cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1285/i24212113v8i1p60
title: Everyday injustices of serving time in a penal welfare system in Aotearoa New Zealand

description: Contemporary socio-economic precarity and associated welfare responses feature considerable psychological and procedural challenges for persons and communities seeking assistance. Adopting a systems perspective that is informed by social practice theory, this article considers issues of injustice in the consumption of the time of low- income community members accessing welfare support in Aotearoa New Zealand. We draw examples of time injustice from two household case exemplars that reflect wider trends within a larger study of 100 households in Auckland. Each case is comprised of 15 consecutive monthly conversations, six recorded recap interviews, and various participant service, employment, food, housing, and relationship maps. Drawing on these materials we document everyday issues of time injustice related to procedural barriers to support from within the Government welfare system that propel the participants into often undignified, time exhausting, and exploitative situations beyond the system. The analysis demonstrates the time dilemmas that arise when welfare and employment organisations become out of sync with the everyday rhythms and needs of low-income community members.
DOI: 10.1594/pangaea.909946
title: Basic and other measurements of radiation at station Ny-Ålesund (2019-06)
DOI: 10.17632/dhgbssb8ns.2
title: Small and medium-sized bakery production data for scheduling

description: Production scheduling is one of the most important research topics in the discipline of industrial engineering and management. The use of a flow shop model and optimization algorithm to find an optimum schedule with the lowest production cost is common. The approach begins with the collection of production data in order to have a better understanding of the production environment.
This dataset contains information from a single bakery production line. It is slightly modified from the original production data. The hybrid no-wait flow shop scheduling concept is applicable for bakeries. There should be no delay between two successive stages. Furthermore, the products are grouped according to their internal dependencies. One product can be produced at any point during the manufacturing process of a group of products. The remainder of the products in the same group, however, are dependent on it.
The parameters in this dataset are described in detail below.

machine_id (M): The id for machines
machine_name: The name of the corresponding machine
machine_category: Machines of the same functionality are identified, so that one of them can be chosen.
employee_id (E): The id for employees
shift_start: The production time [min] when the shift of an employee starts
shift_end: The production time [min] when the shift of an employee ends
pr_group	(PG): Product group number, grouped based on internal dependency of the products
product_id (P): ID for the products within a group
bowl_time_WT (WT): Start time difference between independent product and another product of the group
product_name: Name of the product
batches: Number of batches of a group. If the value is 2 for a group, all the products in this group have to produce two times. However, 2 groups must not be produced at the same time or one after one. In other words, batches of same group do not depend on each other. Therefore, multiple batches can be produced independently. However, products withing a batch of a group must follow the bowl time (WT).
stage_name: Name of the processing stages
duration_PT (PT): Processing time [min] for the stages
machines_ employees_ME (M, E): Required machines or employees that can perform the task. Dough rest and cooling require no machines. For proofing no blockage is considered. It means proving chamber can operate as many product as possible to produce at a time. In such cases, the cells are kept empty. The term "Manual" indicates that the task has to performed by any employee.
DOI: 10.21227/875n-f104
title: DYB-PlanktonNet
DOI: 10.24432/c53w49
title: EMG Physical Action Data Set
DOI: 10.5066/p13nmkwu
title: Downscaled Climate Projections for the Edwards Aquifer Region (EAR) using CMIP5 for the years 2006 – 2010 and CMIP6 for the years 2015 - 2010

description: Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections and 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1) and six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005 for CMIP5 and 1980-2014 for CMIP6) and future projections (2006-2100 for CMIP5 and 2015-2100 for CMIP6) are provided. Applied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.
DOI: 10.5281/zenodo.10718469
title: ProstateZones - Segmentations of the prostatic zones and urethra for the PROSTATEx dataset

description: Segmentations of the prostatic zones and urethra for 200 patients from the publicly avaliable PROSTATEx image dataset [1-3]. The dataset is intended for research purposes in the field of medical imaging and to enable training and evaluation of autmatic segmentation methods.

The prostatic zones and urethra were manually segmented slice by slice on axial T2w MRIs by two experienced radiologists in collaboration with three junior colleagues. All delineations by junior colleagues has been checked, and if necessary, corrected by one of the more experienced radiologists. The two most experienced radiologists have independently delineated duplicate segmentations for 40 patients, for a total of 240 segmentations. This is intended as a test set where automatic methods can compare their performance with the inter-reader variability of the two radiologists.

To ease the use of the dataset, help with structuring the data can be found in the linked GitHub repository.

