Find research datasets worth reusing

Holocene Reconstruction: Model and proxy data for running code

This is the model output and proxy data for running the Holocene data assimilation. The data assimilation code can be found by searching for "Holocene reconstruction code". Newer versions of the code may be found at https://github.com/Holocene-Reconstruction/Holocene-code. To read more about the Holocene data assimilation, see the paper Erb et al., 2022: "Reconstructing Holocene temperatures in time and space using paleoclimate data assimilation". Once downloaded, unzip this holocene_da_data.zip file. It contains a variety of directories. Some are empty but are present to accommodate output generated by the data assimilation code mentioned above. Directories with data are: <ul> <li>models/original_model_data/TraCE_21ka/: TraCE-21ka temperature output from: https://www.earthsystemgrid.org/project/trace.html</li> <li>models/original_model_data/HadCM3_transient21k/: HadCM3 temperature output (the spatially smoothed version, signified by "_s" at the end of the filename)</li> <li>proxies/temp12k/ - Temperature 12k proxy records from: https://lipdverse.org/</li> </ul>

Data for: Determining Young's modulus of arbitrarily-shaped granite samples using accurate grain-based modelling with Micro-RME

HaH 346 meteorite samples with shock melt veins are tested using the nanoindentation experiment with Berkovich indenter. The data includes Young's modulus of different rock-forming minerals  measured by nanoindentation test and  Raman spectrum data for jadeite and wadsleyite in HaH346 meteorite.

opencc-zeroDec 2022View details →

Online appendix and simulated data sets for assesment of Birth-Death Exposed-Infectious (BDEI) phylodynamic model estimators

The birth-death exposed-infectious (BDEI) phylodynamic model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters, from time-scaled phylogenetic trees. We implemented a highly parallelizable estimator for the BDEI model in a maximum likelihood framework (<a href="https://github.com/evolbioinfo/bdei">PyBDEI</a>) using a combination of numerical analysis methods for efficient equation resolution. This dataset contains the assessment of PyBDEI in comparison with a Bayesian implementation in <a href="http://www.beast2.org/">BEAST2</a> (mtbd package) and a deep learning estimator <a href="https://github.com/evolbioinfo/phylodeep">PhyloDeep</a>: the parameter values estimated by the 3 tools. The PyBDEI and the theoretical findings behind it are described in A Zhukova, F Hecht, Y Maday, and O Gascuel. Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees Syst Biol 2023. This dataset contains the online Appendix (Fig S1-S3 and Table S1).

Magnetotelluric data from Santos basin (SE Brazil) and inversion resistivity models exploring basin wedge and deep crustal structure beneath.

Magnetotelluric data Processed data from 90 magnetotelluric broadband stations acquired in are available in Electrical Data Interchange (EDI) and ModEM format. The MMT data were recorded in 2007 by WesternGeco Electromagnetics as part of the National Observatory Rio de Janeiro project funded by Petrobras. The campaign comprised a total of 92 sites from shallow water (about 50 m depth) to deep water (about 1600 m depth). The stations are placed along three NW-SE parallel profiles in the northwest part of Santos basin. The central profile  is approximately 160 km long and consists of 56 stations, while the west profile and east profile extend about 55 km each and contain 18 and 16 stations, respectively.   Models Inversion models and predicted data are present for two different starting resistivity model testes 10 and 1 Ohm.m. The inversion models were estimated using ModEM - modular system for inversion of electromagnetic geophysical data.

opencc-zeroDec 2022View details →

Data for: Nitrogen deposition in forests: Statistical modeling of total deposition from throughfall loads

Introduction: Nitrogen (N) gradient studies in some cases use N deposition in throughfall as measure of N deposition to forests. For evaluating critical loads of N, however, information on total N deposition is required, i.e., the sum of estimates of dry, wet and occult deposition. Methods: The present paper collects a number of studies in Europe where throughfall and total N deposition were compared in different forest types. From this dataset a function was derived which allows to estimate total N deposition from throughfall N deposition. Results: At low throughfall N deposition values, the proportion of canopy uptake is high and thus the underestimation of total deposition by throughfall N needs to be corrected. At throughfall N deposition values >20 kg N ha-1 yr-1 canopy uptake is getting less important. Conclusions: This work shows that throughfall clearly underestimates total deposition of nitrogen. With the present data set covering large parts of Europe it is possible to derive a critical load estimate from gradient studies using throughfall data.

