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245 results for “Mathematics”
Replication data for: 'A First-Order Statistical Exploration of the Mathematical Limits of Micromagnetic Tomography'
<p>This repository contains the random data generated for obtaining results described in "A first-order statistical exploration of the mathematical limits of Micromagnetic Tomography". All tested parameters are systematically divided over different folders and subfolders. This dataset contains only .npy files, generated with python version 3.8.8 and numpy version 1.21.5.</p> <p>Each file can be opened with numpy.load(filename)</p> <p>The resulting figures are constructed with data of at least 15 iterations; each iteration is stored in a separate folder 'test_' followed by the iteration number.</p> <p>The README file inside provides a detailled overview of the files included.</p>
MLFMF: Data Sets for Machine Learning for Mathematical Formalization
<h3>MLFMF</h3><p><strong>MLFMF (Machine Learning for Mathematical Formalization) </strong>is a collection of data sets for benchmarking recommendation systems used to support formalization of mathematics with proof assistants. These systems help humans identify which previous entries (theorems, constructions, datatypes, and postulates) are relevant in proving a new theorem or carrying out a new construction. </p><p>The MLFMF data sets provide solid benchmarking support for further investigation of the numerous machine learning approaches to formalized mathematics. With more than 250,000 entries in total, this is currently the largest collection of formalized mathematical knowledge in machine learnable format. </p><p>In addition to benchmarking the recommendation systems, the data sets can also be used for benchmarking <strong>node classification</strong> and <strong>link prediction</strong> algorithms. </p><h3>The four data sets</h3><p>Each data set is derived from a library of formalized mathematics written in proof assistants <a href="https://agda.readthedocs.io/en/v2.6.4/"><i>Agda</i></a> or <a href="https://lean-lang.org/"><i>Lean</i></a>. The collection includes </p><ol><li>the largest Lean 4 library <a href="https://github.com/leanprover-community/mathlib4"><strong>Mathlib</strong></a>,</li><li>the three largest Agda libraries:<ul><li>the <a href="https://github.com/agda/agda-stdlib"><strong>standard library</strong></a></li><li>the library of univalent mathematics <a href="https://github.com/UniMath/agda-unimath"><strong>Agda-unimath</strong></a>, and</li><li>the <a href="https://github.com/martinescardo/TypeTopology"><strong>TypeTopology</strong></a> library.</li></ul></li></ol><p>Each data set represents the corresponding library in two ways: as a heterogeneous network, and as a list of syntax trees of all the entries in the library. The network contains the (modular) structure of the library and the references between entries, while the syntax trees give complete and easily parsed information about each entry.</p><p>The Lean library data set was obtained by converting <strong>.olean</strong> files into s-expressions (see the <a href="https://github.com/andrejbauer/lean2sexp"><strong>lean2sexp</strong></a> tool).</p><p>The Agda data sets were obtained with an <a href="https://github.com/andrejbauer/agda/tree/master-sexp">s-expression extension</a> of the official Agda repository (use either master-sexp or release-2.6.3-sexp branch).</p><p>For more details, see our <a href="https://arxiv.org/abs/2310.16005"><strong>arXiv copy</strong></a><strong> </strong>of the paper.</p><h3>Directory structure</h3><p>First, the <strong>mlfmf.zip</strong> archive needs to be unzipped. It contains a separate directory for every library (for example, the standard library of Agda can be found in the stdlib directory) and some auxiliary files. Every library directory contains</p><ul><li>the <strong>network file</strong> from which the heterogeneous network can be loaded,</li><li>a zip of the <strong>entries directory</strong> that contains (many) files with abstract syntax trees. Each of those files describes a single entry of the library.</li></ul><p>In addition to the auxiliary files which are used for loading the data (and described below), the zipped sources of lean2sexp and Agda s-expression extension are present.</p><h4>Loading the data</h4><p>In addition to the data files, there is also a simple python script <strong>main.py</strong> for loading the data. To run it, you will have to install the packages listed in the file <strong>requirements.txt</strong>: <strong>tqdm</strong> and <strong>networkx</strong>. The easiest way to do so is calling <i><strong>pip install -r requirements.txt</strong></i>.</p><p>When running <strong>main.py </strong>for the first time, the script will unzip the entry files into the directory named <strong>entries</strong>. After that, the script loads the syntax trees of the entries (see the <strong>Entry</strong> class) and the network (as <i>networkx.MultiDiGraph</i> object).</p><p><i>Note. The entry files have extension <strong>.dag </strong>(directed acyclic graph), since Lean uses node sharing, which breaks the tree structure (a shared node has more than one parent node).