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276 results for “Integrated assessment”

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zenodo48/100

Data for "emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves"

This dataset contains codes, data, tables, andd figures (high resolution) related to the following publication: Xiong, W., K. Tanaka, P. Ciais, D. J. A. Johansson, M. Lehtveer (2022) emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves. Submitted to arXiv on 23 December 2022.

opencc-by-4.0Dec 2022View details →

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Life cycle inventories for the article: Circular Battery Production in the EU: Insights from integrating Life Cycle Assessment into System Dynamics Modeling on Recycled Content and Environmental Impacts

This repository provides the unregionalized life cycle inventories to the paper "Ginster, R., Blömeke, S., Popien, J. L., Scheller, C., Cerdas, F., Herrmann, C., & Spengler, T. S. (2024). Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts. Journal of Industrial Ecology, 1–18. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a>". <h2>Contents</h2> The repository is split into 2 parts and comprises the following files: 01_production: contains the necessary life cycle inventories for battery production. <ul> <li>01_primary: contains the life cycle inventories for battery production from primary materials.</li> <li>02_secondary: contains the life cycle inventories for battery production from secondary materials.</li> <li>03_active_material: contains the life cycle inventories for the active battery materials from primary materials.</li> <li>04_active_material: contains the life cycle inventories for the active battery materials from secondary materials.</li> </ul>   02_recycling: contains the necessary inventories for battery recycling. <ul> <li>01_process: contains the life cycle inventories for battery recycling.</li> <li>02_intermediate: contains the life cycle inventories for the intermediate system for battery recycling.</li> <li>03_output: contains the life cycle inventories for the resulting substances from battery recycling.</li> </ul> <h2>Summary</h2> These files allow to reproduce the results of our study. Each file contains the life cycle inventory of one distinct battery capacity (20, 45, 68, 85, 95, 100 kWh) with a specific cell chemistry (LFP, NCA, NMC333, NMC532, NMC622, NMC811, NMC955) for battery production (based on Knehr et al. 2022) or for battery recycling (based on Blömeke et al. 2023). <h2>Related publication</h2> More details on the scientific context is provided in the publication itself: Ginster, R., Blömeke, S., Popien, J. L., Scheller, C., Cerdas, F., Herrmann, C., & Spengler, T. S. (2024). Circular battery production in the EU: Insights from integrating life cycle assessment into system dynamics modeling on recycled content and environmental impacts. Journal of Industrial Ecology, 1–18. <a href="https://doi.org/10.1111/jiec.13527">https://doi.org/10.1111/jiec.13527</a> <h2>Funding</h2> This publication (Raphael Ginster and Steffen Blömeke) was created within the Research Training Group CircularLIB, supported by the Ministry of Science and Culture of Lower Saxony with funds from the program zukunft.niedersachsen of the Volkswagen Foundation (MWK | ZN3678). The publication on which this dataset is based were funded by the German Federal Ministry of Education and Research within the Competence Cluster Recycling & Green Battery (greenBatt) under the grant numbers 03XP0302A (Christian Scheller) and 03XP0331A (Jan-Linus Popien). The authors are responsible for the contents of this publication.

opencc-by-4.0Jan 2024View details →

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Contamination pattern and risk assessment of polar compounds in snow melt: an integrative proxy of road runoffs

Abstract To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for more than 500 chemicals using LC-HRMS. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig WWTP during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). A toxic unit-based assessment was performed to investigate the risk of adverse environmental effects in the receiving water. Description of the dataset The dataset contains the list of sampling points, the target compounds, the chemical findings, the results of the toxic unit assessment, the underlying ecotoxicity data, and the estimated compound removal rates in WWTP. The data is provided in xlsx and ods formats.

opencc-by-4.0Nov 2022View details →

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Integrated global assessment of the natural forest carbon potential (tifs)

Since we have large data files for the maps used in the paper 'Integrated global assessment of the natural forest carbon potential', we have uploaded the maps separately here. After downloading the maps, you can place them in the path: Data/BiomassMergedMaps. Then, the code for making figures will be replicable. This is version 1.1, which includes the addition of the 'readMe.txt' and the soil carbon potential map, and corrections to each model's maps in TGB for both full and net potential.