 

For more information about the dataset, see the dataset publication:

Holmlund, W., Simkó, A., Söderkvist, K. et al. ProstateZones – Segmentations of the prostatic zones and urethra for the PROSTATEx dataset. Sci Data 11, 1097 (2024). https://doi.org/10.1038/s41597-024-03945-2

 

 



PROSTATEx


[1] Geert Litjens, Oscar Debats, Jelle Barentsz, Nico Karssemeijer, and Henkjan Huisman. "ProstateX Challenge data", The Cancer Imaging Archive (2017). DOI: 10.7937/K9TCIA.2017.MURS5CL

[2] Litjens G, Debats O, Barentsz J, Karssemeijer N, Huisman H. "Computer-aided detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging 2014;33:1083-1092. DOI: 10.1109/TMI.2014.2303821

[3] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7
DOI: 10.5281/zenodo.12775323
title: An Economical Open-Source Lagrangian Drifter Design to Measure Deep Currents in Lakes

description: An economical, open-source Lagrangian drifter designed to collect current data on lakes<200km2 was evaluated against existing designs. The new design was tested in deep inland lakes in the Finger Lakes region of New York, USA and is effective at tracking deep currents. The ease and low-cost of fabrication and launch/recovery should facilitate use of this design by less-advantaged communities & researchers.

This project includes data and code for preparation of graphs and charts to illustrate Lagrangian drifter experiments in Seneca Lake and Keuka Lake, New York, USA.
DOI: 10.5281/zenodo.13782500
title: Experiment data

description: Experimental data on compression in the x- and z-directions.
DOI: 10.6073/pasta/6c8478d9aa04eeab55646ffa8e62b278
title: Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts (Instructor Materials)

description: This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. To be useful for management, ecological forecasts need to be both accurate enough for managers to be able to rely on them for decision-making and include a representation of forecast uncertainty, so managers can properly interpret the probability of future events. To improve forecast accuracy, forecasts can be updated with observational data once they become available, a process known as data assimilation. Recent improvements in environmental sensor technology and an increase in the number of sensors deployed in ecosystems have increased the availability of data for assimilation to develop and improve forecasts for natural resource management. In this module, students will explore how assimilating data with different amounts of observation uncertainty and at different temporal frequencies affects forecasts of lake water quality, using data from the U.S. National Ecological Observatory Network (NEON). The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module7/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module7) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module7_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at DOI 10.5281/zenodo.10903839 and RMarkdown version at DOI 10.5281/zenodo.10909589). Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module7.html).
DOI: 10.6073/pasta/b3953d81b3b4158e0ec5375c04774bd2
title: Macrosystems EDDIE Module 5 version 2: Introduction to Ecological Forecasting (Instructor Materials)

description: Ecological forecasting is a tool that can be used for understanding and predicting changes in populations, communities, and ecosystems. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Ecological forecasters develop and update forecasts using the iterative forecasting cycle, in which they make a hypothesis of how an ecological system works; embed their hypothesis in a model; and use the model to make a forecast of future conditions. When observations become available, they can assess the accuracy of their forecast, which indicates if their hypothesis is supported or needs to be updated before the next forecast is generated. In this Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration) module, students will apply the iterative forecasting cycle to develop an ecological forecast for a National Ecological Observation Network (NEON) site. Students will use NEON data to build an ecological model that predicts primary productivity. Using their calibrated model, they will learn about the different components of a forecast with uncertainty and compare productivity forecasts among NEON sites. The overarching goal of this module is for students to learn fundamental concepts about ecological forecasting and build a forecast for a NEON site. Students will work with an R Shiny interface to visualize data, build a model, generate a forecast with uncertainty, and then compare the forecast with observations. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This EDI data package contains instructional materials necessary to teach the module. Intructional materials (instructor manual, introductory presentation for the module, and a presentation to introduce students and instructors to R Shiny) are provided in both pdf and editable formats within a compressed file. The module R Shiny application is available at https://macrosystemseddie.shinyapps.io/module5/. Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module5.html) and GitHub repo (https://github.com/MacrosystemsEDDIE/module5) and/or Zenodo data package (Moore et al. 2024; DOI: 10.5281/zenodo.10733117) for the R Shiny application code.
DOI: 10.6084/m9.figshare.26038639
title: Data for 10.6084/m9.figshare.26038639

description: Data for 10.6084/m9.figshare.26038639
DOI: 10.6084/m9.figshare.27186939
title: Supplementary Material for: IL36RN Mutations and Correlated Characteristics in Generalized Pustular Psoriasis Patients in Can Tho City, Vietnam: A Cross-sectional study

description: Introduction: Pustular psoriasis is a rare and severe form of psoriasis characterized by sterile pustules on an erythematous background. The disease ranges from localized to generalized forms, with the latter being particularly life-threatening and recurrent. Understanding the genetic basis of pustular psoriasis, particularly IL36RN mutations, is crucial for developing better treatments. This study aims to determine the prevalence and types of IL36RN gene mutations and their relationship with clinical and paraclinical features in patients with pustular psoriasis in Can Tho City, Vietnam.

