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High-resolution maps of material stock, population and employment in Austria from 1985 to 2018 - Supplementary Material

Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed. This dataset is a supplement to previously generated detailed maps of the distribution of material stocks, population and employment across Austria from 1985 to 2018 (10.5281/zenodo.7195101). The data are aggregated tabular data used to create illustrations in an accompanying data article. Data format and units This dataset features: <ul> <li>Tabular aggregated data of material stocks, population and employment on a municipality level from 1985 to 2018 (in administrative borders of 2018. <ul> <li>Note: Only layers used to create illustrations in the accompanying data article are presented as tabular data!</li> </ul> </li> <li>Municipalities of Austria as as shape file (from https://www.data.gv.at/katalog/dataset/stat_gliederung-osterreichs-in-gemeinden14f53#resources)</li> <li>Annual population and employment numbers on a federal states level, extracted and aggregated from Statistik Austria (see readme.txt)</li> </ul> Further information For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website </a>to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Funding This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

opencc-by-4.0Nov 2022View details →

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Neural Networks for Structure-Informed Prediction of Formation Energy (employed in SIPFENN)

pySIPFENN Documentation: <a href="https://pysipfenn.org">pysipfenn.org</a> pySIPFENN GitHub: <a href="https://github.com/PhasesResearchLab/pySIPFENN">git.pysipfenn.org</a> Original SIPFENN Paper: <a href="https://doi.org/10.1016/j.commatsci.2022.111254">10.1016/j.commatsci.2022.111254</a>   Network Changelog: V 0.10 - All models moved to the open ONNX format for improved interchangeability; NN30 neural network similar to NN20 but accepting the new KS2022 feature vector; Python code migrated to public GitHub repository. V 0.9 - Python code updated to the release version; paper published V 0.8 - Python code (beta) to run models included V 0.7 - Original upload of development models    Selected works with SIPFENN alongside DFT and experiments: - <a href="https://doi.org/10.1016/j.actamat.2021.117448">10.1016/j.actamat.2021.117448</a> - <a href="https://doi.org/10.1038/s41598-021-03578-0">10.1038/s41598-021-03578-0</a>   SIPFENN Abstract (original publication, 2021): In recent years, numerous studies have employed machine learning (ML) techniques to enable orders of magnitude faster high-throughput materials discovery by augmentation of existing methods or as standalone tools. In this paper, we introduce a new neural network-based tool for the prediction of formation energies based on elemental and structural features of Voronoi-tessellated materials. We provide a self-contained overview of the ML techniques used. Of particular importance is the connection between the ML and the true material-property relationship, how to improve the generalization accuracy by reducing overfitting, and how new data can be incorporated into the model to tune it to a specific material system.          In the course of this work, over 30 novel neural network architectures were designed and tested. This lead to three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve mean average error (MAE) of 28, 40, and 42 meV/atom respectively. The second model provides better predictions on materials far from ones reported in OQMD, while the third reduces the computational cost by a factor of 8.          We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models but also ships in a ready-to-use form with pre-trained neural networks and a user interface.    Contacts: - Adam Krajewski: ak@psu.edu - Prof. Zi-Kui Liu: zxl15@psu.edu

opencc-by-4.0Aug 2020View details →

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Employment Statistic Illinois -Animus Prime Research

Data charting and excel sheet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Employment Statistics Data Chart

opencc-by-4.0Feb 2023View details →

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Distributional employment challenges and opportunities of decarbonizing the US power system

This dataset presents the results in the manuscript entitled "Distributional employment challenges and opportunities of decarbonizing the US power system". The Low Carbon Transition Employment Distribution (LoCaTED) model used to generate the results can be found on GitHub (<a href="https://github.com/judyjwxie/LoCaTED">https://github.com/judyjwxie/LoCaTED</a>). The suite of data files is based on the ReEDS 2022 Standard Scenarios and our employment calculation variations. Each file shows the job creation in the number of jobs disaggregated into the year, US state, technology, and economic sector (defined by the JEDI model).

opencc-by-4.0Apr 2023View details →

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Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679). The training code is available at https://github.com/mario-linov/graphs4cfd.

opencc-by-4.0May 2023View details →

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Data from: Employing hypothesis testing and data from multiple genomic compartments to resolve recalcitrant backbone nodes in Goodenia s.l. (Goodeniaceae)