</i></p><h3>More information</h3><p>For more information about the <strong>data collection process</strong>, <strong>detailed data (and data format) description</strong>, and <strong>baseline experiments</strong> that were already performed with these data, see our <a href="https://arxiv.org/abs/2310.16005"><strong>arXiv copy</strong></a><strong> of the paper</strong>.</p><p>For the code that was used to perform the experiments and data format description, visit our github repository <a href="https://github.com/ul-fmf/mlfmf-data"><strong>https://github.com/ul-fmf/mlfmf-data.</strong></a></p><h3>Funding</h3><p>Since not all the funders are available in the Zenodo's database, we list them here:</p><ol><li>This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-21-1-0024.</li><li>The authors also acknowledge the financial support of the Slovenian Research Agency via the research core funding No. P2-0103 and No. P1-0294.</li></ol><p> </p>
zbMATHOpenRec: A Gold Standard Dataset for Recommending Scientific Documents with Mathematical Content
<p> </p> <p>Here we include the first gold standard dataset for recommending scientific documents with mathematical content. </p> <p><strong>Contents: </strong></p> <p>As of Feb-2023, there are 421 recommendation pairs with 80 seed documents.</p> <ol> <li>All recommendation pairs are available: recommendationPairs.csv</li> <li>Each document's contents, such as title, abstract/review/summary, authors, MSC codes, Full-text link, references, etc. are available in: documentContents.csv</li> </ol> <p><strong>Dataset construction process</strong>:</p> <p>This is the first gold standard content-based RS dataset, consisting of 421 scientific research entry recommendation pairs with mathematical content. The purpose is to enable math in scientific documents for document recommendations, meaning if two documents have similar math content, one could be recommended to the other. </p> <p>To create this dataset, we analyzed 4.5 million research entires from zbMATH Open (https://zbmath.org/) and performed the following steps to obtain the final dataset:</p> <ol> <li>We selected 80 seeds that capture the most word and math tokens in zbMATH Open using statistical measures.</li> <li>Three experts, one with several years of experience reviewing research entries in mathematics, curated the recommendations for 80 seeds.</li> </ol> <p>Using this dataset, researchers can accelerate the development and testing of recommendation approaches for scientific literature with mathematical content, improving recommendations for the STEM fields where mathematical content is currently being ignored</p> <p>## License </p> <p>Legal restrictions and copyright: The zbMATH Open data is subject to the Terms and Conditions for the zbMATH Open API Service of FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur GmbH. Content generated by zbMATH Open, such as reviews, classifications, software, or author disambiguation data, are distributed under CC-BY-SA 4.0. This defines the license for the whole dataset, which also contains non-copyrighted bibliographic metadata and reference data derived from I4OSC (CC0).</p>
zbMATH Open Mathematics Subject Classification Dataset (springer)
<p>zbMATH Open Mathematics Subject Classification Dataset consisting of the following fields</p> <p><em>doi</em>: digital object identifier</p> <p><em>msc</em>: primary MSC of the article</p> <p><em>zbl_id</em>: zbMATH Open identier</p>
A Study of Improving Mathematics Assessment Readability by using GPT-3
<p><strong>A Study of Improving Mathematics Assessment Readability by using GPT-3</strong></p> <p>This dataset contains 250 math word problems from EngageNY, and their automated simplifications generated using the GPT-3 engine. There are eight ways each prompt is simplified, and three samples are taken for each prompt. Some API calls can fail or return a blank string. Those responses are omitted. Readability measures for input and output passages are generated using the Common Text Analysis Platform (<a href="http://sifnos.sfs.uni-tuebingen.de/ctap/">http://sifnos.sfs.uni-tuebingen.de/ctap/</a>). Text similarity metrics are generated by computing the % of total words between input and output and cosine similarity of the text vectors that GPT-3 embedding API generates.</p>
History in mathematics education - 6th grade
<p>Data set related to an experiment on the use of history of mathematics (ancient Chinese numeration) carried out with 108 sixth grade students. The experiment consists of three parts: ordinary mathematical exercises (items Mxx), an activity (in class, without data), and an evaluation (items Hxx).</p> <p>The experiment was carried out in October 2021.</p>
Quantifying the basic reproduction number and the under-estimated fraction of mpox cases around the world at the onset of the outbreak: a mathematical modeling and machine learning- based study
