m&gt;</code> is the number of requests per user (2 only in this dataset), <code>&lt;mode&gt;</code> satisfaction mode of the request (<code>full</code> or <code>partial</code>), and <code>&lt;model&gt;</code> is the type of problem model used to encode the orbit slot allocation problems (<code>vdpap</code> or <code>rdpap</code>). We redirect to the referenced paper for more details on the DPAP, V-DPAP and R-DPAP models. <strong>Acknowledgements</strong> This work has been performed with the support of the French government in the context of the "Programme d'Invertissements d'Avenir", namely by the BPI PSPC LiChIE project (Lion Chaine Image Elargie), coordinated by Airbus Defence and Space. <strong>References</strong> [1] S. Roussel, G. Picard, C. Pralet and S. Maqrot. Conflicting Bundle Allocation with Preferences in Weighted Directed Acyclic Graphs: Application to Orbit Slot Allocation Problems, in MDPI <em>Systems</em>, Special Issue on Frontiers in Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems, 2023.
DOI: 10.5281/zenodo.7669378
title: Instances of Orbit Slot Allocation Problems modeled as Directed Path Allocation Problems (DPAP)

description: <strong>Contents</strong> This repository contains instances of Orbit Slot Allocation Problems modeled as Directed Path Allocation Problems (DPAP), defined in [1]. These instances represent orbit slot allocation problems encoded as V-DPAP (Vertex-Constrained Directed Path Allocation Problem) or R-DPAP (Resource-Constrained Directed Path Allocation Problem). Each instance is provided as set of files, following the provided schema: One <code>instance_&lt;i&gt;_Graph_Request_&lt;u&gt;_&lt;r&gt;.dot</code> file per request, representing a graph, using the dot language, where <code>&lt;i&gt;</code> is the instance number, <code>&lt;u&gt;</code> is the id of the user emitting the request, and <code>&lt;r&gt;</code> is the unique id of the request/graph. Each such file lists a set of vertices and and set of weighted edges (using the <code>label</code> property of the dot language to set the weights). In the current set of instances, there are 8 requests per instance (2 requests per user, with 4 users). One <code>instance_&lt;i&gt;.inc</code> file, listing the incompabilities between vertices, with one set of incompatible vertices per row. Files are contained in subdirectories following the <code>&lt;n&gt;_&lt;m&gt;_&lt;mode&gt;_&lt;model&gt;</code> pattern, where <code>&lt;n&gt;</code> is the number of satellites per orbit plane (2, 4, 8, 16), <code>&lt;m&gt;</code> is the number of requests per user (2 only in this dataset), <code>&lt;mode&gt;</code> satisfaction mode of the request (<code>full</code> or <code>partial</code>), and <code>&lt;model&gt;</code> is the type of problem model used to encode the orbit slot allocation problems (<code>vdpap</code> or <code>rdpap</code>). We redirect to the referenced paper for more details on the DPAP, V-DPAP and R-DPAP models. <strong>Acknowledgements</strong> This work has been performed with the support of the French government in the context of the "Programme d'Invertissements d'Avenir", namely by the BPI PSPC LiChIE project (Lion Chaine Image Elargie), coordinated by Airbus Defence and Space. <strong>References</strong> [1] S. Roussel, G. Picard, C. Pralet and S. Maqrot. Conflicting Bundle Allocation with Preferences in Weighted Directed Acyclic Graphs: Application to Orbit Slot Allocation Problems, in MDPI <em>Systems</em>, Special Issue on Frontiers in Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems, 2023.
DOI: 10.5281/zenodo.8007683
title: Increased resistance to thermal shock in pacific white shrimp fed on green algae and its effect in conjunction with probiotics

description: This study aimed to evaluate the <em>in vivo </em>effect of the macroalgae <em>Ulva ohnoi, </em>alone and combined with the probiotic <em>Lactobacillus plantarum, </em>on zootechnical, immunological, and microbiological performance, thermal resistance and survival of <em>Litopenaeus vannamei </em>challenged with <em>Vibrio parahaemolyticus. </em>The shrimp were fed four diets: control, seaweed, probiotic, and a combination of seaweed + probiotic. After six weeks, a significant difference between the control and probiotic, and the control and seaweed treatments was demonstrated in shrimp challenged with <em>Vibrio</em>, with the highest percentage of mortality being observed in the group fed only with seaweed, followed by probiotic, seaweed + probiotic, and control. The control and seaweed treatment groups also showed a significant difference in resistance to hermal shock. The animals treated with the control diet had the highest mortality rate, followed by seaweed + probiotic, probiotic, and seaweed. The zootechnical, immunological, and microbiological parameters
DOI: 10.5281/zenodo.8007684
title: Increased resistance to thermal shock in pacific white shrimp fed on green algae and its effect in conjunction with probiotics

