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Fig. 3 in Distributional modeling of Mantophasmatodea (Insecta: Notoptera): a preliminary application and the need for future sampling
Fig. 3 Summed distribution for the Mantophasma genus for both ENVSC (a and b) and MAX (c and d) either considering the LPT (a and c) and ROC (b and d) thresholds
Fig. 1 in Distributional modeling of Mantophasmatodea (Insecta: Notoptera): a preliminary application and the need for future sampling
Fig. 1 Occurrences for all Mantophasmatodea (Notoptera) divided by a genera and b species. The inset map highlighted in a was increased in b
FIGURE 1 in Why Biogeographical Hypotheses Need A Well Supported Phylogenetic Framework: A Conceptual Evaluation
FIGURE 1: Relationship between species and species names in classifications. H‑K correspond to existing biological species (I, J, and K to subspecies in C); M‑P correspond to species names in biological classifications. A. Nominal species M corresponds to a monophyletic group of species. B. Nominal species M corresponds to a paraphyletic group of species. C. Nominal species N corresponds to a paraphyletic group of subspecies. D. All species names correspond to real species.
FIGURE 3 in Why Biogeographical Hypotheses Need A Well Supported Phylogenetic Framework: A Conceptual Evaluation
FIGURE 3: Areas A and B have the same number of species (10 species each). A. Distribution of species 16‑25 in area A. B. Distribution of species 2‑4, 8‑10 and 12‑15 in area B. C. Phylogenetic relationships including the taxa distributed in areas A and B showing that area B has a larger number of distantly related monophyletic groups than A.
FIGURE 2 in Why Biogeographical Hypotheses Need A Well Supported Phylogenetic Framework: A Conceptual Evaluation
FIGURE 2: The concept of the cactophilous "Drosophila serido" before and after the proper understanding of the phylogenetic relationships "below the species level". A. Distribution of D. serido and of its "sister species", D. borborema. B. Distribution patterns after the discovery of the species previously included as groups of D. serido and their relationships with D. borborema. C. Phylogenetic relationships in the group.
Report on the identification of stakeholder measurement needs for food safety
Open the record for dataset details and reuse information.
Dataset: Gold standard dataset for explainability need detection in app reviews.
<p>We crawled 90,000 app reviews from both Google Play Store and Apple App Store, including reviews from both free and paid apps. These reviews were filtered for explainability needs, and after this process, 4,495 reviews remained. Among them, 2,185 reviews indicated an explanation need, while 2,310 did not. This resulting gold standard dataset was used to train and evaluate several machine learning models and rule-based approaches for detecting explanation needs in app reviews.</p> <p>The dataset includes both balanced and unbalanced evaluation sets, as well as the original crawled data from October 2023. In addition to machine learning approaches, rule-based methods optimized for F1 score, precision, and recall are also included.</p> <p>We provide several pre-trained machine learning models (including BERT, SetFit, AdaBoost, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Random Forest, and SVM) along with training scripts and evaluation notebooks. These models can be applied directly or retrained using the included datasets.</p> <p>For further details on the structure and usage of the dataset, please refer to the README.md file within the provided ZIP archive.</p>
Digital and Physical Worlds: Exploring Consumer Information Needs During UK Bank Branch Closures
<p><span>The transition from physical to digital business models has accelerated rapidly in all sectors. This </span><span>shift is evident in the UK retail banking sector through the swift adoption of digital business models</span><span> particularly with the widespread closure of bank branches across the UK. Smooth transition to digital services requires deeper understanding of consumer behaviours. The paper highlights the early findings on the information needs that emerge during this era of bank branch closures. </span></p>
Knowledge needed for large-scale landscape restoration projects: scientific and experiential. 13.2B Session, Society for Ecological Restoration Conference 2024, Room Hurt, 16:30 -18:00, Thursday, 29th August
<p><strong>Disclaimer: </strong>The DOI of this video does not apply to the content of the slides of the contributors' presentations during this session. The authors of each communication maintain their copyright on the content of the presentations' slides.</p> <p><strong>The creators' roles were as follows:</strong> I. V. A., chair, session coordinator, and presenter; B. S., session coordinator; E. P. M., session coordinator, and presenter; H. J., presenter; F. S., presenter; W. E., presenter; G. M., presenter; and P. I., presenter.