Goodeniaceae is a primarily Australian flowering plant family with a complex taxonomy and evolutionary history. Previous phylogenetic analyses have successfully resolved the backbone topology of the largest clade in the family, Goodenia s.l., but have failed to clarify relationships within the species-rich and enigmatic Goodenia clade C, a prerequisite for taxonomic revision of the group. We used genome skimming to retrieve sequences for chloroplast, mitochondrial, and nuclear markers for 24 taxa representing Goodenia s.l., with a particular focus on Goodenia clade C. We performed extensive hypothesis tests to explore incongruence in clade C and evaluate statistical support for clades within this group, using datasets from all three genomic compartments. The mitochondrial dataset is comparable to the chloroplast dataset in providing resolution within Goodenia clade C, though backbone support values within this clade remain low. The hypothesis tests provided an additional, complementary means of evaluating support for clades. We propose that the major subclades of Goodenia clade C (C1–C3 + Verreauxia) are the result of a rapid radiation, and each represents a distinct lineage.

opencc-zeroDec 2017View details →

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Employment status and sick-leave following obesity surgery: a five-year prospective cohort study

This is a dataset (SPSS, sav. file) related to the study "Employment status and sick-leave following obesity surgery: a five-year prospective cohort study". If anyone wants the file in a different format, contact the uploader (see the link on the right side).

opencc-by-sa-4.0Mar 2015View details →

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pLMMoRF: A web server that accurately predicts membrane-interacting molecular recognition features by employing a protein language model

pLMMMoRF predictor scrips and MemMoRF prediction of the human proteome.

opencc-by-4.0Nov 2024View details →

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Companion data for Asynchronous multi-phase task-based applications: Employing different nodes to design better distributions

This is the companion data for the paper "Asynchronous multi-phase task-based applications: Employing different nodes to design better distributions", by Lucas Leandro Nesi, Arnaud Legrand, and Lucas Mello Schnorr, published at Future Generation Computer Systems, check the main companion at: https://gitlab.com/lnesi/companion-fgcs .

opencc-by-4.0Sep 2023View details →

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Job offers from Lanbide Basque Country Public Employment Portal

Based on the provided sample from the "Job_offers_from_Lanbide_Basque_Country_Public_Employment_Portal" dataset, here is a summarized description in English: The dataset compiles various job listings, including local offers from Lanbide, wider European offers, public sector job listings, and offers from other job portals managed within the Lanbide web platform. Each record in the dataset appears to represent a unique job offer with a range of attributes detailing the position. Common attributes for each job listing include: - Extraction_timestamp: The date and time when the job offer was last updated, reflecting the currency of the data. - Source: A numerical or categorical identifier that may correspond to the origin of the job offer, such as Lanbide, other European job portals, public offers, etc. - Title: The name or title of the job position. - AddressLocality: The local town or city where the job is located. - AddressRegion: The broader region, province, or administrative area where the job is located. - Description: A detailed description of the job, including responsibilities, requirements, and other relevant information such as contract type, shifts, start date, salary, and any specific qualifications like a disability percentage or certifications. - DatePosted: The date the job was originally posted. - Offer_Number: A unique identifier for the job offer. - EmploymentType: The type of employment contract offered (e.g., temporary labor). - Collective: Any specific community or group the job is targeting, such as people with disabilities. - OccupationalCategory: The category or sector of employment, potentially aligning with standard occupational classification systems. - Workday: Details regarding the work schedule. - NumberPositions: The number of positions available. - Participants: Possibly the number of current applicants or participants - EmploymentSituation: This field could detail the employment status required. - Age: Any age requirements. - EducationRequirements: Educational or training requirements necessary for applicants. - DriverLicense: Information on whether a driver's license is required. - ExperienceRequirements: The amount of experience required. - ContactInformation: Details for contacting the employer or recruiter. Each job listing encapsulates comprehensive information that would aid job seekers in understanding the requirements and expectations of each role and for employers or recruiters in managing the recruitment process.

openodc-odblApr 2024View details →

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Data of Psychological Capital, Parent-Student Career Congruences, Employability