description: This study aimed to evaluate the <em>in vivo </em>effect of the macroalgae <em>Ulva ohnoi, </em>alone and combined with the probiotic <em>Lactobacillus plantarum, </em>on zootechnical, immunological, and microbiological performance, thermal resistance and survival of <em>Litopenaeus vannamei </em>challenged with <em>Vibrio parahaemolyticus. </em>The shrimp were fed four diets: control, seaweed, probiotic, and a combination of seaweed + probiotic. After six weeks, a significant difference between the control and probiotic, and the control and seaweed treatments was demonstrated in shrimp challenged with <em>Vibrio</em>, with the highest percentage of mortality being observed in the group fed only with seaweed, followed by probiotic, seaweed + probiotic, and control. The control and seaweed treatment groups also showed a significant difference in resistance to hermal shock. The animals treated with the control diet had the highest mortality rate, followed by seaweed + probiotic, probiotic, and seaweed. The zootechnical, immunological, and microbiological parameters
DOI: 10.5281/zenodo.7664165
title: Quasi-periodic spin down variations in 17 puslars

description: Spindown time-series as computed in Keith & Nitu 2023. For each pulsar, columns are MJD, frequency-derivative value, and frequency derivative error.
DOI: 10.6084/m9.figshare.22999841
title: Data

description: Offshore carbon emission by country; CEPII-Geographical distance between the two countries; Container throughput data; Adjacency matrix of network.
DOI: 10.5519/qd.m8lfulrf
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.8009107
title: A Fundus Image Dataset for Domain Generalization in Joint Segmentation of Optic Disc and Optic Cup

description: We provide a fundus image dataset for domain generalization, which includes 5 different medical centres.<br> This dataset is based on the REFUGE[1] dataset, Drishti-GS[2] dataset, ORIGA[3] dataset, and RIGA[4] dataset. We appreciate their efforts devoted by the authors of [1-4]. Details of this dataset Domain Cases in Each Domain<br> (Training/Test) REFUGE 320/80 Drishti-GS 50/51 ORIGA 500/150 BinRushed (RIGA) 156/39 Magrabia (RIGA) 76/19 [1] Orlando J I, Fu H, Breda J B, et al. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs[J]. Medical image analysis, 2020, 59: 101570. [2] Sivaswamy J, Krishnadas S R, Joshi G D, et al. Drishti-GS: Retinal image dataset for optic nerve head (onh) segmentation[C]//2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, 2014: 53-56. [3] Zhang Z, Yin F S, Liu J, et al. Origa-light: An online retinal fundus image database for glaucoma analysis and research[C]//2010 Annual international conference of the IEEE engineering in medicine and biology. IEEE, 2010: 3065-3068. [4] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. SPIE, 2018, 10579: 55-62. If you find this dataset useful for your research, please consider citing the paper as follows: <pre><code class="language-markdown">@article{chen2023treasure, title={Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation}, author={Chen, Ziyang and Pan, Yongsheng and Ye, Yiwen and Cui, Hengfei and Xia, Yong}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023}, year={2023} }</code></pre>
DOI: 10.5281/zenodo.8009106
title: A Fundus Image Dataset for Domain Generalization in Joint Segmentation of Optic Disc and Optic Cup

description: We provide a fundus image dataset for domain generalization, which includes 5 different medical centres.<br> This dataset is based on the REFUGE[1] dataset, Drishti-GS[2] dataset, ORIGA[3] dataset, and RIGA[4] dataset. We appreciate their efforts devoted by the authors of [1-4]. Details of this dataset Domain Cases in Each Domain<br> (Training/Test) REFUGE 320/80 Drishti-GS 50/51 ORIGA 500/150 BinRushed (RIGA) 156/39 Magrabia (RIGA) 76/19 [1] Orlando J I, Fu H, Breda J B, et al. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs[J]. Medical image analysis, 2020, 59: 101570. [2] Sivaswamy J, Krishnadas S R, Joshi G D, et al. Drishti-GS: Retinal image dataset for optic nerve head (onh) segmentation[C]//2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, 2014: 53-56. [3] Zhang Z, Yin F S, Liu J, et al. Origa-light: An online retinal fundus image database for glaucoma analysis and research[C]//2010 Annual international conference of the IEEE engineering in medicine and biology. IEEE, 2010: 3065-3068. [4] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. SPIE, 2018, 10579: 55-62. If you find this dataset useful for your research, please consider citing the paper as follows: <pre><code class="language-markdown">@article{chen2023treasure, title={Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation}, author={Chen, Ziyang and Pan, Yongsheng and Ye, Yiwen and Cui, Hengfei and Xia, Yong}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023}, year={2023} }</code></pre>
DOI: 10.5519/qd.z0wucws1
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.3886/e117431v1-139301
title: Data and Code for: Improving Last-Mile Service Delivery using Phone-Based Monitoring