</p> <p><strong>Session description</strong></p> <p>This session aimed to explore the critical capacities and functions relevant to the large-scale ecosystem restoration (LER) economy and point out the complementarity of scientific and experiential knowledge and the key role of case studies in increasing this resilience. The restoration economy can be defined as “the market consisting of a network of businesses, investors, consumers, and government initiatives engaging in or driving the economic activity related to ecological restoration.” Its segment dealing with LER can be delimited by the large complexity of the system related to the natural, legislative, institutional, funding, project structure, and organizational network to implement the project, as well as of the economic sectors involved in implementing the projects. Success or failure depends on the interplay between the variables describing the environment of the project, its structure, and its functioning. For success, the uptake of scientific information is as important as the experiential (tacit) knowledge of practitioners, stakeholders, and policymakers. Scientific knowledge can be delivered to the LER economy by structures (models) and data (variables and measurements/estimations of their values). Still, in practice, it is often not a limiting resource. Experiential knowledge can be transferred to some extent by narratives (stories, opinions, examples in common language). The role of case studies for LER is to provide hints about what worked and did not work in other situations and, on this basis, to prepare the adaptive management and increase the resilience of each new project. The session provided opportunities for scientists to interact with practitioners about the problem of LER adaptive management and illustrate the issues with examples of scientific knowledge transfer to current or potential projects and case studies. The session was conceived as crosscutting the missions of the LER and European sections of the Society of Ecological Restoration. It is relevant for the development of the restoration economy in the EU through the implementation of the future European Nature Restoration Law, its effectiveness, and its efficiency.</p> <p> </p> <p><strong>Session report</strong></p> <p><em><strong>Three key elements emerged from the session:</strong></em></p> <p>· Even though there is a widespread need for this kind of knowledge in all phases of project and program cycle development, the role of experiential knowledge/ know-how in ecological restoration is not explicitly treated.</p> <p>· The proprietary know-how can be included in portfolios of patents for technologies and services needed in landscape restoration, which are open-access or undisclosed by practitioners. These types are functionally complementary in the restoration industry and appropriate for specific circumstances.</p> <p>· One can test hypotheses about the role of experiential knowledge in the success of landscape restoration through changes in human resources and human resource formation practice by including people or practices that increase the role of experiential knowledge in the overall knowledge capital available in the social ecosystem implementing the restoration programs and projects.</p> <p> </p> <p><em><strong>Narrative of the session:</strong></em></p> <p>The session showed exciting complementarity between various restoration project phases and provided solid arguments about the importance of experiential knowledge [1]. In the first communication, Jeoelen showed us social science research about what factors positively and negatively control cooperation in the restoration ecosystem of individuals, private organizations, NGOs, and public institutions. Then, she provided information about guidelines for practitioners about how to build trustful relations. She was asked what solutions one has when there are people in the team with undesirable traits, and the answer was to avoid such persons from the very beginning or, if one needs that in the team for other qualities, to discuss with them explicitly the problems. The second speaker, Sebastian, contributed with a complex process-based socio-ecological model presentation and its use in decision-making. His question was about the possibility of including details of cost-benefit analysis in the models of stakeholders' decisions, and the answer was that, in principle, it is possible. However, one must also consider the model's optimal complexity in terms of incertitude propagation. In the third presentation, Pal and Erik presented the practical approach to peatland restoration in catchments from Norway. In their context, it seemed feasible because of data available for hydrological modeling from other state monitoring systems and the fact that most restoration projects occurred on public land.</p> <p>Several examples demonstrated the approach's success in a catchment near Oslo, where the public's interest in recreation is high, and there is support for restoration. The question from the audience related their work to the first presentation of the session topic, namely human resources and trust building with stakeholders. In their context, the usual interactions and roundtables with stakeholders before the start of the projects, as well as continuous formal and informal communication in the implementation phase, were enough to build the needed social capital for a successful restoration outcome. In the first online presentation, Martina provided details about non-formal education projects in marine areas that need restoration ecology. The target group was children, but the projects also engaged adults and environmental professionals from the local socio-ecological systems, thus propagating the informed trust building to the whole community. Her details pointed out the experiential approach in education. Iulia from WWF Romania underlined the importance of case studies in communicating restoration's positive and negative aspects to practitioners. She touched on critical points of experiential knowledge related to the genuine national-scale social interest in restoration, despite the significant interest of local users and local authorities and even the existence of funding for restoration. The lack of cooperation between institutions is a limiting factor for up-scaling