The data brief of our research (psychological capital, parent-student congruences and employability)

opencc-by-4.0Jan 2022View details →

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Framework for the development of employability competencies at Campus FP

Framework for the development of employability competencies at Campus FP

opencc-by-4.0May 2022View details →

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Data from: Vibrational Transportation on a Platform Subjected to Sinusoidal Displacement Cycles Employing Dry Friction Control

Data from the paper "Vibrational Transportation on a Platform Subjected to Sinusoidal Displacement Cycles Employing Dry Friction Control", <a href="https://doi.org/10.3390/s21217280">https://doi.org/10.3390/s21217280</a> Currently used vibrational transportation methods are usually based on asymmetries of geometric, kinematic, wave, or time types. This paper investigates the vibrational transportation of objects on a platform that is subjected to sinusoidal displacement cycles, employing periodic dynamic dry friction control. This manner of dry friction control creates an asymmetry, which is necessary to move the object. The theoretical investigation on functional capabilities and transportation regimes was carried out using a developed parametric mathematical model, and the control parameters that determine the transportation characteristics such as velocity and direction were defined. To test the functional capabilities of the proposed method, an experimental setup was developed, and experiments were carried out. The results of the presented research indicate that the proposed method ensures smooth control of the transportation velocity in a wide range and allows it to change the direction of motion. Moreover, the proposed method offers other new functional capabilities, such as a capability to move individual objects on the same platform in opposite directions and at different velocities at the same time by imposing different friction control parameters on different regions of the platform or on different objects. In addition, objects can be subjected to translation and rotation at the same time by imposing different friction control parameters on different regions of the platform. The presented research extends the classical theory of vibrational transportation and has a practical value for industries that operate manufacturing systems performing tasks such as handling and transportation, positioning, feeding, sorting, aligning, or assembling.

opencc-by-4.0Oct 2021View details →

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Do Go Chasing Waterfalls: Enoyl Reductase (FabI) in Complex with Inhibitors Stabilizes the Tetrameric Structure and Opens Water Channels - trajectories employed in Markov State Models and water analyses

The following trajectories were employed in the generation of MSM models and water analyses: SaFabI_60us_align.zip EcFabI_60us_align.zip waters_SaFabI.tar.gz waters_EcFabI.tar.gz

opencc-by-4.0Aug 2022View details →

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Survey among self-employed persons in Germany during the COVID-19 pandemic. wave 2020.

In spring 2020, DIW Berlin, ZEW Mannheim, and the University of Trier conducted an online survey among self-employed persons to collect data on the situation of the self-employed in Germany at the onset of the COVID-19 pandemic. The online survey was disseminated in cooperation with the Verband der Gründer und Selbstständigen Deutschland (VGSD) e.V. and other professional associations. The questionnaire included 51 questions and focused on the following topics: - the self-employed's affectedness by the pandemic and by government containment measures - financial situation during the pandemic - application to government support programs - business strategies to overcome the crisis - assessments of the future - sociodemographic characteristics - business-related characteristics In total, 27,262 interviews were collected.

opencc-by-4.0Sep 2022View details →

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Employment & Labour Relations Court Kenya

This data is scrapped from the Kenya law website (<a href="http://www.kenyalaw.org/">http://www.kenyalaw.org/</a>) which reports on the development of Kenya’s jurisprudence via Kenya Law Reports. The training dataset contained 3,087 entries with details of cases from the ELRC. We use this dataset in out study to assessed both the set of binary variables (case class, case outcome), as well as the multi-label variables (case action, nature of case, county) of this dataset.

opencc-by-4.0Sep 2022View details →

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Assessing the Impact of Supply-Side Policies on Oil Extraction, Health, and Employment in California

California's ambitious goal to slash GHG emissions by 90% by 2045 marks a significant shift towards sustainability. Supply-side policies, such as Senate Bill 1137, which bans new oil and gas wells within 3,200 feet of sensitive areas, signal a commitment to environmental and public health protection. To gauge SB 1137's impact accurately, the existing model must be adapted to incorporate this setback distance. This capstone project aims to bridge this gap by updating the model and creating accessible educational materials for Californians. Objectives include updating the model, predicting well locations and oil extraction using machine learning, and developing a public online app with R Shiny. The MEDS capstone group will investigate the effects of the 3,200-foot setback distance on emissions, employment, and health, contributing to the evidence supporting SB 1137.