Reuse of Model Transformations for Propagating Variability Annotations in Annotative Software Product Lines - Evaluation Data

This package contains all data that was produced for and used in the doctoral thesis for evaluating commutativity of propagating annotations in model-driven product lines. This includes the  implementation that conducts the evaluation, the measured results, and the input subjects.

Data from: Predicting primate-parasite associations with exponential random graph models

Ecological associations between hosts and parasites are influenced by host exposure and susceptibility to parasites, and by parasite traits, such as transmission mode. Advances in network analysis allow us to answer questions about the causes and consequences of traits in ecological networks in ways that could not be addressed in the past. We used a network-based framework (exponential random graph models, or ERGMs) to investigate the biogeographic, phylogenetic, and ecological characteristics of hosts and parasites characteristics that affect the probability of interactions among nonhuman primates and their parasites. Parasites included arthropods, bacteria, fungi, protozoa, viruses, and helminths. We investigated existing hypotheses, along with new predictors and an expanded host-parasite database that included 213 primate nodes, 763 parasite nodes, and 2,319 edges among them. Analyses also investigated phylogenetic relatedness, sampling effort, and spatial overlap among hosts. In addition to supporting some previous findings, our ERGM approach demonstrated that more threatened hosts had fewer parasites, and notably, that this effect was independent of threatened hosts also having a smaller geographic range. Despite having fewer parasites, threatened host species shared more parasites with other hosts, consistent with the loss of specialist parasites and threats arising from generalist parasites that can be maintained in other, non-threatened hosts. Viruses, protozoa, and helminths had broader host ranges than bacteria or fungi, and parasites that infect non-primates had a higher probability of infecting more primate species. The value of the ERGM approach for investigating the processes structuring host-parasite networks provided a more complete view of the biogeographic, phylogenetic, and ecological traits that influence parasite species richness and parasite sharing among hosts. The results supported some previous analyses and revealed new associations that warrant future research, thus revealing how hosts and parasites interact to form ecological networks.

Data for: PerchPicker classifier model v7: A catalog of American silver perch (Bairdiella chrysoura) calls for machine learning

This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep - 2023), Automated cataloging of American silver perch (Bairdiella chrysoura) calls using machine learning. The software accompanying this paper is known as PerchPicker (<a href="https://github.com/drbohnen/PerchPicker" rel="noopener">GitHub - drbohnen/PerchPicker)</a>, and the classifier model presented in the paper is v7. It consists of more than 6000 labeled perch and 6000 labeled other signals. Labeled scalogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 90 ms long. The center 30 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'perch' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the PerchPicker model or develop alternative classifiers.

opencc-by-4.0Oct 2022View details →

Use of Time Dependent Data in Bayesian Global 21cm Foreground and Signal Modelling (supplementary data)

These are the posterior files, foreground simulation data sets and chromaticity factor values used to produce the results for <a href="https://arxiv.org/abs/2210.04707">arXiv:2210.04707</a>.   A plotting script to reproduce key figures is included. Software used: <ul> <li><a href="https://github.com/PolyChord/PolyChordLite/tree/839292290a7747dbee82933bb9f7f955ac45c3ca">PolyChord</a></li> <li><a href="https://github.com/williamjameshandley/fgivenx">fgivenx</a></li> </ul>

Figure data to "Continuous similarity transformation for critical phenomena: easy-axis antiferromagnetic XXZ model"

This collection of data is complementary to the publication "Continuous similarity transformation for critical phenomena: easy-axis antiferromagnetic XXZ model", Matthias R. Walther, Dag-Björn Hering, Götz S. Uhrig, Kai P. Schmidt, arXiv:2211.05689 (https://arxiv.org/abs/2211.05689). It contains the data points calculated by the method of Continuous Similarity Transformation(CST) used in Figures 3,5,6,7 and 8 in the CSV-Format. For details on the CST, the used error estimates and physical quantities we refer the the publication. For details on the format we recommend the README.md file.

Multi-proxy agreement on Atlantic circulation dynamics since the last ice age: Model output data

This dataset contains model output for the simulations presented in: "Multi-proxy agreement on Atlantic circulation dynamics since the last ice age".

opencc-by-4.0Jul 2020View details →

Numerical modeling data

This dataset contains the numerical modelling results of Models 1-13 showed in the paper "Two Phases of Crustal Shortening in Northeastern Tibet as a Result of a Stronger Qaidam Lithosphere During the Cenozoic India–Asia Collision".