<p><span>In 2022, there was a global resurgence of mpox, with different clinico-epidemiological features compared with previous</span><br><span>outbreaks. During this resurgence, sexual contact was hypothesized as the primary transmission route, with the community</span><br><span>of men having sex with men (MSM) being disproportionately affected. Because of the stigma associated with sexually</span><br><span>transmitted infections, especially those impacting MSM, the real burden of mpox could be masked.</span><br><span>We quantified the basic reproduction number (R</span><span>0</span><span>) and the under-estimated fraction of mpox cases in 16 countries, from the</span><br><span>onset of the outbreak until early September 2022, using Bayesian inference and a compartmentalized, risk-structured (high-</span><br><span>and low-risk populations), two-route (sexual and non-sexual transmission) mathematical model. Machine learning (ML) was</span><br><span>leveraged to identify under-estimation determinants.</span><br><span>Estimated R</span><span>0</span><span> </span><span>ranged between 1·37 (Canada) and 3·68 (Germany). The under-estimation rates for the high- and low-risk</span><br><span>populations varied between 25-93% and 65-85%, respectively. The estimated total number of mpox cases, relative to the</span><br><span>reported cases, is highest in Colombia (3·60) and lowest in Canada (1·08). In the ML analysis, two clusters of countries could</span><br><span>be identified, differing in terms of attitudes towards the 2SLGBTQIAP+ community and importance of religion.</span><br><span>Given the substantial mpox under-estimation, surveillance should be enhanced and campaigns against the stigmatization of</span><br><span>MSM should be organized. Countries have different social characteristics, potentially explaining the various degrees of under-</span><br><span>reporting in mpox cases, which should be considered by studies assessing the effectiveness of community-based</span><br><span>interventions.</span></p>
Survey on the usage of Mathematical Modelling, Simulation and Optimization software
<p>This dataset contains the result of a survey we carried out in the context of the MSO4SC project in order to know which kinds of tools for simulation were using our stakeholders. The purpose was to prioritize functionalities depending on stakeholders' preferences. It was a survey with 41 questions grouped in 10 areas (impact of simulation software on their entities, usage of pre/post-processing, usage of visualization, etc...). The pdf file includes the list of questions for clarification. Such survey was answered by academia and industry from several European countries.</p>
Database of the assessment of two instructional design variables in verbal reasoning and mathematical reasoning courses from the perspective of a Peruvian pre-university center students
<p>These are the data obtained from 4 evaluations made to a sample of 630 students of a Peruvian pre-university center. First, two study variables were evaluated: teaching sequence compliance and the student's educational need according to the perspective of 315 students of the verbal reasoning course. Second, the same study variables were assessed in the remaining 315 students of the mathematical reasoning course. This information is being used in research to obtain an academic degree and later to make a publication of a scientific article.</p> <p>For the treatment of these data, inferential statistics was used through the software R version 3.4.4 (2018) The R Foundation for Statistical Computing.</p>
PLOS Comput. Biol. "Biophysically detailed mathematical models of multiscale cardiac active mechanics": datasets
<p>This repository contains the data accompanying the PLOS Computational Biology paper "<em>Biophysically detailed mathematical models of multiscale cardiac active mechanics</em>", by Francesco Regazzoni, Luca Dedè and Alfio Quarteroni.</p> <p>It contains the following datasets:</p> <ul> <li><strong>steady_state.csv</strong>: steady-state active tension for constant calcium concentration and sarcomere length (Figs. 11, 12, 13 ,14).</li> <li><strong>isometric_twitches.csv</strong>: active tension transients in isometric conditions (Figs. 15, 16, 17).</li> <li><strong>force_velocity_relationship.csv</strong>: force-velocity relationship at different calcium concentrations and sarcomere lenghts (Fig. 18).</li> <li><strong>fast_transient_response.csv</strong>: tension-elongation curve after a fast step in length (Fig. 19).</li> </ul> <p>CSV headers refer to the following variables (and measure units):</p> <ul> <li><strong>Ca</strong> (<em>μM</em>): intracellular calcium concentration.</li> <li><strong>SL</strong> (<em>μm</em>): sarcomere length.</li> <li><strong>active_tension</strong> (<em>kPa</em>): active tension.</li> <li><strong>Delta_L</strong> (<em>nm/hs</em>): step length.</li> <li><strong>velocity</strong> (<em>hs/s</em>): shortening velocity.</li> <li><strong>time</strong> (<em>s</em>): time.</li> </ul>
Computational Implementation of "Uncoupling electrokinetic flow solutions", published in Mathematical Geosciences
