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Fig. 2 in Strong phylogenetic constraint on transition metal incorporation in the mandibles of the hyper-diverse Hymenoptera (Insecta)
Fig. 2 Phylogenetic tree mapping the ranked Zn % for each taxon analyzed in this study. Zn was ranked as 0 = <0.1 wt%; 1 = 0.1– 1.0 wt%; 2 = 1.0–5.0 wt%; 3 = 5.0–10.0 wt%; and 4 => 10 wt%. Node 1 (in violet) identifies Zn enrichment as ancestral state for Apocrita (proportional likelihoods for Zn %: 0 = 0.0015, 1 = 0.0015, 2 = 0.121, 3 = 0.9785, 4 = 0.0062). Nodes 2 (Proctotrupidae + Pelecinidae) and 3 (Agaonidae) (in pink) identify losses of Zn enrichment in non-Aculeata (proportional likelihoods for Zn %: node 2: 0 = 0.9390, 1 = 0.0036, 2 = 0.0483, 3 = 0.0052, 4 = 0.0037; node 3: 0 = 0.9454, 1 = 0.0038, 2 = 0.0389, 3 = 0.0050, 4 = 0.0068). Nodes 4 (Chrysidoidea), 5 (Formicidae), 6 (Mutillidae + Sapygidae), and 7 (Ampulicidae) (in violet) identify re-acquisitions of Zn enrichment within Aculeata (proportional likelihoods for Zn %: node 4: 0 = 0.0597, 1 = 0.0112, 2 = 0.3890, 3 = 0.5156, 4 = 0.0244; node 5: 0 = 0.2138, 1 = 0.0173, 2 = 0.0174, 3 = 0.2796, 4 = 0.4717; node 6: 0 = 0.3439, 1 = 0.0233, 2 = 0.1286, 3 = 0.1291, 4 = 0.3748; node 7: 4 = 1)
Fig. 1 in Strong phylogenetic constraint on transition metal incorporation in the mandibles of the hyper-diverse Hymenoptera (Insecta)
Fig. 1 SEM pictures of mandibles and representative spectra (on base-10 logarithmic scale) from X-ray energy-dispersive analysis of mandible teeth, generated with a primary electron beam energy of 20 keV. a, b Camponotus cruentatus (Formicidae); c, d Ibalia rufipes (Ibaliidae); e, f Isodontia mexicana (Sphecidae); g, h Argidae sp. Note that Zn occurs only in a and b, and Mn occurs only in b. Squares in the SEM pictures indicate the area of the mandibles where the EDS point analysis was done. Peaks for transition metals (Zn and Mn), for Cl (the halogen co-associated with Zn), and for the basic elements of the cuticle (C and O) are highlighted
On the Understandability of Graphical and Textual Pattern-Based Behavioral Constraint Representations
<p>Experimental material & data</p>
Scalability Measurement Data for "Efficient Data Flow Constraint Analysis" Master's Thesis
<p>Raw measurement data and plots of performed scalability measurments of our proposed approach for analyzing software systems regarding data flow constraints.</p>
IEEE-30 energy system data of multi-period market with intertemporal constraints
<p>This is the dataset that is used for the original article: "Locational marginal pricing in multi-period AC OPF environment"</p> <p>The dataset consists of the following files</p> <ul> <li>Case1.zip</li> <li>Case2.zip</li> <li>Case3.zip</li> <li>case_modifications.py</li> <li>data_spec.py</li> <li>OPF_formulation.pdf</li> </ul> <p>Multiperiod AC OPF is given in OPF_formulation.pdf. Modifications of traditional IEEE 30-node case are given in case_modifications.py</p> <p>The case files incorporate input and output multiperiod AC OPF and LMP decomposition data in csv and pickle formats. Data structure of case files is given in data_spec.py.</p> <p>For python users pickle files are given. Nevertheless, python environment is not required. Specification can be read as a text file. All necessary data are repeated in csv format.</p> <p>Step 1 are to define LMPs of limited energy resources or storage resources that are formed by actual marginal resources from all time periods (first LMP definition in fig. 6 in the paper).</p> <p>Step 2 are to define all other LMPs at price-taking nodes (second LMP definition in fig. 6 in the paper).