Model for Calibrating a Model to Thirty Years of Data to Capture the Accumulation of Chloride from Winter Deicers in a Shallow Aquifer

We created a ten layer model to simulate the flow and transport of chloride in Will County’s shallow aquifer with Groundwater Vistas software. This model is transient with yearly stress periods starting in 1950 and ending in 2020. The groundwater flow model was developed using the U.S. Geological Survey (USGS) finite difference code MODFLOW-NWT (McDonald and Harbaugh 1988; Niswonger et al., 2011) within the graphical user interface Groundwater Vistas 7.24 (Rumbaugh and Rumbaugh, 2020). We simulated chloride transport with the MODFLOW post-processing package MT3D-USGS (Bedekar et al., 2016). See Methods Section of 'Calibrating a Model to Thirty Years of Data to Capture the Accumulation of Chloride from Winter Deicers in a Shallow Aquifer' With questions email Cecilia Cullen (ccullen3@illinois,edu) or Daniel Abrams (dbabrams@illinois.edu)

Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle"

Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle".   Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022. Three directories can be downloaded: DataOBS :  AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022).  FigPaper : Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282).  MY_SRC_PISCES_NEMO_3.6 : Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)

Data for: Combining environmental niche models, multi-grain analyses, and species traits identifies pervasive effects of land use on butterfly biodiversity across Italy

Understanding how species respond to human activities is paramount to ecology and conservation science, one outstanding question being how large-scale patterns in land use affect biodiversity. To facilitate answering this question, we propose a novel analytical framework that combines Environmental Niche Models, multi-grain analyses, and species traits. We illustrate the framework capitalizing on the most extensive dataset compiled to date for the butterflies of Italy (106,514 observations for 288 species), assessing how agriculture and urbanization have affected biodiversity of these taxa from landscape to regional scales (3–48 km grains) across the country while accounting for its steep climatic gradients. Multiple lines of evidence suggest pervasive and scale-dependent effects of land use on butterflies in Italy. While land use explained patterns in species richness primarily at grains ≤ 12 km, idiosyncratic responses in species highlighted "winners" and "losers" across human-dominated regions. Detrimental effects of agriculture and urbanization emerged from landscape (3-km grain) to regional (48-km grain) scales, disproportionally affecting small butterflies and butterflies with a short flight curve. Human activities have therefore reorganized the biogeography of Italian butterflies, filtering out species with poor dispersal capacity and narrow niche breadth not only from local assemblages but also from regional species pools. These results suggest that global conservation efforts neglecting large-scale patterns in land use risk falling short of their goals, even for taxa typically assumed to persist in small natural areas (e.g., invertebrates). Our study also confirms that consideration of spatial scales will be crucial to implementing effective conservation actions in the Post-2020 Global Biodiversity Framework. In this context, applications of the proposed analytical framework have broad potential to identify which mechanisms underlie biodiversity change at different spatial scales. Funding statement: FR is supported by the PROBAE project "Protect butterflies across Europe through climate refugia" funded by the European Commission through Horizon 2020, Marie Skłodowska-Curie Actions (MSCA) individual fellowship, reintegration panel (Grant agreement ID: 101024579). Open Access Funding provided by Universita degli Studi di Torino within the CRUI-CARE Agreement.

Data from: Modelling harvest of Greenland barnacle geese and its implications in mitigating human-wildlife conflict

Arctic-breeding goose populations have increased in recent decades and their expansion into agricultural areas has caused increasing conflict with farmers due to the damage they cause. Lethal control and scaring are common management strategies of conflict mitigation. Management typically focuses on local/national scales, making addressing the impact of localised control on the wider population challenging, particularly when populations move over large areas and cross international borders. We construct an integrated population model (IPM) to assess the cumulative impact of all shooting harvest (hunting and derogation shooting) on the Greenland barnacle goose, Branta leucopsis. We use data from monitoring schemes throughout the migratory flyway and use population projections to evaluate the impact of potential future shooting strategies on abundance. Our model suggests flyway abundance has declined since its 2012 peak, consistent with an increase in harvest rate and low productivity. Harvest rate increase was most pronounced on Islay (rising from 2% to 7% from 2011 to 2017), suggesting this was a probable cause of flyway abundance decline. Islay abundance has declined since derogation shooting began in 2000, whilst abundance at other wintering sites has increased. This may indicate that declines in Islay abundance may be due to both shooting mortality and emigration from Islay. Should future flyway-level harvest rates increase, further declines in abundance could be expected, and are likely to be more pronounced if harvests are extended to the entire winter range. Conversely, should harvest rates decline, an increase in abundance is predicted. Projections can therefore be used to allocate flyway-level harvest rates to alleviate local pressure without hindering flyway-level management objectives. Synthesis and applications. Our findings demonstrate the impact of local harvests on global abundance, emphasising the importance of internationally coordinated monitoring and management strategies of migratory species. IPMs provide a framework for adaptation to incorporate additional data when they become available and enable comparisons of future harvest scenarios to inform management strategies throughout the flyway.