Methods: A cross-sectional study was conducted at Can Tho Dermatology Hospital involving 59 patients diagnosed with generalized pustular psoriasis (GPP) according to ERASPEN and Japanese Dermatological Association criteria. Data on demographic, clinical, and laboratory characteristics were collected. IL36RN gene mutations were identified through genomic DNA sequencing. Statistical analyses were performed to explore associations between IL36RN mutations and clinical features.

Results: The study included 59 participants, predominantly female (69.5%), with an average age of 39.12 years. A significant proportion (83.1%) had a history of psoriasis, with frequent recurrences (94.9%). The most common IL36RN mutation identified was p.Arg10ArgfsX1, present in 44.1% of patients. Other mutations included p.Pro76Leu (20.3%) and p.Arg102Trp (1.7%). Patients with IL36RN mutations were younger and had an earlier disease onset. Significant associations were found between IL36RN mutations and clinical features such as fever (OR = 11, p &lt; 0.0001) and geographic tongue (OR = 14.67, p &lt; 0.0001).

Conclusion: Our study reveals a high prevalence of IL36RN mutations, particularly p.Arg10ArgfsX1, in Vietnamese pustular psoriasis patients, strongly associating these mutations with clinical features like fever and geographic tongue.
DOI: 10.15129/601bedb4-000e-430c-92f6-693afb06cce8
title: Data for: "Sub-THz and THz Cherenkov radiation source with two-dimensional periodic surface lattice and multistage depressed collector"

description: This dataset includes numerical simulation data used in the manuscript entitled, "Sub-THz and THz Cherenkov radiation source with two-dimensional periodic surface lattice and multistage depressed collector"
DOI: 10.17909/t9-p5vg-ke50
title: Mapping Young Stars in Space and Time ("MYSST")

description: The "Measuring Young Stars in Space and Time" (MYSST) project is a large, high spatial resolution, deep Hubble Space Telescope survey of the star forming complex N44 located in the Large Magellanic Cloud (LMC). Observing objects with masses as low as 0.09 M_sun (unreddened 1 Myr pre-main-sequence star), the project aims to draw a comprehensive picture of star formation on the scales of giant molecular clouds by quantifying the star formation history of N44 across space and in time. Observations were taken with the Advanced Camera for Surveys (Wide Field Channel) and the Wide Field Camera 3 (UVIS channel) in the broad band filters F555W and F814W, covering a field of view of approximately 12.2 x 14.7 arcmin^2 or 180 x 215 pc^2 at the distance of the LMC. This archive comprises the primary science output of the survey, i.e. the MYSST photometric catalog and the mosaic images.
DOI: 10.5067/aura/mls/data2509
title: MLS/Aura Level 2 Hydrogen Chloride (HCl) Mixing Ratio V005
DOI: 10.5281/zenodo.10974079
title: Data from: Atomic-vapor magnetometer in a laser-written micro-channel
DOI: 10.57780/sm1-5ebe10b
title: SMOS L3 Sea Ice Thickness

description: SMOS L3 Sea Ice Thickness
DOI: 10.6073/pasta/1ce758925388a9273083c24ed0ee0c05
title: Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts (Instructor Materials)

description: This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Forecast uncertainty is derived from multiple sources, including model parameters and driver data, among others. Knowing the uncertainty associated with a forecast enables forecast users to evaluate the forecast and make more informed decisions. This module will guide students through an exploration of the sources of uncertainty within an ecological forecast, how uncertainty can be quantified, and steps that can be taken to reduce the uncertainty in a forecast that students develop for a lake ecosystem, using data from the National Ecological Observatory Network (NEON). Students will visualize data, build a model, generate a forecast with uncertainty, and then compare the contributions of various sources of forecast uncertainty to total forecast uncertainty. The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module6/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module6) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module6_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at https://zenodo.org/doi/10.5281/zenodo.10380759 and RMarkdown version at https://zenodo.org/doi/10.5281/zenodo.10380339). Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module6.html).
DOI: 10.5063/f1gm85j8
title: Global terrestrial Human Footprint maps for Alaska, 1993 and 2009, with SASAP regional subsetting

description: Human Footprint data was extracted for thirteen regions of Alaska for years 1993 and 2009 for the State of Alaska Salmon and People (SASAP) project. Methods and code for the regional extraction can be found in "HumanFootprint.ipynb" in this package. Human Footprint data is based on an original dataset "Global terrestrial Human Footprint maps for 1993 and 2009" acquired here https://doi.org/10.1038/sdata.2016.67, by Venter O, Sanderson EW, Magrach A, Allan JR, Beher J, Jones KR, Possingham HP, Laurance WF, Wood P, Fekete BM, Levy MA, Watson JE (2016). The Human Footprint index is based on remotely-sensed and bottom-up survey information on eight variables: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. Original data is distributed by the Dryad Digital Repository (https://doi.org/10.5061/dryad.052q5.2). To the extent possible under law, the authors have waived all copyright and related or neighboring rights to this data.