the restoration to the full Lower Danube River scale. Even in these conditions, WWF-Romania managed to implement many restoration projects on the Danube River and built a solid relationship with the local people by putting an equal accent on nature and people in the design and implementation phase of the projects. Iulia was asked what they do when there are problems with environmental management (such as illegal garbage disposal) that are not formally relevant to the restoration project but occur in the communities they work with. The answer was that they adopted a proactive and solution-oriented approach and started discussions with the people to identify potential solutions and even develop new projects in cooperation that targeted those problems. Finally, Virgil referred to the audience to the content of this supplementary material, as available on the WhatsApp group of the conference in its preliminary form, and communicated about the cross-cutting session 13.0 from Friday as a framework for further discussion of the issues and disentangling opportunities and challenges in landscape restoration. His presentation showed the relationship between scientific knowledge and experiential one (know-how) in the technology readiness level system. It illustrated this notion by developing a TRL6 environmental service for the cumulative impact assessment of multiple active management actions in contaminated river basins, including restoration of buffer zones and control of mining point sources by remediation projects [2].</p> <p> </p> <p><em><strong>Operational concept of experiential knowledge</strong>:</em></p> <p>· Experiential knowledge is undiscursive knowledge needed for practical activities, particularly ecological restoration.</p> <p>· Related terms: know-how, tacit knowledge.</p> <p>· We acquire it by working with experienced people, and the chains of transfer lead to practical traditions.</p> <p>· It can be transferred to some extent by narratives, films, etc, formally communicated by case studies in professional societies, but personal interactions are decisive for the successful transfer.</p> <p>· It complements scientific knowledge, forming the knowledge capital needed for ecological restoration.</p> <p>· It can be proprietary, non-proprietary, disclosed, and undisclosed.</p> <p>· Its availability geographically and in specific phases of the restoration programs and project cycles limits the success of the restoration.</p> <p>· Due to its complexity, large-scale ecosystem/landscape restoration is highly sensitive to the impact of a lack of experiential knowledge.</p> <p>· Experiential knowledge is needed at the first level in all phases of the program and project cycles and in all themes associated with landscape restoration (at general objectives formulation, implementation/monitoring, financing, and supporting the local economies). Appendix 1 of the full report [1] shows a potential distribution of the contributions to landscape restoration sessions and workshops by these themes, referring in several cases to experiential knowledge).</p> <p>· An extra level of experiential knowledge is needed to successfully transfer first-level know-how/ experiential knowledge.</p> <p>· Despite its unstructured and informal/unprocedural reality, one can test hypotheses about the role of experiential knowledge in the success of landscape restoration through changes in human resources and human resource formation practice by including people or practices that increase the role of experiential knowledge in the overall knowledge capital available in the social ecosystem implementing the restoration programs and projects. This complements the challenge of hypotheses testing at scientific standards of active measures in restoring ecological networks (large-scale ecosystems, landscapes).</p> <p><em><strong>Notes</strong></em></p> <p>[1] For the place of the contributions of this session in the general landscape of the conference, one can download the full report here: <a href="https://virgiliordache.com/2024/08/31/scientific-vs-experiential-knowledge-13-2b-session-report-sere-2024-tartu-estonia/">https://virgiliordache.com/2024/08/31/scientific-vs-experiential-knowledge-13-2b-session-report-sere-2024-tartu-estonia/</a></p> <p>[2] The pdf of his presentation is available for download at <a href="https://virgiliordache.com/2024/08/24/grounding-the-landscape-scale-restoration-for-biogeochemical-services-on-cumulative-impact-assessment-a-process-based-approach/">https://virgiliordache.com/2024/08/24/grounding-the-landscape-scale-restoration-for-biogeochemical-services-on-cumulative-impact-assessment-a-process-based-approach/</a></p> <p><strong>Acknowledgments</strong></p> <p>We thank the organizers of the SERE 2024 conference and the technical team involved in broadcasting and registering this video. We also thank to the persons from the audience who contributed with questions and comments.</p>
DUGseis processing example with needed data files
<p>The DUGseis software is designed for seismic data processing and developed for monitoring seismicity during hydraulic stimulations in the Bedretto Undergound Laboratory. Here, we provide a python run-file and the associated configuration file (.yaml). In the configuration file, paths to station xml files (.xml) and waveform files (.h5) are defined. As an example and to test the software, a small subset of 7 minute waveform data of two acquisition systems (system 03 ending on _03 and system 04 ending on _04) and the needed station xml files are available.</p> <p> </p> <p><span> </span></p>
Multilevel Modeling of Training Needs in Artificial Intelligence