opencc-by-4.0Nov 2023View details →

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Supplementary material to the manuscript: Regionalised Heat Demand and Power-To-Heat Capacities in Germany - An Open Data Set for Assessing Renewable Energy Integration

This is the supplementary material for the manuscript: "Regionalised Heat Demand and Power-To-Heat Capacities in Germany -  an Open Data Set for Assessing Renewable Energy Integration" Article DOI: <a href="https://doi.org/10.1016/j.apenergy.2019.114161">https://doi.org/10.1016/j.apenergy.2019.114161</a> Open access preprint: <a href="https://arxiv.org/abs/1912.03763">https://arxiv.org/abs/1912.03763</a>   DESCRIPTION OF THE DATASET AND LICENSES: The subdirectory "04_results" contains the regionalised heat demand an power-to-heat capacity data on administrative district level (NUTS-3) for Germany. The subdirectories "01_census_special_evaluation_data" and "02_other_input_data" contain the utilised input data. The subdirectory "03_code" contains the developed and applied source code. The data in this repository are provided under open source licenses. For license information and other general information on the supplementary material, refer to the LICENSE files and README files in the respective subdirectories. For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the manuscript "Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration". METADATA: Sector: Residential Buildings – Space Heating and Domestic Hot Water Geographical scope: Germany Geographical resolution: Administrative districts (NUTS-3) Temporal scope: 2011, three scenarios for 2030 Temporal resolution: 15min   UNITS: In the final results folders (04_results/01_installed_heating_p2h_capacity; 04_results/02_daily_time_series; 04_results/03_yearly_time_series) the units of the data are indicated in the file names or the column names, e.g. by "in_MW". In case of unit indication in the file name, the unit refers to all columns in the file. In the intermediate results folder (04_results/00_sql_tables_exported_to_csv) all units referring to power are "kW" and all units referring to energy are "kWh". NEWS AND CONTACT: This dataset will be used as part of the <a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model</a>. We are currently enhancing the data by temporally and spatially resolved COP time series and determining load shifting potentials. If you wish to receive news or have general questions please contact: wilko.heitkoetter@dlr.de.

opencc-by-4.0Jun 2019View details →

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Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"

This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided.

opencc-by-4.0Apr 2023View details →

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Determining non-significant bits on a C++ implementation of the LeNet-5 convolutional neural network to be used for storing error correcting codes to protect weights and biases. Robustness assessment of the network after integrating the proposed codes.

