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1,855 results for “Framework”
Data from: A framework for modeling the impacts of searcher behavior on the efficiency of abundance surveys
<p>When planning abundance surveys, the impact of search effort on the quality of the density estimates is rarely considered. We constructed a time-budget modeling framework for abundance surveys using principles from optimal foraging theory. We link search effort to the number of sample units surveyed, searcher detection probability, the number of detections made, and the precision of the estimated resource density. This framework allowed us to determine how a surveyor should behave to produce optimal density estimates. Using data collected from quadrat and removal surveys of zebra mussels (<em>Dreissena polymorpha</em>) in central Minnesota, we applied this framework to evaluate potential improvements. By tuning searcher behavior, we find that density estimates from removal surveys of zebra mussels could be improved by up to 60% in some cases, without changing the overall survey effort. Our framework also predicts a critical population density where the best survey method switches from removal surveys at low densities to quadrat surveys at high densities, consistent with past empirical work. Our results provide insights into how to improve the performance of many survey methods in high-density environments by either tuning searcher behavior or decoupling the estimation of resource density and detection probability.</p>
Fig. 9 in Orthoceratoid and coleoid cephalopods from the Middle Triassic of Switzerland with an updated taxonomic framework for Triassic Orthoceratoidea
Fig. 9 Lectotype and paralectotype of Trematoceras elegans (MÜnster, 1841) from the Carnian of the Cassian Formation. A, C SNSB-BSPG AS VII 1014, lectotype. B, D–F SNSB-BSPG AS VII 1015, paralectotype. A External (lateral?) view. B Apertural view. C Longitudinal section. D Apical view. E Lateral view, venter right. F Ventral view
Fig. 2 in Orthoceratoid and coleoid cephalopods from the Middle Triassic of Switzerland with an updated taxonomic framework for Triassic Orthoceratoidea
Fig. 2 Orthoceratoids from the Besano Formation (Middle Triassic) of the Monte San Giorgio, Ticino, Switzerland. A–H: Trematoceras cf. elegans (MÜnster, 1841). A PIMUZ 39056, partially fixed with silicon, bed 41. B PIMUZ 39487, bed 87. C-E PIMUZ 39586, sectioned specimen, bed 45. F–G PIMUZ 39029, bed 61. H PIMUZ 39064, bed 162
Cross section shape: circular elliptical unknown Siphuncle position: central marginal unknown Fig. 4 Morphometrics of orthoconic cephalopods from the Besano Formation. Measurements are compared with discrete characters of the shell. Orange circles represent definite and orange crosses likely orthoceratoids, while blue triangles represent definite and blue crosses likely coleoids. Black crosses are indeterminable. A Apical angle, calculated from length and diameters of the specimens. B Maximum diameter in Orthoceratoid and coleoid cephalopods from the Middle Triassic of Switzerland with an updated taxonomic framework for Triassic Orthoceratoidea
Cross section shape: circular elliptical unknown Siphuncle position: central marginal unknown Fig. 4 Morphometrics of orthoconic cephalopods from the Besano Formation. Measurements are compared with discrete characters of the shell. Orange circles represent definite and orange crosses likely orthoceratoids, while blue triangles represent definite and blue crosses likely coleoids. Black crosses are indeterminable. A Apical angle, calculated from length and diameters of the specimens. B Maximum diameter
Fig. 3 in Orthoceratoid and coleoid cephalopods from the Middle Triassic of Switzerland with an updated taxonomic framework for Triassic Orthoceratoidea
Fig. 3 Ticinoteuthis chuchichaeschtli gen. et sp. nov. from the Besano Formation (Middle Triassic) of the Monte San Giorgio, Ticino, Switzerland and a co-occurring orthoceratoid for taphonomic comparison. A–D PIMUZ 39491, holotype, bed 112 A Lateral view. B Adapical view of preserved phragmocone, exposing the ventral position of the siphuncle. C Lateral view of preserved phragmocone, exposing faint traces of inclined septa. D Lateral view of adapical external mould, exposing potential traces of septa. E–G PIMUZ 39493, T. chuchichaeschtli gen. et sp. nov., bed 94. E Lateral view. F Enlarged view of longitudinal ribs. G Lateral view of counterpart. H PIMUZ 39060, bed 94, likely orthoceratoid with similar longitudinal ribs. Note the irregularity in these structures, suggesting that these represent a taphonomic artefact. Abbreviations: si = siphuncle, se = septa
Fig. 10 in Orthoceratoid and coleoid cephalopods from the Middle Triassic of Switzerland with an updated taxonomic framework for Triassic Orthoceratoidea
Fig. 10 Reconstructions of non-ammonoid cephalopods from the Anisian of Monte San Giorgio. A Breviconoteuthis breviconus (Reis, 1907); B Mojsisovicsteuthis sp.; C Ticinoteuthis chuchichaeschtli gen. et sp. nov.; D Trematoceras elegans (MÜnster, 1841); F Enoploceras rieberi Pieroni, 2022
Fig. 1 in Orthoceratoid and coleoid cephalopods from the Middle Triassic of Switzerland with an updated taxonomic framework for Triassic Orthoceratoidea
Fig. 1 Stratigraphic distribution of orthoconic cephalopods in the Besano Formation at Point 902 and corresponding strata at Monte San Giorgio. Each symbol represents one specimen. Stratigraphy modified after Röhl et al. (2001) and Pieroni (2022)
Figure 2 in Towards a framework for invasive aquatic plant survey design in Great Lakes coastal areas
Figure 2. Sample units surveyed by year (points indicate sample unit centroids). Milwaukee, 2017–2019 (A–C), Cleveland 2017, 2019 (D, E), St Joseph River 2017 (F), Detroit River 2018, 2019 (G, H), Saginaw River 2018 (I).