<p>Nowadays, Artificial Intelligence (AI) is playing a rapidly increasing role in several fields of research and in almost all sectors of real life. However, few studies have assessed the effects of AI applications on training needs. This paper proposes an innovative multilevel modeling in order to investigate Awareness, Attitude and Trust towards AI and their reflections on learning needs. In particular, it is shown how a machine learning variable selection algorithm can support the definition of the optimal subset of all relevant covariates with respect to the outcome variable and improve the multilevel model performance for estimating the probability of educational needs. Thus, starting from a complex web survey to European citizens distributed in eight countries, the estimation of a multilevel binary model, defined on the basis of covariates selected through the Boruta random forest algorithm, is proposed. A discussion on the gender differences of the related estimated multilevel logit models is presented. A sensitivity analysis is also included in order to assess the prediction accuracy of the proposed multilevel logit modeling.</p> <p> </p> <p>This repository contains data generated for the manuscript: " A two-stage procedure for optimal modeling of the probability of training needs in artificial intelligence". It comprehends: (1) the dataset Data_Boruta_Random_Forest used to estimate the variables importance. (2) the dataset Data_Multilevel to perform the comparison among different multilevel binary models proposed in the paper.</p>
A comprehensive descriptive analysis : Assessing the guidance and counseling needs through cognitive and non-cognitive development of students in secondary level public schools
<p>This data has been collected from a secondary level public school in Karachi for a descriptive analysis . I</p> <p> </p>
Data from: High species diversity and turnover in granite inselberg floras highlight the need for a conservation strategy protecting many outcrops
Determining patterns of plant diversity on granite inselbergs is an important task for conservation biogeography due to mounting threats. However, beyond the tropics there are relatively few quantitative studies of floristic diversity, or consideration of these patterns and their environmental, biogeographic and historical correlates for conservation. We sought to contribute broader understanding of global patterns of species diversity on granite inselbergs and inform biodiversity conservation in the globally significant Southwest Australian Floristic Region (SWAFR). We surveyed floristics from 16 inselbergs (478 plots) across the climate gradient of the SWAFR stratified into three major habitats on each outcrop. We recorded 1060 species from 92 families. At the plot level, local soil and topographic variables affecting aridity were correlated with species richness in herbaceous (HO) and woody vegetation (WO) of soil-filled depressions, but not in woody vegetation on deeper soils at the base of outcrops (WOB). At the outcrop level, bioclimatic variables affecting aridity were correlated with species richness in two habitats (WO and WOB) but, contrary to predictions from island biogeography, were not correlated with inselberg area and isolation in any of the three habitats. Species turnover in each of the three habitats was also influenced by aridity, being correlated with bioclimatic variables and with inter-plot geographic distance, and for HO and WO habitats with local site variables. At the outcrop level, species replacement was the dominant component of species turnover in each of the three habitats, consistent with expectations for long-term stable landscapes. Our results therefore highlight high species diversity and turnover associated with granite outcrop flora. Hence, effective conservation strategies will need to focus on protecting multiple inselbergs across the entire climate gradient of the region.
Data from: What you need is what you eat? Prey selection by the bat Myotis daubentonii
Optimal foraging theory predicts that predators are selective when faced with abundant prey, but become less picky when prey gets sparse. Insectivorous bats in temperate regions are faced with the challenge of building up fat reserves vital for hibernation during a period of decreasing arthropod abundances. According to optimal foraging theory, prehibernating bats should adopt a less selective feeding behaviour – yet empirical studies have revealed many apparently generalized species to be composed of specialist individuals. Targeting the diet of the bat Myotis daubentonii, we used a combination of molecular techniques to test for seasonal changes in prey selectivity and individual-level variation in prey preferences. DNA metabarcoding was used to characterize both the prey contents of bat droppings and the insect community available as prey. To test for dietary differences among M. daubentonii individuals, we used ten microsatellite loci to assign droppings to individual bats. The comparison between consumed and available prey revealed a preference for certain prey items regardless of availability. Nonbiting midges (Chironomidae) remained the most highly consumed prey at all times, despite a significant increase in the availability of black flies (Simuliidae) towards the end of the season. The bats sampled showed no evidence of individual specialization in dietary preferences. Overall, our approach offers little support for optimal foraging theory. Thus, it shows how novel combinations of genetic markers can be used to test general theory, targeting patterns at both the level of prey communities and individual predators.