</p> <p>The following interrelation between Lagrange multipliers, LMP components, and price-bonding factors holds true:</p> <pre>assert np.max(np.abs(output_ramp.sensitivities.dot(output_ramp.offer_gen_data.price).tolist() - output_ramp.ramping_gen_data.price)) < 1e-2 if output_pt_step1.components.shape[0]: step1_pf_filter = (~output_pf.is_limited_energy) & (~output_pf.is_storage) assert np.max(np.abs(output_pt_step1.components.node_price - (output_pt_step1.components.f + output_pt_step1.components.tc_sum + output_pt_step1.components.vc_sum))) < 1e-2 assert (output_pt_step1.components.f - output_pt_step1.w_f.dot(output_pf.node_price[step1_pf_filter])).abs().max() < 1e-2 assert (output_pt_step1.components.tc_sum - pd.concat( (w.dot(output_pf.offer_price[step1_pf_filter]) for w in output_pt_step1.w_tc_list), axis=1 ).sum(axis=1)).abs().max() < 1e-2 assert np.max(np.abs(output_pt_step2.components.node_price - (output_pt_step2.components.f + output_pt_step2.components.tc_sum + output_pt_step2.components.vc_sum))) < 1e-2 assert (output_pt_step2.components.f - output_pt_step2.w_f.dot(output_pf.node_price)).abs().max() < 1e-2 assert (output_pt_step2.components.tc_sum - pd.concat( (w.dot(output_pf.offer_price) for w in output_pt_step2.w_tc_list), axis=1 ).sum(axis=1) ).abs().max() < 1e-2</pre> <p> </p>
Development of a dense cratonic keel prior to the destruction of the North China Craton: Constraints from sedimentary records and numerical simulation
<p>This file is created for Liu et al. (2019) -- ‘<em>Development of a dense cratonic keel prior to the destruction of the North China Craton: Constraints from sedimentary records and numerical simulation</em>’. The folder 'mutils-0.2' contains the quick search algorithm tsearch2 (Mutilspackage:http://milamin.sourceforge.net/downloads)</p>
Differential Rotation in Convective Envelopes: Constraints from Eclipsing Binaries
<p>Data, inlists, and scripts associated with doi:10.1093/mnras/stz2983</p>
Datasets for "Constraints on the geometry and frictional properties of the Main Himalayan Thrust using co-, post- and interseismic deformation in Nepal" "
<p>This contains GNSS and InSAR data used in the publication "Constraints on the geometry and frictional properties of the Main Himalayan Thrust using co-, post- and interseismic deformation in Nepal". GNSS data come as separate text files for east and north displacements and errors. Each text file consists of a number of columns. Each file has 40 columns, where the first column is time in decimal years after the Gorkha earthquake and the following columns correspond to each GNSS station. The time series are those obtained after the removal of "non-postseismic" deformation such as linear and sinusoidal terms</p> <p>The InSAR data are in the form of GMT GRD files at 200m resolution. Data are from Sentinel-1a track 19 and are a derived work of Copernicus data, subject to the ESA use and distribution conditions. The name of each GRD file is as follows: t19_{date1}_{date2}_hgt_ramp_200m.grd. Date 1 and date 2 are the dates of the two images used to make each interferogram.</p>
Figure 1 in Environmental constraints affect underground reproduction of the common toad (Bufo bufo)
Figure 1. Geographical location of the study area within the Carpathian Mountains (A), The map (fragment) of Peştera cu Apă Cave from Gârliştei Gorges (Aninei Mountains, Romania) showing the breeding areas (BA) location (B).
FIGURE 10. Calibration diagram for mammals, excluding Archonta. Euarchontoglires continued into Figure 11 in Constraints on the timescale of animal evolutionary history
FIGURE 10. Calibration diagram for mammals, excluding Archonta. Euarchontoglires continued into Figure 11.
FIGURE 11 in Constraints on the timescale of animal evolutionary history
FIGURE 11. Calibration diagam for Archonta, including Primates. Other mammals continued in Figure 10.