opencc-zeroJun 2024View details →

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Figure 2 in The employment of a conformal polydopamine thin layer reduces the cytotoxicity of silver nanoparticles

Figure 2. Cell viability test of NP systems at different concentrations.

opencc-by-4.0Jan 2020View details →

zenodo36/100

BIOPLAT-EU: Target Area Base Layer providing statistical information on demography, employment and land use for sustainability assessment

This dataset provides information on demographic variables related to population and employment as well as land use/land cover share on the basis of local administrative units (LAU). Within the BIOPLAT-EU project, this information is integrated into the webGIS  sustainability assessment tool. Main source of the administrative unit geometries is the spatial data set of local administrative units (LAU) (2016) provided by the European Commission (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/lau#lau19). The data set was extended by Level 2 administrative boundaries of Albania (https://data.humdata.org/dataset/albania-administrative-level-0-3-boundaries) and Level 3 administrative boundaries of Ukraine as of 2017 (https://data.humdata.org/m/dataset/ukraine-administrative-boundaries-as-of-q2-2017?force_layout=light) Data on demography (2016) for LAU + Albania was acquired using the Eurostat statistical database (https://ec.europa.eu/eurostat/web/main/data/database). To calculate land use share for LAU and Albania, Corine Land cover (CLC) data from 2018 was used (https://land.copernicus.eu/pan-european/corine-land-cover). CLC classes were summarized into the following classes: urban areas (UrAr), forest (Fo), permanent crops (PeCr), annual crops (AnCr), permanent meadows and pastures (PeMaPa), industrial sites (InSi), water and wetlands (We), others (Ot). For Ukraine data on demography provided by the State Statistics Survey of Ukraine (http://www.ukrstat.gov.ua/) . To calculate land use share per administrative unit, the land use map produced by Myroniuk et al. 2020 (https://doi.org/10.3390/rs12010187) was used. Based to this map shares of the following land use classes are calculated: urban areas (UrAr), forest (Fo), annual and permanent cropland (AnPeCr), grassland (Gra), water and wetlands (We), others (Ot).   Terms of use: These data are provided "as is". The authors make no warranty, representation, or guaranty of any type as to the completeness, accuracy, content or fitness for any particular purpose or use of any open data set made available here, nor shall any warranties be implied with respect to the data provided.

opencc-by-4.0Jun 2021View details →

dryad36/100

Nonaggressive behavior: A strategy employed by an obligate nest invader to avoid conflict with its host species

In addition to its builders, termite nests are known to house a variety of secondary opportunistic termite species so‐called inquilines, but little is known about the mechanisms governing the maintenance of these symbioses. In a single nest, host and inquiline colonies are likely to engage in conflict due to nestmate discrimination, and an intriguing question is how both species cope with each other in the long term. Evasive behaviour has been suggested as one of the mechanisms reducing the frequency of host‐inquiline encounters, yet, the confinement imposed by the nests' physical boundaries suggests that cohabiting species would eventually come across each other. Under these circumstances, it is plausible that inquilines would be required to behave accordingly to secure their housing. Here, we show that once inevitably exposed to hosts individuals, inquilines exhibit nonthreatening behaviours, displaying hence a less threatening profile and preventing conflict escalation with their hosts. By exploring the behavioural dynamics of the encounter between both cohabitants, we find empirical evidence for a lack of aggressiveness by inquilines towards their hosts. Such a nonaggressive behaviour, somewhat uncommon among termites, is characterised by evasive manoeuvres that include reversing direction, bypassing and a defensive mechanism using defecation to repel the host. The behavioural adaptations we describe may play an important role in the stability of cohabitations between host and inquiline termite species: by preventing conflict escalation, inquilines may improve considerably their chances of establishing a stable cohabitation with their hosts.

opencc-zeroJul 2021View details →

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Figure 2 in The employment of a conformal polydopamine thin layer reduces the cytotoxicity of silver nanoparticles

BIOPLAT-EU: Target Area Base Layer providing statistical information on demography, employment and land use for sustainability assessment

Nonaggressive behavior: A strategy employed by an obligate nest invader to avoid conflict with its host species

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Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro