cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17026/dans-zvf-4p9m
title: Source code of the PRSS and CWSS applications

description: Source code accompanying the paper:M. Lubbers, P. Koopman, A. Ramsingh, J. Singer, and P. Trinder, ‘Tiered versus Tierless IoT Stacks: Comparing Smart Campus Software Architectures’, presented at the 10th International Conference on the Internet of Things, Malmö, 2020, doi: 10.1145/3410992.3411002.Abstract:Internet of Things (IoT) software stacks are notoriously complex, conventionally comprising multiple tiers/components and requiring that the developer not only uses multiple programming languages, but also correctly interoperate the components. A novel alternative is to use a single tierless language with a compiler that generates the code for each component, and for their correct interoperation.We report the first ever systematic comparison of tiered and tierless IoT software architectures. The comparison is based on two implementations of a non-trivial smart campus application. PRSS has a conventional tiered Python-based architecture, and Clean Wemos Super Sensors (CWSS) has a novel tierless architecture based on Clean and the iTask and mTask embedded DSLs. An operational comparison of CWSS and PRSS demonstrates that they have equivalent functionality, and that both meet the University of Glasgow (UoG) smart campus requirements.Crucially, the tierless CWSS stack requires 70% less code than the tiered PRSS stack. We analyse the impact of the following three main factors. (1) Tierless developers need to manage less interoperation: CWSS uses two DSLs in a single paradigm where PRSS uses five languages and three paradigms. (2) Tierless developers benefit from automatically generated, and hence correct, communication. (3) Tierless developers can exploit the powerful high-level abstractions such as Task Oriented Programming (TOP) in CWSS.Contents:- README.md: this readme containing information about both applications- cwss.tgz: source code for the mTask based smart campus applications dubbed CWSS.It consists of:* A snapshot of the mTask git repository: https://gitlab.science.ru.nl/mlubbers/mTask* A Clean programming language compiler.* The source code for the CWSS application.- prss.zip: source code for the anyscale sensor based smart campus application dubbed PRSS.It consists of:* A snapshot of the anyscale-sensors git repository: https://bitbucket.org/jsinger/anyscale-sensors
DOI: 10.17026/dans-zdg-y4sg
title: Source code of the resource analysis compiler

description: Source code accompanying the paperMarkus Klinik, Jurriaan Hage, Jan Martin Jansen, and Rinus Plasmeijer. Predicting resource consumption of higher-order workflows. In Proceedings of PEPM 2017, Paris, France, January 18-20, 2017, pages 99–110. ACM, 2017a. ISBN 978-1-4503-4721-1.CONTENTS- *.dcl/.icl: the Clean modules of the analyzer- *.prj: project files for the main program and the test- test: test cases for the corresponding Clean module, includes source code forthe TestFramework needed to run the tests- bash_completion.d: source this file in your .bashrc to get simplecommand-line completion for the mtasks command.- programs: example programs to demonstrate the analyzer- More information on how to compile and run this program can be found in README.txtSHORT SUMMARYWe present a type and effect system for the static analysis of programs written in a simplified version of iTasks.iTasks is a workflow specification language embedded in Clean, a general-purpose functional programming language.Given costs for basic tasks, our analysis calculates an upper bound of the total cost of a workflow.The analysis has to deal with the domain-specific features of iTasks, in particular parallel and sequential composition of tasks, as well as the general-purpose features of Clean, in particular let-polymorphism, higher-order functions, recursion and lazy evaluation.Costs are vectors of natural numbers where every element represents some resource, either consumable or reusable.
DOI: 10.17026/dans-z28-jb59
title: Immunisation for HIV-Infected People

description: Vaccines are critical components for protecting HIV-infected adults from an increasing number of preventable diseases. However, missed opportunities for vaccination among HIV-infected persons persist, likely due to concerns regarding the safety and efficacy of vaccines, as well as the changing nature of vaccine guidelines. In addition, the optimal timing of vaccination among HIV-infected adults with regard to HIV stage and receipt of antiretroviral therapy remain an important question. This article provides a review of the current recommendations regarding vaccines among HIV-infected adults and a comprehensive summary of the evidence-based literature of the benefits and risks of vaccines among this vulnerable population.
DOI: 10.17026/dans-xvb-b9w9
title: Source code of the resource-time analysis compiler

description: Source code accompanying the paper:Klinik, M., Jansen, J.M. & Plasmeijer, R. (2017). The Sky is the Limit: Analysing Resource Consumption Over Time Using Skylines. In N. Wu (Ed.), IFL 2017: Proceedings of the 29th Symposium on the Implementation and Application of Functional Programming Languages, Bristol, United Kingdom — August 30 - September 01, 2017 (pp. 8-1-8-12). New York: ACMCONTENTS- *.dcl/.icl: the Clean modules of the analyzer- *Spec.icl/.dcl: test cases for the corresponding Clean module- *.prj.default: original project files for the main program and the testcases. Must be renamed to .prj to compile. This is because the clean compilermodifies those files, but we don't want the modification under versioncontrol.- bash_completion.d: source this file in your .bashrc to get simplecommand-line completion for the mtasks command.- test: source code of the TestFramework, needed to run the unit tests- boxes: some brainstorming how skylines are appended and stacked- programs: example programs to demonstrate the analyzer- More information on how to compile and run this program can be found in README.txt- We ran into some problems when trying to organize the source code in subdirectories, because compile errors of the Clean compiler does not reflect module structure well enough for integration with external tools. That is why all source code is in one directory.SHORT SUMMARYIn this paper we present a static analysis for costs of higher-order workflows, where costs are maps from resource types to simple functions over time. We present a type and effect system together with an algorithm that yields safe approximations for the cost functions of programs.
DOI: 10.17026/dans-29u-kyh8
title: Source code for the SECA symbolic execution engine

description: Source code accompanying the paper:Markus Klinik, Bernard van Gastel, Cynthia Kop, and Marko van Eekelen. Skylines for symbolic energy consumption analysis. In Proceedings of 25th FMICS. Springer, 2020.CONTENTS- app: Contains the main program.- src: Contains the source code of the compiler, the analysis itself, the merge algorithm, and the graph generator.- test: Contains unit tests; mostly interesting for developers.- programs: Contains various test programs to demonstrate the analysis.- More information on how to compile and run this program can be found in README.txtSHORT SUMMARYEnergy consumption in embedded systems plays a large role as it has implications for the power supply and the batteries used. Programmers of these systems should consider how their programs control external devices , and where energy consumption hotspots lie. We present a static analysis to predict and visualize energy consumption of external devices controlled by programs written in a simple imperative programming language. Currently available energy consumption analysis techniques generate graphs over time, which makes it difficult to see from where in the source code the consumption originates. Our method generates graphs over source locations, called skyline diagrams, showing the maximum power draw for each line of source code. Our method harnessess symbolic execution extended with support for controlling external devices. This gives accurate predictions and complete code path coverage, as far as the limits of computability allow. To make the diagrams easier to understand, we introduce a merge algorithm that condenses all skylines into a concise overview. We demonstrate the potential by analysing various example programs with our prototype implementation. We envision this approach being used to identify energy consumption hotspots of embedded systems during the design and development phase, in a less involved way than traditional approaches.
DOI: 10.17026/dans-z54-yz6c
title: A speleothem record from Portugal reveals phases of increased winter precipitation in western Iberia during the Holocene

description: Two speleothems collected from the Galeria das Lâminas (GdL), in the greater Almonda Karst System (Torres Novas, Portugal). The chronology is substantiated by U-Th dates. Included data: Stable isotope data and age-depth model for both specimens.
DOI: 10.17026/dans-xya-4urm
title: Source code for the genetic algorithm scheduler