Proton temperature anisotropy constraint associated with alpha beam instability in the solar wind
<p>Simulation data and code repository for the paper "Proton temperature anisotropy constraint associated with alpha beam instability in the solar wind". This repository contains the particle-in-cell hybrid simulation code based on CAM-CL scheme and the simulation results. The parameter files are also included in the archived tar file. Another package contains the files for the data used in the paper.</p> <p> </p>
Data for the publication "Wind farm layout optimization with alignment constraints", submitted to Wind Energy Science, 2024
<p>The data in pickle format contains the optimal layouts corresponding to the numerical applications of the paper "Wind farm layout optimization with alignment constraints" submitted in Wind Energy Science, 2024.</p>
13 New Instances for the Mixed Capacitated Arc Routing Problem with Intermediate Facilities under Time Constraints (MCARPTIF) (Road Cleaning Problem)
<p><strong>Dataset Overview</strong></p> <p>This dataset includes 13 instances specifically designed for evaluating the performance of vehicle routing algorithms in road cleaning problems. Existing benchmark instances in the literature predominantly focus on waste collection applications, which are not directly suitable for testing the unique characteristics of the road cleaning problem addressed in this study. To fill this gap, we have utilized a real-world dataset from the city of Zibo and a synthetic dataset modeled after the well-known lpr dataset.</p> <p><strong>1. Zibo Road Cleaning Dataset (zb)</strong></p> <p>The Zibo dataset (zb) represents a portion of the actual road network in Zibo, consisting of three depots, 319 nodes, and 962 arcs. We partitioned this road network into three sub-networks—zb1, zb2, and zb3—based on the distance between the arcs and depots, as well as the operational capabilities of the fleet. This transformation allowed us to treat the problem as a single-depot problem for each sub-network. The zb dataset serves as a realistic benchmark for the road cleaning problem.</p> <p><strong>2. lpr-rc Dataset</strong></p> <p>To further test the heuristics, we generated the lpr-rc dataset using a generation method inspired by the lpr dataset (Belenguer et al., 2006). The lpr-rc dataset consists of ten instances (labeled lpr-rc-1 to lpr-rc-10) of varying sizes, with nodes ranging from 27 to 268 and arcs ranging from 88 to 952. Each instance features a depot fixed in the top-left corner of the road network. Infrastructure facilities (IFs), including sewage disposal stations and water replenishment stations, are uniformly distributed across the network. The number of IFs is scaled based on the network size, adding complexity to the routing tasks.</p> <p><strong>Dataset Format</strong></p> <p>The dataset is provided in Excel format, with each instance containing two sheets:</p> <ol> <li> <p><strong>Road Network</strong>: This sheet includes the unique road segment codes, the codes for the endpoints of each road segment, and the latitude and longitude coordinates for these points. Both road segment codes and endpoint codes are unique identifiers.</p> </li> <li> <p><strong>Intermediate Facilities (IFs)</strong>: This sheet provides the unique codes for each facility and their respective latitude and longitude coordinates. The facilities represent points where vehicles can perform water replenishment or sewage disposal.</p> </li> </ol> <p><strong>Vehicle Fleet Information</strong></p> <p>While the dataset does not impose specific vehicle parameters, it is designed to support the use of heterogeneous fleets. The following information describes a typical fleet of medium-sized mechanized cleaning vehicles, which we used in our study:</p> <ul> <li>The fleet is homogeneous, and we define the following parameters: <ul> <li>Q_w: Freshwater tank capacity = 8000 L</li> <li>Q_s: Sewage tank capacity = 2000 L</li> <li>R_w: Freshwater consumption rate = 83.33 L/min</li> <li>R_s: Sewage collection rate = 22.22 L/min</li> <li>\lambda_w: Duration for water replenishment at IFs = 0.25 h</li> <li>\lambda_s: Duration for sewage disposal at IFs = 0.16 h</li> <li>V_w: Service speed = 10 km/h</li> <li>V_d: Dead-heading speed (traveling without service) = 30 km/h</li> </ul> </li> </ul> <p>This fleet configuration enables a realistic simulation of road cleaning operations and can be adjusted based on real-world vehicle specifications as needed.</p> <p><strong>Availability</strong></p> <p>The 13 instances, including the zb and lpr-rc datasets, are now publicly available on Zenodo. These instances are designed to facilitate further research in road cleaning and related vehicle routing problems.</p> <p><strong>Reference</strong></p> <p>Belenguer, J.M., Benavent, E., Lacomme, P., Prins, C., 2006. Lower and upper bounds for the mixed capacitated arc routing problem. Computers & Operations Research 33, 3363–3383. doi:10.1016/j.cor.2005.02.009.</p>
Paleomagnetic Constraints on the Timing of Alteration Associated with the Emplacement of the Leqingla Pb-Zn Deposit, South Tibet, China
Open the record for dataset details and reuse information.
