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Fig. 3. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions

Fig. 3. A time series of Cyrtostrombidium sp. (inset) abundance during ~ 1 year at a fix point in a coastal lagoon. The autocorrelation function indicates positive spikes for weeks 2, 3 and 4 suggesting a persistence of Cyrtostrombidium bloom for ~ 1 month. Horizontal dashed lines indicate the ~ 95% confidence interval for the signifi- cance of each autocorrelation value.

Fig. 2 in Good Reasons and Guidance for Mapping Planktonic Protist Distributions

Fig. 2. Geostatistical analysis of Lohmaniella oviformis (inset in a) abundance (cells ml–1) produces: a) the variogram, b) the kriging map, and c) a map of the coefficient of variation (CV). A spherical model (a, line) is fit to the empirical variogram (a, points); the points account for different number of pairs of abundance averaged on a class distance (lag). Only half of the maximum distance was calculated and represented to avoid the edge effect, where there are fewer sampling points (see text). The model (a, line) is used to predict abundance at unsampled points and to assess characteristics of patches. The model is also used to map patches of L. oviformis abundance (b, grey areas) using the kriging interpolator; a patch is operationally defined as abundance in the upper quartile. On the CV map (c), grey areas (with lower abundance and closer to edges) have the highest coefficient of variation of the estimated distribution.

Fig. 1. A in Good Reasons and Guidance for Mapping Planktonic Protist Distributions

Fig. 1. A schematic description of establishing a variogram, modelling a function, and producing maps by kriging. Samples (e.g. to determine ciliate abundance) are collected at points of a sampling grid (a). Variance estimates of ciliate abundances at points separated by a common distance (lag, h) are calculated using the equation (explanations in the text); this is repeated for each lag (three examples of lags are illustrated in a). Each variance estimate is then plotted against its respective lag to produce an empirical variogram (points in b). Then, a model is fit to the variogram data (lines in b), and the model is used to predict abundance at unsampled points and to characterize patches. The parameters of the variogram models are the nugget, the range, and the sill (see text for their interpretation). Three models are the most common: the Gaussian, spherical and exponential (thick, medium, and thin lines, respectively, in b). Models are used to map ciliate abundance by the kriging procedure, with each model producing different predicted distributions (c, d, e): the spherical and exponential produce "fuzzier" images than the Gaussian.

Fig. 6 in Good Reasons and Guidance for Mapping Planktonic Protist Distributions

Fig. 6. Patches of total phytoplankton biomass (ng C ml–1, left) and total ciliate abundance (cells ml–1, right) in the Irminger Sea, North Atlantic. The spatial coincidence indicates a potential prey-predator relationship.

opencc-by-4.0Dec 2019View details →

"I was the class teacher at that time. It was a class trip, usually organized near the end of the schoolterm in summer. The pupils went there by bike to have a barbecue at the sandy banks of the river Rhine near Dusseldorf. The landscape around is mostly dominated by agriculture and glasshouse cultures. You find a mixture of former villages nowadays completely suburbanized. The population finds jobs in the nearby urban centers like Dusseldorf, Neuss and other big cities. The reason why Irecorded the scene is simply because Iam interested in collecting sounds in general by doing recordings in different surroundings like nature, cities and everything between. My memories about the event are that it was a relaxing and funny atmosphere, which is not always the case while teaching in a classroom" [Reinhard/reinsamba]15 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice

"I was the class teacher at that time. It was a class trip, usually organized near the end of the schoolterm in summer. The pupils went there by bike to have a barbecue at the sandy banks of the river Rhine near Dusseldorf. The landscape around is mostly dominated by agriculture and glasshouse cultures. You find a mixture of former villages nowadays completely suburbanized. The population finds jobs in the nearby urban centers like Dusseldorf, Neuss and other big cities. The reason why Irecorded the scene is simply because Iam interested in collecting sounds in general by doing recordings in different surroundings like nature, cities and everything between. My memories about the event are that it was a relaxing and funny atmosphere, which is not always the case while teaching in a classroom" [Reinhard/reinsamba]15

opencc-by-4.0Jun 2020View details →

The reasonable application of Bayesian multi-model averaging to produce gross primary production with high quality in China

To reduce the uncertainties of output data from the Multi-scale Terrestrial Model Intercomparison Project (MsTMIP), Bayesian Model Averaging (BMA) was trained by observed GPP from ChinaFLUX and a set of monthly 0.5° by 0.5° GPP data from 1948 to 2010 for China was produced.

opencc-by-4.0Jul 2020View details →

SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals

SemEval-2020 Task 5   Subtask-1: Recognizing Counterfactual Statements (RCS) -- Determine whether a given sentence is counterfactual or not. Subtask-2: Detecting Antecedent and Consequent (DAC) -- Extract the antecedent and consequent part in a given counterfactual sentence.   The released dataset consists of train/test data of both subtask-1 and subtask-2. In our competition, participants could only use the corresponding dataset in each subtask.   Task 5 Codalab Website: <a href="https://competitions.codalab.org/competitions/21691">https://competitions.codalab.org/competitions/21691</a>

opencc-zeroJul 2014View details →

ORE 2014 Reasoner Competition Dataset

The ORE 2014 Reasoner Competition Dataset is a set of ontologies, curated for DL Reasoner benchmarking. All files are serialised as OWL Functional syntax.