The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid.It consists of the following layers:<ul><li>conv1: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li>relu1: Rectified Linear Unit (3@28x28).</li><li>max1: Subsampling buy max pooling (3@14x14).</li><li>conv2: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li>relu2: Rectified Linear Unit (6@14x14).</li><li>max2: Subsampling buy max pooling (6@7x7).</li><li>fc1: Fully connected (294, 147)</li><li>fc2: Fully connected (147, 10)</li></ul>The fault hypotheses for this work include the occurrence of:<ul><li>S0/S1: multiple adjacent stuck-at-0 and stuck-at-1 faults to determine the least significant bits of weights and biases that could be used to store the proposed error correcting codes.</li><li>BF: single, double, and triple bit-flip faults to assess the robustness of the considered CNN</li></ul>In the memory cells containing all the parameters of the CNN:  <ul><li>w: weights (float32)</li><li>b: biases (float32)</li></ul>All the images (10000) from the MNIST dataset have been used as workload.The weights and biases of the LeNet-5 architecture have been protected using six different error correcting codes that have been deployed in the least significant bits of these elements.The parity check matrices (H = P I) that define these ECCs are:<ul><li>SEC(32, 26) (Hamming) under a classic policy (see methodology below):</li></ul>        11010010001000011101101000 100000        10101001000100011011010100 010000        01100100100010010110110010 001000        00011100010001001110001101 000100        00000011110000100001111011 000010        00000000001111100000000111 000001<ul><li>SEC(23, 18) (Hamming) under a conservative policy (see methodology below):</li></ul>        111100001111000000 10000        110011101000111000 01000        101011010100100110 00100        010110110010010101 00010        001101110001001011 00001<ul><li>SEC(13, 9) (Hamming) under an aggressive policy (see methodology below):</li></ul>        110111000 1000        101100110 0100        011010101 0010        111001011 0001<ul><li>DEC(32, 21) (low redundancy and reduced overhead DEC) under a classic policy (see methodology below):</li></ul>        111000011001010010000 10000000000        110110000011101000000 01000000000        101011000110000010001 00100000000        100101101000110001000 00010000000        011010101100100000100 00001000000        010101010100001001010 00000100000        001100110010010100100 00000010000        000011110001000110010 00000001000        000000001111001101001 00000000100        000000000000111100111 00000000010        000000000000000011111 00000000001<ul><li>DEC(28, 18) (low redundancy and reduced overhead DEC) under a conservative policy (see methodology below):</li></ul>        111111000000000000 1000000000        110100111100000000 0100000000        110000100011110000 0010000000        001110010011001100 0001000000        101100001010101010 0000100000        010001001101010110 0000010000        001011000101101001 0000001000        101000011000110101 0000000100        010001110000011011 0000000010        000010100110000111 0000000001<ul><li>DEC(17, 9) (low redundancy and reduced overhead DEC) under an aggressive policy (see methodology below):</li></ul>        111110000 10000000        111001100 01000000        110101010 00100000        101010110 00010000        101101001 00001000        100110101 00000100        100011011 00000010        110000111 00000001This dataset contains the raw data obtained from:<ul><li>running exhaustive fault injection campaigns for increasingly multiple stuck-at faults in the least significant bits of all weights and biases (simultaneously) and for all the images in the workload.</li><li>running statistical fault injection campaigns for single, double, and triple bit-flip faults, randomly targeting the considered locations and images in the workload.</li></ul><h3>Files information</h3><ul><li>no_ecc folder: Results obtained for the original (not protected) version of the CNN.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults.</li><li>sampling_SBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults.</li><li>sampling_TBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults.</li><li>locating_sensitive_bits folder: Prediction obtained for all the images considered in the workload in presence of stuck-at-0/stuck-at-1 faults that simultaneously target the N least significant bits of all weights and biases. There is one file for each parameter of type of fault and range of targeted bits. Files for bits in the range [11, 0] are not included as they obtain eactly the same results as the Golden Run (faults do not alter the behaviour of the network).</li></ul></li><li>sec/classic, sec/conservative, and sec/aggressive folders: They contain the results obtained for the CNN protected by SEC(32, 26), SEC(23, 18), and SEC(13, 9), respectively.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li>sampling_SBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for single bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li><li>dec/classic, dec/conservative, and dec/aggressive folders: They contain the results obtained for the CNN protected by DEC(32, 21), DEC(28, 18), and DEC(17, 9), respectively.<ul><li>golden_run.csv: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. It must be noted that this file could be different that the golden_run.csv file for the original version of the CNN, as deploying the ECC in the weights and biases may have affected the behaviour of the network.</li><li>sampling_DBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for double bit-flip faults. They should all be tolerated by the definition of the ECC.</li><li>sampling_TBF_10000.csv: Prediction obtained for running 10000 statistical fault injection experiments for triple bit-flip faults. They could be more harmful than for the unprotected version of the CNN, as the ECC may erroneously flip correct bits.</li></ul></li></ul><h3>Methodology information</h3>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is golden_run.csv file.To locate non-significant bits in weights and biases, fault injection experiments were executed targeting all elements of all parameters of the CNN using the following procedure:<ul><li>The initial mask targeted only the least significant bit</li><li>Until the mask targets all bits of the elements (32 bits as they are single-precision floating point values):<ul><li>Affect the bits (setting them to 0 or 1 in case of stuck-at-0 or stuck-at-1 faults) identified by the mask for all elements of all parameters.</li><li>Classify all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Remove the fault from the CNN by restoring the affected bits to its previous value.</li><li>Add the next adjacent bit to the mask, so it targets an additional least significant bit.</li></ul></li></ul>The analysis of the obtained results may help in determining which bits can be used to store an ECC:<ul><li>which bits never affect the behaviour of the CNN, as the predicted classification is exactly the same than in the absence of faults.</li><li>which bits midly affect the behaviour of the CNN, as although the predicted classifications differ from those in the absence of faults, the accuracy of the network is barely affected.</li><li>which bits greatly affect the behaviour of the CNN, as the accuracy of the network is significantly affected.</li></ul>Accordingly, three different policies have been identified for deploying an ECC using these bits:<ul><li>Classic policy: The ECC protects as much bits as possible.</li><li>Conservative policy: The ECC protects all those bits that may affect the prediction of the network.</li><li>Aggressive policy: The ECC protects only those bits that significantly affect the accuracy of the network.</li></ul>After designing and deploying a single ECC and a double ECC for each of the identified policies, fault injection experiments were executed to verify their behaviour in the presence of faults.Single and double ECCs were tested against single and double bit-flip, respectively (all faults should be tolerated,) and double and triple bit-flips, respectively (a correct bit could be erroneously flipped.)Due to the heavy computational load of the decoders, statistical injection was used to run the required fault injection campaigns with a sample size (number of experiments) of 10000.Each experiment consisted in:<ul><li>Randomly selecting the image to process, and the parameter, element, and bits (mask) to be targeted by the fault.</li><li>Affecting the bits (inverting them) identified by the mask.</li><li>Classifying the selected image of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li>IMGID: Integer number identifying the considered image (1-9999).</li><li>TENSORID: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.b, 3 - conv2.w, 4 - conv2.b, 5 - fc1.w, 6 - fc1.b, 7 - fc2.w, 8 - fc2.b).</li><li>ELEMID: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - conv1.b, [0-74] - conv1.w, [0-5] - conv2.b, [0-149] - conv2.w, [0-146] - fc1.b, [0-43217] - fc1.w, [0-9] - fc2.b, [0-1469] - fc2.w).</li><li>MASK: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty]).</li><li>FAULT: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1).</li><li>SOFTMAX: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li>PRED: Integer number representing the category predicted for the processed image.</li><li>LABEL: integer number representing the actual category for the processed image.</li></ul>