Data for "Estimate of biological carbon pump: External versus internal constraints" by Wu et al
<p>Data for generating figures in "<strong><span>Estimate of biological carbon pump: External versus internal constraints</span></strong>" by Wu et al.</p>
Cross-Dock Trailer Scheduling with Workforce Constraints: A Dynamic Discretization Discovery Approach
<p>The zip file contains the dataset used in the research published under the title: Cross-Dock Trailer Scheduling with Workforce Constraints: A Dynamic Discretization Discovery Approach. There are 640 instances in this dataset.</p> <p>There are a total of 1920 .csv files with names starting with: </p> <ol> <li>LoadingDoor (640 files for the 640 instances): contains the loading door index, loading door deadline, and the penalty for violating the deadline at the corresponding loading door</li> <li>Trailer_&_Processing (640 files for the 640 instances): contains the trailer index, unloading door index, and the processing time of the trailer at the unloading door</li> <li>Trailer_&_Shipment (640 files for the 640 instances): contains the trailer index, the arrival time of the trailer, loading door index, and the number of shipments to be cross-docked from the trailer to the loading door.</li> </ol>
Constraints on global mined geological resource production from limited regional water availability
<p>This depository provides the input dataset and codes/analytical procedures required to assess the sustainable capacity of mineral production considering local water resources as a constraint. The output dataset derived from the study are also provided.</p>
Data from: Developmental constraints in a wild primate
Early-life experiences can dramatically affect adult traits. However, the evolutionary origins of such early-life effects are debated. The predictive adaptive response hypothesis argues that adverse early environments prompt adaptive phenotypic adjustments that prepare animals for similar challenges in adulthood. In contrast, the developmental constraints hypothesis argues that early adversity is generally costly. To differentiate between these hypotheses, we studied two sets of wild female baboons: those born during low-rainfall, low-quality years and those born during normal-rainfall, high-quality years. For each female, we measured fertility-related fitness components during years in adulthood that matched and mismatched her early conditions. We found support for the developmental constraints hypothesis: females born in low-quality environments showed greater decreases in fertility during drought years than females born in high-quality environments, even though drought years matched the early conditions of females born in low-quality environments. Additionally, we found that females born in low-quality years to high-status mothers did not experience reduced fertility during drought years. These results indicate that early ecological adversity did not prepare individuals to cope with ecological challenges in later life. Instead, individuals that experienced at least one high-quality early environment—either ecological or social—were more resilient to ecological stress in later life. Together, these data suggest that early adversity carries lifelong costs, which is consistent with the developmental constraints hypothesis.
How long is 3 kilometres for a butterfly? Ecological constraints and functional traits explain high mitochondrial genetic diversity between Sicily and the Italian Peninsula
<p>1. Populations inhabiting Mediterranean islands often show contrasting genetic lineages, even on islands that were connected to the mainland during glacial maxima. This pattern is generated by forces acting in historical and contemporary times. Understanding these phenomena requires comparative studies relating genetic structure, functional traits and dispersal constraints.</p> <p>2. Using as a model the butterfly species living across the Messina strait separating Sicily from the Italian Peninsula (3 km wide), we aimed to unravel the mechanisms limiting the dispersal of matrilines across a narrow sea strait and producing genetic differentiation. </p> <p>3. We analysed the mitochondrial COI gene of 84 butterfly species out of 90 documented in Sicily and compared them with populations from the neighbouring southern Italian Peninsula (1398 sequences) and from the entire Palearctic region (8093 sequences). For each species, we regressed 13 functional traits and two ecological constraints to dispersal (winds experienced at the strait and climatic suitability) against genetic differentiation between Sicily and Italian Peninsula to understand the factors limiting dispersal.</p> <p>4. More than a third of the species showed different haplogroups across the strait and most of them also represented endemic haplogroups for this island. One fifth of Sicilian populations (and 32.3% of endemic lineages) had their closest relatives in distant areas, instead of the neighbouring Italian Peninsula, which suggests high relictuality. Haplotype diversity was significantly explained by length of the flight period, an intrinsic phenology trait, while genetic differentiation was explained by both intrinsic traits (wingspan and degree of generalism) and contemporary local constraints (winds experienced at the strait and climatic suitability).</p> <p>5. A relatively narrow sea strait can produce considerable differentiation among butterfly matrilines and this phenomenon showed a largely deterministic fingerprint. Because of unfavourable winds, populations of the less dispersive Sicilian butterflies tended to differentiate into endemic variants or to maintain relict populations. Understanding these phenomena required the integration of DNA sequences, species traits and physical constraints for a large taxon at continental scale. Future studies may reveal if the patterns here shown for mitochondrial DNA are also reflected in the nuclear genome or, alternatively, are the product of limited female dispersal.</p>
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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.