description: Source code accompanying the paper:Klinik, M.A.A., Jansen, J.M. & Plasmeijer, R. (2019). Resource Scheduling with Computable Constraints for Maritime Command and Control. In MAST Asia 2019, Makuhari Messe, Chiba, Tokyo, Japan 17-19 June 2019. S.l.: MASTDESCRIPTIONScheduler for a variant of the Multi-Skill Resource-Constrained ProjectScheduling Problem (MSRCPSP). It uses a genetic algorithm to compute a set ofpossible schedules for a given instance definition.CONTENTS- *.icl/.dcl: implementation of the scheduler and all utilities- *.prj.default: rename these files to .prj to get project files forcompilation. The clean compiler modifies these files, and we don't want thesemodification under version control.- main.prj.default: project file for the web application- mainIO.prj.default: project file for the command-line application- test.prj.default: project file for the test cases- simple-genetic-algorithm: genetic algorithm library, used by the scheduler.- TestFramework: test framework library we use to run the test cases- instances: some example scheduling problems, to demonstrate the scheduler- More information on how to compile and run this program can be found in README.txtSHORT SUMMARYMaritime command and control currently trends towards reduced crew size with highly trained crew members, with the intention of allowing more flexible deployment.As people no longer have fixed roles, dynamic scheduling of personnel and equipment is required.In this paper we identify what kind of scheduling problem arises for this, and how to solve it.We focus on the requirements of incident response and damage control scenarios, which can be anticipated but are impossible to plan out in advance.We perform a literature study to put our scheduling problem in context and present an algorithm that satisfies the identified requirements.One particular requirement we want to support is the assessment of schedules with user-defined, arbitrary computable quality metrics.The estimation of how good a resource, be it a person or a machine, will utilize its capability for a given task should consider factors like weather, positions of resources, or equipment degradation.This should go alongside classical metrics like length of a schedule's critical path.Our work should be seen as a puzzle piece for the development of an integrated mission support environment.
DOI: 10.17026/dans-ztc-57yq
title: Crystal Methamphetamine Use Among HIV-positive Gay and Bisexual Men

description: This study describes factors associated with methamphetamine initiation in a racially diverse sample of methamphetamine-using, HIV-positive gay and bisexual men. A factor analysis was conducted on reasons for initiation, and four factors were identified: to party, to cope, for energy, and to improve self-esteem. Methamphetamine to party accounted for more than one-third of the variance in the factor analysis. Methamphetamine to cope captured almost 9% of the variance, methamphetamine for energy accounted for approximately 8% of the variance, and methamphetamine for self esteem accounted for approximately 7% of the variance. Regression analyses revealed differential associations between methamphetamine initiation factors and HIV risk behaviours. Methamphetamine for self esteem predicted binge methamphetamine use, while methamphetamine to cope was associated with injecting methamphetamine. Using methamphetamine for energy was associated with number of illicit drugs used and using methamphetamine to party was associated with having a greater number of STIs. These findings suggest that methamphetamine initiation among gay and bisexual men is multifaceted, which could have implications for intervention development.
DOI: 10.17026/dans-xjb-xzw9
title: BFCS: Bayes Factors of Covariance Structures

description: Gene regulatory networks play a crucial role in controlling an organism’s biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. We propose a novel efficient Bayesian method (BFCS) for discovering local causal relationships among triplets of (normally distributed) variables. In our approach, we score covariance structures for each triplet in one go and incorporate available background knowledge in the form of priors to derive posterior probabilities over local causal structures. Our method is flexible in the sense that it allows for different types of causal structures and assumptions. The proposed algorithm produces stable and conservative posterior probability estimates over local causal structures that can be used to derive an honest ranking of the most meaningful regulatory relationships.The data set contains source code implementing the BFCS algorithm, which is described in the article titled "A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks" (http://proceedings.mlr.press/v72/bucur18a.html) by Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen and Tom Heskes, as well as in the follow-up extension titled "Large-scale Local Causal Inference of Gene RegulatoryRelationships" (https://doi.org/10.1016/j.ijar.2019.08.012). The data set also contains simulated data necessary for reproducing the figures in the article as well as routines necessary for recreating it. This research is presented in Chapter 3 of the PhD thesis titled "Being Bayesian about Causal Inference" by Ioan Gabriel Bucur. The code is written in the R and C++ programming languages.
DOI: 10.17026/dans-xuj-vggs
title: MASSIVE: Model Assessment and Stochastic Search for Instrumental Variable Estimation

description: The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal inference. One popular approach is to utilize these many-dimensional measurements as instrumental variables (instruments) for improving the causal effect estimate between other pairs of variables. Unfortunately, searching for proper instruments in a many-dimensional set of candidates is a daunting task due to the intractable model space and the fact that we cannot directly test which of these candidates are valid. We propose a general and efficient causal inference algorithm (MASSIVE) consisting of Model Assessment and Stochastic Search for Instrumental Variable Estimation. The MASSIVE algorithm accounts for model uncertainty by performing Bayesian model averaging over the most promising many dimensional instrumental variable models, while at the same time employing weaker assumptions regarding the data generating process compared to similar methods.The data set contains source code implementing the MASSIVE algorithm, which is described in the article titled "MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models"(http://proceedings.mlr.press/v124/gabriel-bucur20a.html) by Ioan Gabriel Bucur, Tom Claassen and Tom Heskes. The data set also contains simulated data necessary for reproducing the figures in the article as well as routines necessary for recreating it. This research is presented in Chapter 5 of the PhD thesis titled "Being Bayesian about Causal Inference" byIoan Gabriel Bucur. The code is written in the R and C++ programming languages.
DOI: 10.17026/dans-xeq-5ag3
title: building landscape data