<p>This dataset includes Python and Mathematica scripts used to generate figures, and images used in the Mathematical Geosciences (MG) manuscript "Uncoupling Electrokinetic Flow Solutions" by Kuhlman and Malama (2020).</p> <p>Python scripts implementing eigenvalue uncoupling approach for differential equations governing 1D cylindrically symmetric electrokinetic flow problem (i.e., flow to a pumping well).</p> <ol> <li>mpmath python script (recombine-expint.py) implementing Theis "type curve" solution for an infinite domain (Figures 1-3 in MG manuscript)</li> <li>fipy python script (compare-via-fipy.py) and plotting script (plot_fipy_results.py) showing a finite-volume fully coupled solution for a similar finite domain for comparison against eigenvalue uncoupling approach (Figure 4 in MG manuscript). Also includes two shell scripts for driving python scripts for a variety of inputs.</li> </ol> <p>mathematica script (periodic-1D-steady-state-type-1.nb) for solving the algebra associated with the governing equations and plotting figures for analytical solution of periodically driven 1D solution (i.e., laboratory sinusoidal streaming potential and electroosmosis; Figures 4-9 in MG manuscript).</p> <p> </p>
Mathematics Education for Young Children in Japan: An Ethnographic Study
<p>This research project examines mathematics education for young children aged 3 to 8 years old in contemporary Japan.</p>
Data from: Temperature drives Zika virus transmission: evidence from empirical and mathematical models
<p>Temperature is a strong driver of vector-borne disease transmission. Yet, for emerging arboviruses we lack fundamental knowledge on the relationship between transmission and temperature. Current models rely on the untested assumption that Zika virus responds similarly to dengue virus, potentially limiting our ability to accurately predict the spread of Zika. We conducted experiments to estimate the thermal performance of Zika virus (ZIKV) in field-derived Aedes aegypti across eight constant temperatures. We observed strong, unimodal effects of temperature on vector competence, extrinsic incubation period, and mosquito survival. We used thermal responses of these traits to update an existing temperature-dependent model to infer temperature effects on ZIKV transmission. ZIKV transmission was optimized at 29oC, and had a thermal range of 22.7oC - 34.7oC. Thus, as temperatures move toward the predicted thermal optimum (29oC) due to climate change, urbanization, or seasonally, Zika could expand north and into longer seasons. In contrast, areas that are near the thermal optimum were predicted to experience a decrease in overall environmental suitability. We also demonstrate that the predicted thermal minimum for Zika transmission is 5oC warmer than that of dengue, and current global estimates on the environmental suitability for Zika are greatly over-predicting its possible range.</p>
Language Model for Mathematics
<p>This file provides an n-gram language model for mathematics. It was created by parsing papers from arXiv. It is in ARPA format.</p>
Kinematic data and mathematical modeling of sea star locomotion
<p>It is unclear how animals with radial symmetry control locomotion without a brain. Using a combination of experiments, mathematical modeling, and robotics, we tested the extent to which this control emerges in sea stars from the local control of their hundreds of feet and their mechanical interactions with the body. We discovered that these animals (<em>Protoreaster nodosus</em>) compensate for an experimental increase in their submerged weight by recruiting more feet that synchronize in the power stroke of the locomotor cycle. Mathematical modeling replicated this response to loading in the absence of nervous communication and demonstrated how the body weight serves as a regulator of recruitment. We built a robotic sea star with an array of independently-controlled actuators that were also recruited in greater numbers under higher loads due to their collective mechanics. These findings demonstrate that an array of actuators in biological and robotic systems are capable of cooperative transport with dynamic adjustments to loading. This form of distributed control contrasts the conventional view of animal locomotion as governed by the central nervous system and offers inspiration for the design of engineered devices with arrays of actuators.</p>
Data and Mathematical notebook for "Fractional-statistics-induced entanglement from Andreev-like tunneling"
<p>The uploaded files "SourceRightON_full.txt", "SourceLeftON_full.txt" and "BothSourcesON_full.txt" contain data for the work entitled "Fractional-statistics-induced entanglement from Andreev-like tunneling".</p> <p> </p> <p>The other file "New_Anyonic_data_fittings v2.nb" is the Mathematica notebook with which we perform the data analysis. When using it, please place three data files (mentioned above) in the Download folder.</p>
Fig. 2 in The Challenges of Incorporating Realistic Simulations of Marine Protists in Biogeochemically Based Mathematical Models
Fig. 2. The mechanistic phytoplankton model of Flynn (2001) that represents multi nutrient uptake and utilisation of N – nitrate; A – ammonium; F – bioavailable iron; P – phosphate; S – silicate; and the interaction with light (PFD). Major flows in and out of state variables (boxes) are depicted by solid arrows, with the major feedback processes depicted by dashed arrows. C – carbon biomass; Cell – cell density; NC – N C-quota; ChlC – chlorophyll C-quota, FC – iron C-quota; IPC – inorganic P C-quota, OPC – organic P C-quota; Scell – silicon cell-quota (reproduced with permission).