Figure 3 in Towards a framework for invasive aquatic plant survey design in Great Lakes coastal areas
Figure 3. Species-effort curves for each site, showing number of observed species (Sobs) and average species-effort curve (solid line) with 95% confidence interval (CI, dashed lines). The level of effort required to sample the estimated species total (Sasym; error bars show 95% CI) and 95% (S95%) of Sasym are indicated. Milwaukee (A–C, 2017–2019; D, combined), Cleveland (E, combined data), Detroit River (F, combined data), Saginaw River (G), St Joseph River (H).
A theoretical framework for learning through structural plasticity
<p>This dataset contains the data and scripts used for the preprint:</p> <div> <div><span>Tiddia, G., Sergi, L., & Golosio, B.</span> <span>(</span><span>2023</span><span>)</span><span>.</span> <span>A theoretical framework for learning through structural plasticity</span><span>.</span> <span><em>ArXiv</em></span><span>. DOI:</span> <a href="https://doi.org/10.48550/arXiv.2307.11735" rel="nofollow">https://doi.org/10.48550/arXiv.2307.11735</a></div> </div> <p>The dataset contains a README file with all the instructions for compiling and running the model. </p> <p> </p>
Evaluation datasets and results of the paper "A Framework for Measuring the Quality of Business Process Simulation Models"
<p>Datasets and files used in the evaluation of the publication entitled "A Framework for Measuring the Quality of Business Process Simulation Models", where:</p> <ul> <li><strong><em>BPS-models/</em></strong>: folder containing the BPS models used in the evaluation (the BPS models discovered by ServiceMiner are not included due to privacy reasons). <ul> <li>The BPS models discovered by SIMOD are composed of <em>i)</em> a BPMN file with the process model structure, and <em>ii)</em> a JSON file with the parameters of the simulation. These files correspond to the format of Prosimos simulation engine (<a href="https://prosimos.cloud.ut.ee/">https://prosimos.cloud.ut.ee/</a>).</li> <li>The BPS models of the Loan Application and Procure to Pay processes are composed of a BPMN file with both the process model structure and parameters, corresponding to the format of the BIMP simulator used in APROMORE (<a href="https://apromore.com/">https://apromore.com/</a>).</li> </ul> </li> <li><em><strong>measures/</strong></em>: folder containing the distance values of each measure reported in the paper.</li> <li><em><strong>original-event-logs/</strong></em>: folder containing the (train and test) event logs used in the evaluation.</li> <li><em><strong>simulated-logs/</strong></em>: folder containing the simulated logs evaluated in the paper (synthetic, SIMOD, and ServiceMiner).</li> <li><em><strong>ComputeLogDistance.py</strong></em>: script to compute the distance measures proposed in the paper.</li> </ul> <p> </p> <p>To evaluate the distance measures of a set of simulated event logs in the folder <em>simulated_logs/</em> against the test log <em>test_event_log.csv.gz</em>, run:<br><em> python ComputeLogDistance.py -cfld test_event_log.csv.gz simulated_logs/</em></p> <p>*The flag <em>-cfld</em> is optional, due to the high computational complexity of the CFLD measure.</p> <p><strong>WARNING</strong>: set the column names of each log accordingly (where <em>log_1_ids</em> are the IDs of the test log, and <em>log_2_ids</em> the IDs of the simulated logs). Examples:</p> <pre><code># Column IDs for the (train/test) real-life logs, and the SIMOD simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='start_time', end_time='end_time', resource='resource' ) # Column IDs for the Loan Application and Procure to Pay simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='Start_Time', end_time='End_Time', resource='resource' ) # Column IDs for the ServiceMiner simulated logs. EventLogIDs( case='case_id', activity='Activity', start_time='start_time', end_time='end_time', resource='Resource' )</code></pre> <p> </p>
Electronic Structure Data for "Design of Covalent Organic Frameworks through on-the-fly Batch-based Bayesian Optimization"