opencc-by-4.0Oct 2023View details →

Quantitative Reasoning of Microservice System Evolution

This dataset contains the extracted data of Software Architecture Reconstruction process from TrainTicket testbench.It includes the data and service view data, in addition to the metrics calculations.This dataset is employed in a research paper with title: Assessing Microservice System Evolution through Quantitative Reasoning

opencc-by-4.0Oct 2023View details →

DATA for the paper SEMIGROUPS, KEIS AND GROUPS INDUCED BY KNOT DIAGRAMS: AN EXPERIMENTAL INVESTIGATION WITH AUTOMATED REASONING

This upload contains supplementary materials  for the paper SEMIGROUPS, KEIS AND GROUPS INDUCED BY KNOT DIAGRAMS: AN EXPERIMENTAL INVESTIGATION WITH AUTOMATED REASONING

opencc-by-4.0Apr 2024View details →

Utilization of outdoor spaces for specific activities (Barcelona, Rotterdam, Gothenburg): ratings and reasoning

What outdoor spaces are more likely to be used for different activities? In this repository, we share the data we collected and analysed to explore the impact of physical characteristics of outdoor spaces on the probability of utilization across diverse individuals, considering various age and gender groups. To collect this data we performed a crowdsourcing campaign. We recruited 409 participants from 21 European countries. To ensure a diverse range of outdoor spaces’ physical characteristics, our selection includes a range of spaces such as public squares, open marketplaces, greenspaces, pocket parks, play spaces, and streets, sourced from three European cities: Rotterdam, Barcelona, and Gothenburg. In our experiments, we presented to participants five different outdoor spaces and asked them to indicate to what degree, and why, they consider them suitable for any of the aforementioned activities. In total we collected data for 413 spaces representing 102 public open spaces, 107 streets, 114 greenspaces, 91 pocket parks, and 84 play spaces. In particular this dataset includes: <ul> <li>9700 ratings of outdoor spaces likely use for different activities (Likert scale 1-5)</li> <li>6388 short explanations of these ratings</li> <li>Each space is assigned a geolocation and a gsv_id which links this location to its corresponding google street view image</li> </ul>

opencc-by-2.0Nov 2021View details →

TG 114: Reasonableness and Tolerability in the System Of Radiological Protection: ICRP TG114 On-Going Reflections

The model of reasonableness and tolerability of radiological risk is a conceptual framework for the implementation of the ICRP principles of optimisation of protection (guided by constraints and reference levels) and application of dose limits, based mainly on the level of exposure, and closely related to the level of risk. Discussions about reasonableness and tolerability have been part of ICRP publications for many years, including the introduction of a model of risk tolerability in Publication 60. More recently, Publication 101 developed the approach to address the implementation of the optimisation process including the involvement of stakeholders and the way to elucidate what is reasonably achievable. Further considerations have been addressed while examining the ethic, identifying four core values underpinning the system of radiological protection (Publication 138). In 2019, ICRP has set up a dedicated task group (TG114) to review the historical and current perspectives on reasonableness and tolerability in order to consolidate and clarify Publication 103, and to prepare the considerations and basis needed for development of future recommendations. The ICRP on-going reflections on reasonableness and tolerability in the system of radiological protection have started to addressed a series of questions including, for example: What is the link between tolerable and reasonable? What are the considerations and criteria on which the concepts of tolerability and reasonableness are based? What are some strategies to assist in balancing competing values in determining what is tolerable and/or reasonable? For addressing these questions, the model of reasonableness and tolerability of radiological risk is revisited with the following objectives: <ul> <li>Investigate the rational for the application of the tolerability of risk as well as the borders with unacceptable level of risks and compare with the approaches adopted for managing other risks.</li> <li>Better articulate the link between tolerability and reasonableness in the process of implementation the radiological protection system, with clarification on the criteria to be considered for defining “where we don’t want to go above” and which process could be put in place for evaluating “what is reasonable”.</li> <li>Refine the radiological criteria to be considered and their link with dose limits and reference levels, relying on the radiological detriment as benchmark for tolerability and reasonableness as well as using risk comparison but without limiting to numerical criteria.</li> <li>Emphasize the importance of the application of the model for the different exposure situations, including the deliberative process with the stakeholders for the implementation of the optimisation principle, referring to good judgement, fairness, practicability, and moderateness.</li> </ul> Presented by Thierry Schneider on behalf of the whole Task Group.