Dataset#1 and Dataset#2 for Making drawings speak through mathematical metrics
<p>Dataset 1 and Dataset 2 for the paper Making drawings speak through mathematical metrics</p> <p>Figurative drawing is a skill that takes time to learn, and evolves during different childhood phases that begin with scribbling and end with representational drawing. Between these phases, it is difficult to assess when and how children demonstrate intentions and representativeness in their drawings. The marks produced are increasingly goal-oriented and efficient as the child’s skills progress from scribbles to figurative drawings. Pre-figurative activities provide an opportunity to focus on drawing processes. We applied fourteen metrics to two different datasets (N=65 and N=345) to better understand the intentional and representational processes behind drawing, and combined these metrics using principal component analysis (PCA) in different biologically significant dimensions. Three dimensions were identified: efficiency based on spatial metrics, diversity with colour metrics, and temporal sequentiality. The metrics at play in each dimension are similar for both datasets, and PCA explains 77% of the variance in both datasets. These analyses differentiate scribbles by children from those drawn by adults. The three dimensions highlighted by this study provide a better understanding of the emergence of intentions and representativeness in drawings. We have already discussed the perspectives of such findings in Comparative Psychology and Evolutionary Anthropology.</p>
Mathematics Subject Classification interrater agreement dataset
<p>The Mathematics Subject Classification organizes Publications, Software, and Research Data into a hierarchical classification scheme maintained by MathSciNet (mr) and zbMATH Open (zbmath). According to the classification scheme, both organizations mr and zbmath agree on this classification and use labels to organize publications from mathematics and related fields. However, the classification of individual papers is done independently of each other. This dataset contains references to papers that occur in both collections (mr and zbmath) together with the respective classification labels.</p> <p>The dataset is followed in the follwing form</p> <pre><code>zbmath-id, zbmath-msc, mr-id, mr-msc 5635019, 55-06 57-06 55R70 57Q45 00B25, MR2556072, 54-06 55-06 5641347, 68R10 05C85, MR2588354, 68W25 05C70 05C85 5641348, 68R10, MR2588355, 68Q25 05C65 05C70 05C85 68Q15 5641349, 68Q05, MR2588356, 68Q05 5641350, 68M20 68W25 68T42, MR2588357, 68T42 68W25 5641351, 68M10, MR2588358, 68Q85 68Q10 5641352, 68R15, MR2588359, 68R15 05A05 05C78 5641353, 68T30 68R10, MR2588360, 05C62 68R10 5641354, 68Q30, MR2588361, 68Q30 60A99 60J20 5641355, 68W27 68M10, MR2588362, 68M10 05C82 05C85 68W27 68W40 5641356, 68W05 68T05, MR2588363, 68T05 62H30 5641357, 68W40, MR2588364, 05A15 68R05 5641358, 91A10 91A05 68Q17 91A06, MR2588365, 91A05 68W25 5641359, 91A10 68T42 68M10, MR2588366, 91B26 5641360, 68W40 68P05 68P10, MR2588367, 68W40 68P05 68Q87 5641361, 68P25 94A62, MR2588368, 94A62 11T71 68P25 5641362, 68Q45, MR2588369, 68Q45 05A05 5641363, 90C35, MR2588370, 68R10 05C85 68W25 90C35 5641364, 54H20, MR2588371, 37E10 37B10 37E45 </code></pre> <p>The meaning of the fields is:</p> <ul> <li><strong>zbmath-id</strong> Unique identifier from zbMATH Open. Prefix with <code>https://zbmath.org/</code> to visit additional information on the article. For example, <code>5635019</code> is associated with <a href="https://zbmath.org/5635019">https://zbmath.org/5635019</a></li> <li><strong>zbmath-msc</strong> space separated list of <a href="https://zbmath.org/classification/">Mathemematics Subject Classifiction</a> labels created by zbMATH Open staff. A description of the label can be retrieved by prefixing <code>https://zbmath.org/classification/?q=</code>. For example, <code>55-06</code> is associated with <a href="https://zbmath.org/classification/?q=55-06">https://zbmath.org/classification/?q=55-06</a></li> <li><strong>mr-id</strong> Unique identifier from MathReviews. Prefix with <code>https://mathscinet.ams.org/mathscinet-getitem?mr=</code> to retrieve additional information on the publication. For example, <code>MR3844132</code> is associated with <a href="https://mathscinet.ams.org/mathscinet-getitem?mr=MR2556072">https://mathscinet.ams.org/mathscinet-getitem?mr=MR2556072</a>.