description: Analysis of the spatial differentiation and scale effects of the three-dimensional architectural landscape in the main urban area of Xi'an
DOI: 10.17026/dans-z28-s8wb
title: BayesMR: A Bayesian Mendelian Randomization Approach

description: The use of genetic variants as instrumental variables – an approach known as Mendelian randomization – is a popular epidemiological method for estimating the causal effect of an exposure (phenotype, biomarker, risk factor) on a disease or health-related outcome from observational data. Instrumental variables must satisfy strong, often untestable assumptions, which meansthat finding good genetic instruments among a large list of potential candidates is challenging. This difficulty is compounded by the fact that many genetic variants influence more than one phenotype through different causal pathways, a phenomenon called horizontal pleiotropy. This leads to errors not only in estimating the magnitude of the causal effect, but also in inferringthe direction of the putative causal link. We propose a Bayesian approach (BayesMR) that is a generalization of the Mendelian randomization technique in which we allow for pleiotropic effects and, crucially, for the possibility of reverse causation. The output of the method is a posterior distribution over the target causal effect, which provides an immediate and easily interpretable measure of the uncertainty in the estimation. More importantly, with the BayesMR algorithm we can estimate the model evidence for both directions to determine how much more likely the expected direction is relative to the reverse direction.The data set contains source code implementing the BayesMR algorithm, which is described in the article titled "Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach"(https://doi.org/10.1177/0962280219851817) by Ioan Gabriel Bucur, Tom Claassen and Tom Heskes. The data set also contains simulated data necessary for exactly reproducing the figures in the article as well as the routines necessary for recreating it. This research is presented in Chapter 4 of the PhD thesis titled "Being Bayesian about Causal Inference" byIoan Gabriel Bucur. The code is written in the R and C++ programming languages.
DOI: 10.17026/dans-xyv-j4mg
title: WaterVersusFoodSecurityData

description: Dataset of spatial and aggregated simulated data for water and food security, belonging to the article 'Exploring the potential of agricultural system change as an integrated adaptation strategy for water and food security in the Indus basin'.
DOI: 10.17026/dans-xpq-3v7v
title: Integration of Landsat-8 Thermal and Visible-Short Wave Infrared Data for Improving Prediction Accuracy of Forest Leaf Area Index

description: Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A ﬁeld campaign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportionofvegetationcover,weremeasuredfor37plots. Anormaliseddifferencevegetationindex threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artiﬁcial neural network was used to predict the LAI using: (i) reﬂectance data from the Landsat-8 operational land imager (OLI) sensor; (ii) reﬂectance data from the OLI sensor and the land surface emissivity; and (iii) reﬂectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivitycomparedtothatbetweenLAIandlandsurfacetemperature. Ingeneral,LAIwaspredicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modiﬁed vegetation index had the highest accuracy for LAI prediction (R2CV = 0.33, RMSECV = 1.21 m2m−2). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artiﬁcial neural network, the prediction accuracy of LAI increased (R2CV = 0.58, RMSECV = 0.83 m2m−2). The integration of reﬂectance and land surface emissivity signiﬁcantly improved the prediction accuracy of the LAI (R2CV = 0.81, RMSECV = 0.63 m2m−2). For the ﬁrst time, our results demonstrate that the combination of Landsat-8 reﬂectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This ﬁnding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.
DOI: 10.17026/dans-zsm-n3s6
title: Using Synergy between Water Limnology and Satellite Imagery to Identify Algal Blooms Extent in a Brazilian Amazonian Reservoir

description: Monitoring algal blooms from space is a very challenging task, which becomes particularly difficult when dealing with cyanobacteria blooms. Cyanobacteria are strategic organisms adapted to a wide variety of environmental conditions. In high concentrations, they form scum on the water surface, which is a concern for public health due to the production of toxins, as well as being a nuisance. Knowledge of the ecological role of these organisms is, therefore, essential when trying to estimate their extent from satellite-based data. We present a multidisciplinary approach, based on both the ecological and the optical perspective. This approach is applied in a Brazilian Amazonian reservoir using spatial and temporal scales.