opencc-by-2.0Nov 2021View details →

The Three R's Of Reasonable: Relationships, Rationale, & Resources

Central to applying the principle of optimisation in the system of radiological protection is the evaluation of what level of radiation exposure should be considered “as low as reasonably achievable” (ALARA) in a given circumstance. Determining what is “reasonable” is an abstract although somewhat intuitive concept with many potential answers depending on both the situation and those involved, whether individuals or organisations. There are common themes across exposure scenarios and within existing radiation protection guidance related to the determination of reasonableness. However, despite the fairly consistent and agreeable nature, there remains a gap in how to apply these themes in real situations. For example, without measurable goalposts (or a transparent process for setting such goalposts) for determining what constitutes ALARA, we can find ourselves misinterpreting the optimisation process as keeping exposures “as low as possible.” We thus propose herein, by consolidating and building on existing ideas, an easily understandable and actionable “reasonableness” framework. This simple, yet broadly applicable tool is intended to help radiation protection practitioners systematically reflect on all of the factors that make up “reasonable” before making a decision, although the process and decision itself will necessarily retain the complexity of the prevailing circumstance. The proposed “Rs” of Reasonable represent Relationships (stakeholders, transparency, and empathy), Rationale (contextual, scientific, and ethical justifications), and Resources (technology/technique, finances, time).

opencc-by-4.0Jan 2022View details →

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

This contains data described in detail in our paper, "Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery", where we develop a novel algorithm called RPath that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures.

dryad36/100

Understanding farmers' reasons behind mitigation decisions is key in supporting their coexistence with wildlife

1. Coexistence between wildlife and farmers can be challenging and can endanger the lives of both, prompting the provisioning of mitigation methods by governments and non-governmental organisations (NGOs). However, provision of materials, demonstration of the effectiveness of methods or willingness to uptake a method do not predict uptake of methods. 2. We used Ethnographic Decision Models to understand how farmers' work through the decisions of uptake or non-uptake of methods to mitigate crop consumption by elephants, and how the government and NGOs can either enable or impede the ability of farmers to protect themselves and their crops. 3. While farmers were motivated to use methods if they received or could afford to buy materials and they believed in the effectiveness of the methods, they still did not use them if they considered a method to be dangerous, or issues with elephants not to be severe enough, or when the supply of materials or income was not sufficient. Methods were not even considered by farmers if they lacked awareness or knowledge of the method. Government departments and NGOs enabled farmers to mitigate elephant crop consumption by providing opportunities for cash income, and providing materials and knowledge. Yet, there was disparity between the materials farmers received and methods they wished to adopt. 4. One-off inputs of materials did not result in sustainable use of mitigation methods. We see an opportunity for governmental departments or NGOs to stimulate logistics (e.g. roads and retail) to increase availability of mitigation materials since this promoted farmer autonomy. We also highlight the importance of empowering farmers by facilitating within community sharing of mitigation ideas and increasing knowledge about the effectiveness of promising wildlife conscious farming, as despite promising farmer testimonies, only a few farmers used these techniques.

opencc-zeroAug 2022View details →

opencc-by-4.0Sep 2022View details →

Certifying Parity Reasoning Efficiently Using Pseudo-Boolean Proofs --- Supplemental Material

Supplemental material for the extended version of the paper "Certifying Parity Reasoning Efficiently Using Pseudo-Boolean Proofs".

opencc-by-4.0Oct 2017View details →

Experimental data for the paper Automated reasoning for knot semigorups and \pi-orbifold groups of knots

This upload contains experimental data to supplement the paper Automated reasoning for knot semigroups and \pi-orbifold groups of knots, by Alexei Lisitsa and Alexei Vernitski, 2017 ALTERNATING-SG.zip Proofs by Prover9 for Section 3, (4-plats) KS_Models.zip Models found by Mace4 for Section 2.3 (Non-cyclic knot semigorups: small knots) PROVING-TRIVIAL.zip Proofs by Prover 9 for Section 2.2 (Cyclic knot semigroups)

dryad36/100

Data set from: Inferential reasoning in wild bumblebees

The ability to make a decision by excluding alternatives (i.e., inferential reasoning) is a type of logical reasoning that allows organisms to solve problems with incomplete information. Several species of vertebrates have been shown to find hidden food using inferential reasoning abilities. Yet little is known about invertebrates' logical reasoning capabilities. In three Experiments, I examined wild-caught bumblebees' abilities to locate a "rewarded" stimulus using direct information or incomplete information—the latter requiring bees to use inferential reasoning. To do so, I adapted 3 paradigms previously used with primates—the two-cup, three-cup, and double 2-cup tasks. Bumblebees saw either 2 paper strips (Experiment 1), 3 paper strips (Experiment 2), or 2 pairs of paper strips (Experiment 3) and experienced one of them being rewarded or unrewarded. At test, they could choose between 2 (Experiment 1), 3 (Experiment 2), or 4 paper strips (Experiment 3). Bumblebees succeeded in the three tasks and their performance was consistent with inferential reasoning. These findings highlight the importance of comparative studies with invertebrates to comprehensively track the evolution of reasoning abilities, in particular, and cognition, in general.

opencc-zeroMay 2024View details →

opencc-by-4.0Jun 2024View details →

Eight Reasons to Prioritize Brain-Computer Interface Cybersecurity

Open the record for dataset details and reuse information.