opencc-by-4.0Nov 2023View details →

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Figure 1 in Integrative analysis in toxicological assessment of the insecticide Malathion in Allium cepa L. system

Figure 1. Rates of alterations found for Allium cepa cells exposed for 48h to distilled water (H O – negative control), 0.5 mg mL-1, 2 d 1.0 mg mL-1 of Malathion and methyl methanesulfonate (MMS – positive control), concerning: (A) anaphase bridge; (B) chromosome loss; (C) chromosome delay; (D) micronuclei index. KW-H = results of Kruskal-Wallis test and p = value of the statistical probability. Letters on the error bars indicate the result of the statistical Mann-Whitney U test.

opencc-by-4.0Jan 2023View details →

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Figure 3 in Integrative analysis in toxicological assessment of the insecticide Malathion in Allium cepa L. system

Figure 3. Discriminant canonical function, showing the distribution of the centroids e of the groups of the different treatments; 1: treatment submitted to distilled water; 4: positive control with MMS; 2 and 3: groups exposed to Malathion, for 0.5 e 1.0 mg mL-1 concentrations, respectively.

opencc-by-4.0Jan 2023View details →

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Figure 2 in Integrative analysis in toxicological assessment of the insecticide Malathion in Allium cepa L. system

Figure 2. Mitotic index at the radicular meristematic region of Allium cepa cells, after exposure for 48 hours to distilled water (H Od – negative control), 0.5 mg mL-1, 1.0 mg mL-1 of Malathion 2 and methyl methanesulfonate (MMS – positive control). KW-H = results of Kruskal-Wallis test and p = value of the statistical probability. Letters on the error bars indicate the result of the statistical Mann-Whitney U test.

opencc-by-4.0Jan 2023View details →

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Supplementary Material for "Can ZooMS help assess species abundance in highly fragmented bone assemblages? Integrating morphological and proteomic identifications for the calculation of an adjusted ZooMS-eNISP"