</li> <li><strong>mr-msc</strong> space seperated list of <a href="https://zbmath.org/classification/">Mathemematics Subject Classifiction</a> labels created by MathSciNet staff.</li> </ul> <p>The dataset was retrieved in 2016 by querying MathSciNet and zbMATH Open. Therefore, the classifications are based on the MSC 2010 version.</p> <p>This dataset was used in</p> <p>Schubotz M., Scharpf P., Teschke O., Kühnemund A., Breitinger C., Gipp B. (2020) AutoMSC: Automatic Assignment of Mathematics Subject Classification Labels. In: Benzmüller C., Miller B. (eds) Intelligent Computer Mathematics. CICM 2020. Lecture Notes in Computer Science, vol 12236. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-53518-6_15">https://doi.org/10.1007/978-3-030-53518-6_15</a></p> <p>and is now released to the public as an addendum to the paper.</p>
Data and Rscripts from: An integrated experimental and mathematical approach to inferring the role of food exploitation and interference interactions in shaping life history
<p><span>Intraspecific interactions can occur through many ways but the mechanisms can be broadly categorized as food exploitation and interference interactions. Identifying how intraspecific interactions impact life history is crucial to accurately predict how population density and structure influence dynamics. However, disentangling the effects of interference interactions from exploitation using experiments, is challenging for most biological systems.</span></p> <p><span>Here we propose an approach that combines experiments with modeling to infer the pathways of intraspecific interactions in a system. First, a consumer-resource model is built without intraspecific interactions. Then, the model is parameterized by fitting it to life-history data from a first experiment in which food abundance was varied. Next, hypothesized scenarios of intraspecific interactions are incorporated into the model which is then used to predict life histories with increasing competitor density. Lastly, model predictions are compared against data from a second experiment which raised groups of competitors of different densities. This comparison allows us to infer the role of interference and exploitation in shaping life history.</span></p> <p><span>We demonstrated the approach using the smaller tea tortrix <em>Adoxophyes honmai</em> across a range of temperature. We investigated five scenarios of interactions that included exploitation and three pathways for interference through some effects either on energetics to represent changes in ingestion or activity, or on mortality to model deadly interactions, or on mortality and ingestion to model cannibalism.</span></p> <p><span>Overall, intraspecific interactions in tea tortrix are best explained by a high level of deadly interactions along with some level of interference that acts on energy such as escaping and blocking access to food. Deadly interactions increase with temperature while interference that acts on energy is strongest close to the optimal temperature for reproduction. Interestingly, exploitation is more important than interference at low competitor density.</span></p> <p><span>The combination of mathematical modeling and experimentation allowed us to mechanistically characterize the intraspecific interactions in tea tortrix in a way that is readily incorporated into population-level mathematical models. The primary value of this approach, however, is that it can be applied to a much wider range of taxa than is possible with pure experimental approaches. </span></p>
ScienceDex guides
Understand access before you commit
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.