<p>This is a dataset of 1736 potential building blocks for the construction of covalent organic frameworks (COF). Electronic structures were calculated with the GFN1-xTB tight binding DFT approach as implemented in the xTB package (v6.2.3). The dataset contains all necessary inputs and outputs from these calculations. Structures were optimised with xTB's internal normal coordinate rational function optimizer (ANCopt) at the default geometry convergence criterion.</p> <p>The dataset contains calculations for two major parameters determining the suitability of the resulting COFs as an organic semiconductor, specifically, the approximate energy alignment of the homo level and the reorganization free energy.</p>
Figure 2 in AfriBasins: a new framework in FishBase for the analysis of African fresh and brackish water fish distributions, with a discussion on the Congo basin fauna
Figure 2. – Family level composition of the fish fauna of the Congo Basin s.s. "Other" includes the following families: Tetraodontidae, Bagridae, Citharinidae, Dasyatidae, Hepsetidae, Protopteridae, Syngnathidae, Mugilidae, Notopteridae, Channidae, Ariidae, Cynoglossidae, Elopidae, Pristigasteridae, Latidae, Megalopidae, Ophichthidae, Pantodontidae, Phractolaemidae, Pristidae and Carangidae.
Figure 10 in A simple framework for the exploration of functional biodiversity
Figure 10. – Distribution of body shapes in SPT-space for 31,134 species of fishes, where body shape is expressed by the median form factor (Equation 2). The numbers within the trophic level bars give the median form factors for that level, the numbers in the upper right corners are the median form factor for each cell, the numbers left of the rows indicate the median form factor, and the numbers below the columns indicate the median form factor as well as the 5th and 95th percentiles. Numbers, ages and Classes of species per cell are the same as in Fig. 7. Cell-width represents 2,000 species.
Figure 8 in A simple framework for the exploration of functional biodiversity
Figure 8. – Comparison of live history strategies of 15,801 marine and 14,509 freshwater species of fishes. Cell-width represents 1,000 species. See legend of Fig. 7 for description of the various labels and numbers.
Figure 6 in A simple framework for the exploration of functional biodiversity
Figure 6. – Boxplot of trophic levels by productivity group, for 31,148 species of fishes, with indication of median trophic level and number of species (n).
Figure 5 in A simple framework for the exploration of functional biodiversity
Figure 5. – Body weight distributions by trophic group, with indication of number of species (n) and median body weight in grams. Note that herbivores group includes detritivores.
Figure 5 in AfriBasins: a new framework in FishBase for the analysis of African fresh and brackish water fish distributions, with a discussion on the Congo basin fauna
Figure 5. – Cluster analysis on the fish distribution data for the Congo Basin s.s., based on (A) the Ochiai coefficient and (B) the correlation ratio.
Figure 7 in AfriBasins: a new framework in FishBase for the analysis of African fresh and brackish water fish distributions, with a discussion on the Congo basin fauna
Figure 7. – Evolution of the estimated number of species based on various definitions of the Congo basin: Central Congo, i.e. Kinshasa to Kisangani, without Kasai upstream from Mushie (Poll and Gosse, 1963); without lakes Bangweulu and Mweru (Roberts, 1972); entire Congo (Poll, 1973); excluding Lake Tanganyika (Lowe-McConnell, 1987); excluding lakes Tanganyika and Mweru, primary freshwater species (Teugels and Guégan, 1994); Zaïre, probably only includes primary freshwater species (Lévêque, 1997); Zaïre, probably includes primary, secondary and peripheral species (Lévêque, 1997); Congo River system, at least 700 species (Skelton, 2001); Congo and Lake Tanganyika (Revenga and Kura, 2003); Congo River system (Thieme et al., 2005); the Congo (Dumont, 2009); Congo basin including the Rift Valley region with lakes Tanganyika and Kivu and the Malagarasi basin (Snoeks et al., 2011).
Figure 3 in A simple framework for the exploration of functional biodiversity
Figure 3. – Boxplot of body weights by productivity group, with indication of number of species (n) and median body weight in grams.
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.