Validation of STS Conceptual Model Designing Approach. DMI374 and DMI747 Data Sets.

Surveys of DMI374 and DMI747 student groups were conducted for the purpose of validating the STS conceptual model designing method. A 5-point and dichotomous Likert questionnaire was used for the survey. The files contain response datasets used in statistical processing.

opencc-zeroFeb 2023View details →

Predicting daily activity time through ecological niche modeling and microclimatic data

1. Climate temporality is a phenomenon that affects species' activity and distribution patterns across spatial and temporal scales. Despite the global availability of microclimatic data, their use to predict activity patterns and distributions remains scarce, particularly at fine temporal scales (e.g., < month). Predicting activity patterns based on climatic data may allow us to foresee some of the consequences of climate change, particularly for ectothermic vertebrates. 2. The Gila monster exhibits marked daily and seasonal activity patterns linked to physiology and reproduction. Here we evaluate if ecological niche models fitted using microclimate data can predict temporal activity patterns using the Gila monster (Heloderma suspectum) as a study system. Further, we identified if the activity patterns are related to physiological constraints. 3. We used dated occurrences from museum specimens and human observations to generate and test ecological niche models using minimum-volume ellipsoids. We generated hourly microclimatic data for each occurrence site for ten years using the NicheMapR package. For ecological niche modeling, we compared the traditional seasonal approach versus a daily activity pattern strategy for model construction. We tested both using the omission rate of independent observations (citizen science data). Finally, we tested if unimodal and bimodal activity patterns for each season could be recreated through ecological niche modeling and if these patterns followed known physiological constraints. 4. The unimodal and bimodal activity patterns previously reported directly from tracking individuals across the year were recovered by using niche modeling and microclimate across the species' geographical range. We found that upper thermal tolerances can explain the daily activity patterns of this species. 5. We conclude that ecological niche models trained with microclimatic data can be used to predict activity patterns at fine temporal scales, particularly on ectotherm species of arid zones coping with rapid climate modifications. Further, the use of fine temporal scale variables can lead to a better niche delimitation, enhancing the results of any research objective that uses correlative models.

opencc-by-4.0Feb 2023View details →

Data set from Fischertechnik Smart Factory Model at University of St.Gallen (Custom Python Configuration)

This is about 60 mins worth of data collected from Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen. In this data set, we used a custom Python-based software stack to control the smart factory via a business process system (Camunda Platform) that calls the functionality of the smart factory via web services implemented in Python flask. MQTT is used to collect the data. Each entry in the file (low-level_log_20230206-140808.txt) corresponds to one message (as JSON object) received on a specific topic via MQTT. Each line contains all the readings of all the sensors, actuators and additional data from one CPS component (i.e., production station) at one point in time. The data set contains the following files <ul> <li>low-level_log_20230206-140808.txt: low-level IoT data from all the sensors and actuators <ul> <li>*.bpmn: executable BPMN 2.0 models of three different processes that have been executed several times via the Camunda Platform BPM system to control the smart factory</li> </ul> </li> <li>camunda_process-instance.json: event log generated by the BPM system regarding the process instance execution</li> <li>camunda_activity-instance.json: event log generated by the BPM system regarding the activity instance execution</li> </ul> Check the following publications to learn more about our research using the model factory: Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020). Using physical factory simulation models for business process management research. In Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13–18, 2020, Revised Selected Papers 18 (pp. 95-107). Springer International Publishing. Seiger, R., Zerbato, F., Burattin, A., García-Bañuelos, L., & Weber, B. (2020, October). Towards iot-driven process event log generation for conformance checking in smart factories. In 2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 20-26). IEEE. Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. Journal of Manufacturing Systems, 63, 575-592.