description: Improving "last-mile" public-service delivery is a recurring challenge in developing countries. Could the widespread adoption of mobile phones provide a scalable, cost-effective means for improvement? We use a large-scale experiment to evaluate the impact of phone-based monitoring on a program that transferred nearly a billion dollars to 5.7 million Indian farmers. In randomly-selected jurisdictions, officials were informed that program implementation would be measured via calls with beneficiaries. This led to a 7.8% reduction in the number of farmers who did not receive their transfers. The program was highly cost-effective, costing 3.6 cents for each additional dollar delivered.
DOI: 10.5519/qd.kfyh2255
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.23192624
title: Quantifying the impact of AI recommendations with explanations on prescription decisions: an interactive 3 vignette study (data & code)

description: This folder contains all the data and code to reproduce the results and figures in both the manuscript ("<strong>Quantifying the impact of AI recommendations with explanations on prescription decisions: an interactive 3 vignette study</strong>" by <strong>Myura Nagendran, Paul Festor, Matthieu Komorowski, Anthony Gordon, Aldo Faisal</strong>) and the supplementary appendices. For more details about the project structure please check the README file in the root of the folder. A data dictionary is also provided in the data folder for the four raw csv data files. The two jupyter notebooks to (1) prepare the data and (2) generate all results and figures should be run sequentially. Please see README within the zip file for more details.
DOI: 10.6084/m9.figshare.23300726
title: Digital data for "Tethered-flight performance of thermally-acclimated pest fruit flies (Diptera: Tephritidae) suggests that heat waves may promote the spread of Bactrocera species"Digital data for Tethered-flight performance of thermally-acclimated pest fruit flies (Diptera: Tephritidae) suggests that heat waves may promote the spread of Bactrocera species

description: The dataset contains 3 data sheets. One for flight performance traits, one for wing morphology and one for the comparison between two Bactrocera dorsalis populations.
DOI: 10.6084/m9.figshare.23300726.v1
title: Digital data for "Tethered-flight performance of thermally-acclimated pest fruit flies (Diptera: Tephritidae) suggests that heat waves may promote the spread of Bactrocera species"Digital data for Tethered-flight performance of thermally-acclimated pest fruit flies (Diptera: Tephritidae) suggests that heat waves may promote the spread of Bactrocera species

description: The dataset contains 3 data sheets. One for flight performance traits, one for wing morphology and one for the comparison between two Bactrocera dorsalis populations.
DOI: 10.5281/zenodo.8005770
title: Dataset and model weights for ImageGrains

description: Dataset and model weights for ImageGrains (https://github.com/dmair1989/imagegrains), a software library for segmenting and measuring of sedimentary particles in images. Some images that are used are from the dataset of https://doi.org/10.5281/zenodo.5240906.
DOI: 10.5281/zenodo.13386824
title: LoGex - synthetic tail data
DOI: 10.5281/zenodo.13386825
title: LoGex - synthetic tail data
DOI: 10.17632/swhnspgy4s.1
title: 2D Talwani Modelling - MATLAB Scripts

description: This dataset contains a MATLAB function to compute gravit anomaly of a given polygonal shape by using Talwani Method (Talwani & Ewing, 1959), along with a random data generator and an example script to explain the workflow. The three scripts within the pack are as follows:

- talwm.m : Main function to compute the gravity anomalies of a given 2D polygon. The gravity anomalies over (sx,sz) points are calculated resulting from a polygon whose coordinates are given in [px,pz] matrix and has a density contrast of ro. ( g = talwm(sx, sz, [px,pz], ro); )

- perlin.m : A function to create random data using Perlin Noise (Perlin, 2002). The function creates a matrix M for given size (m x n). The smoothness and center-orientation (i.e. maximum values in the center) can be modified. ( a = perlin([1,201], 0.8, 4); )

- talwani_modelling_example.m : a script showing the utilization of the talwm.m function. Subsurface is defined using polygonal areas with vertices. Their gravity anomalies are computed separately by using talwm.m. For testing, a random gravity anomaly is created using perlin.m function. The polygons are manually modified to match the random gravity anomalies with the computed ones. The script finally visualizes the gravity anomalies and the model in a single figure.


For real data cases, observed anomalies can be input into the script instead of using Perlin noise and subsurface units can be defined as needed. Building a semi-iterative procedure to match observed-computed anomalies is also possible.
DOI: 10.17632/2tbyykhn5g.1
title: Foot Health Status Questionnaire

description: The study collected data from 2,424 participants in Spain, using the Foot Health Status Questionnaire (FHSQ) to assess foot pain, function, general health, and footwear. The participants included 1,998 women and 426 men, categorized by age and geographic region. Key findings highlighted differences in foot health based on gender, age, and location, emphasizing the need for personalized podiatric care.