Comparison of adult census size and effective population size support the need for continued protection of two Solomon Island endemics
<p>Because a population's ability to respond to rapid change is dictated by standing genetic variation, we can better predict a population's long-term viability by estimating and then comparing adult census size (<em>N</em>) and effective population size (<em>N<sub>e</sub></em>). However, most studies only measure <em>N</em> or <em>N<sub>e</sub></em>, which can be misleading. Using a combination of field and genomic sequence data, we here estimate and compare <em>N</em> and <em>N<sub>e</sub></em> in two range-restricted endemics of the Solomon Islands. Two <em>Zosterops</em> White-eye species inhabit the small island of Kolombangara, with a high elevation species endemic to the island (<em>Z. murphyi</em>) and a low elevation species endemic to the Solomon Islands (<em>Z. kulambangrae</em>). Field observations reveal large values of <em>N </em>for both species with <em>Z. kulambangrae</em> numbering at 114,781 ± 32,233 adults, and <em>Z. murphyi</em> numbering at 64,412 ± 15,324 adults. In contrast, genomic analyses reveal that <em>N<sub>e</sub></em> was much lower than <em>N</em>, with <em>Z. kulambangrae</em> estimated at 694.5 and <em>Z. murphyi</em> at 796.1 individuals. Further, positive Tajima's D values for both species suggest that they have experienced a demographic contraction, providing a mechanism for low values of <em>N<sub>e</sub></em>. Comparison of <em>N </em>and <em>N<sub>e</sub></em> suggests that <em>Z. kulambangrae</em> and <em>Z. murphyi</em> are not at immediate threat of extinction but may be at genetic risk. Our results provide important baseline data for long-term monitoring of these island endemics, and argue for measuring both population size estimates to better gauge long-term population viability.</p>
Figure 3 in New non-invasive photo-identification technique for free-ranging giant anteaters (Myrmecophaga tridactyla) facilitates urgently needed field studies
Figure 3. Example of 4 years of consistent morphologic characteristics of a giant anteater (Myrmecophaga tridactyla). Variations in boldness of the drop-shaped black spot are typical for varying light situations. Uncommon are some white hairs within the black flag, the white 'blob' in the stripe above it and the ear shape. Photo by Lydia Möcklinghoff in the Brazilian Pantanal.
Figure 2 in New non-invasive photo-identification technique for free-ranging giant anteaters (Myrmecophaga tridactyla) facilitates urgently needed field studies
Figure 2. Example of coded photo-ID of four individual giant anteaters (Myrmecophaga tridactyla) sighted in the Brazilian Pantanal. For every picture the date, time and locality of the sighting were recorded. The arrows point to biometric traits that enable discrimination of individuals. These have been categorised in Table 1; stars refer to categories in this matrix (*foreleg, **bracelet, ***stripe, ****scars). As an example, the animals are here only shown from one lateral side. In practice, other photographs, preferably of both lateral sides as well as the front, were considered for the coding of the matrix and the catalogue. Photos by Lydia Möcklinghoff.
Figure 4 in New non-invasive photo-identification technique for free-ranging giant anteaters (Myrmecophaga tridactyla) facilitates urgently needed field studies
Figure 4. Relation between number of individuals classified by volunteers and grade of interobserver agreement relative to the identity assigned to individual giant anteaters by the first author.
Figure 1 in New non-invasive photo-identification technique for free-ranging giant anteaters (Myrmecophaga tridactyla) facilitates urgently needed field studies
Figure 1. Map of the study area, Fazenda Barranco Alto in the Southern Pantanal. The inset indicates its location in Brazil.
FIGURE 3 in On the need to follow rigorously the Rules of the Code for the subsequent designation of a nucleospecies (type species) for a nominal genus which lacked one: the case of the nominal genus Trimeresurus Lacépède, 1804 (Reptilia: Squamata: Viperidae)
FIGURE 3. Trimeresurus viridis. Lacépède, 1804. Lectophoront, MNHN 4057. Dorsal view of the head. Photograph by Patrick David.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.