Supplementary Material for the article "Can ZooMS help assess species abundance in highly fragmented bone assemblages? Integrating morphological and proteomic identifications for the calculation of an adjusted ZooMS-eNISP" by Discamps et al., published in Palaeoanthropology. SI#1 Cassenade dataset (morphological and ZooMS identifications, sizes, masses, etc.) in RDS format. SI#2 Cassenade dataset (morphological and ZooMS identifications, sizes, masses, etc.) in CSV format. SI#3 R script used for making the figures and statistical tests

opencc-by-4.0Jun 2024View details →

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Fig. 6 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas

Fig. 6. DNA damage scores (mean ± SEM, n = 8) in erythrocytes of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P <0.05).

opencc-by-4.0Mar 2014View details →

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Fig. 2 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas

Fig. 2. Activity (mean ± SEM, n = 8) of glutathione S-transferase in liver (A) and gills (B) of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P <0.05).

opencc-by-4.0Mar 2014View details →

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Fig. 3 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas

Fig. 3. Activity (mean ± SEM, n = 8) of catalase in liver (A) and gills (B) of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P <0.05).

opencc-by-4.0Mar 2014View details →

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Fig. 4 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas

Fig. 4. Content (mean ± SEM, n = 8) of glutathione in liver (A) and gills (B) of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P<0.05).

opencc-by-4.0Mar 2014View details →

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Towards suitable practices for the integration of social life cycle assessment into the ecodesign framework of hydrogen-related products

The hydrogen sector is envisaged as one of the key enablers of the energy transition that the European Union is facing to accomplish its decarbonization targets. However, regarding the technologies that enable the deployment of a hydrogen economy, a growing concern exists about potential burden-shifting across sustainability dimensions. In this sense, social life cycle assessment arises as a promising methodology to evaluate the social implications of hydrogen technologies along their supply chains. In the context of the European projects eGHOST and SH2E, this study seeks to advance on key methodological aspects of social life cycle assessment when it comes to guiding the ecodesign of two relevant hydrogen-related products: a 5 kW solid oxide electrolysis cell stack for hydrogen production, and a 48 kW proton-exchange membrane fuel cell stack for mobility applications. Based on the social life cycle assessment results for both case studies under alternative approaches, the definition of a product-specific supply chain, making use of appropriate cut-off criteria, was found to be the preferable choice when addressing system boundaries definition. Moreover, performing calculations according to the activity variable approach was found to provide valuable results in terms of social hotspots identification to support subsequent decision-making processes on ecodesign, while the direct calculation approach is foreseen as a complement to ease the interpretation of social scores. It is concluded that advancements in the formalization of such suitable practices could foster the integration of social metrics into the sustainable-by-design framework of hydrogen-related products.

opencc-by-4.0Feb 2024View details →

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nmatthews323/SustAssessR: An integrated codebase for sustainability assessment using R

An integrated codebase for sustainability assessment using R This codebase was used for and is made available alongside the following publication: Matthews, N. E., Cizauskas, C. A., Layton, D. S., Stamford, L., & Shapira, P. (2019). Collaborating constructively for sustainable biotechnology. Scientific Reports. <a href="https://doi.org/10.1038/s41598-019-54331-7">https://doi.org/10.1038/s41598-019-54331-7</a> A note on input data This repository is designed to demonstrate and make available the code used for the above publication. Due to the proprietary nature of some of the process flow modelling the data in the directory "Data/ProcessedFlows" has been averaged and rounded to three significant figures. Therefore, while this codebase will authentically replicate the data processing carried out in the published analysis, the outputs will not be identical due to the changes made to the input data. All output data from the original publication has been made available through the supplementary information of the original publication.

opengpl-2.0Dec 2019View details →

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An Online Integrated Development Environment for Automated Programming Assessment Systems Open Source Data

This dataset accompanies the paper "An Online Integrated Development Environment for Automated Programming Assessment Systems". It contains data from the usability evaluation of a feature-rich online IDE designed for integration into Automated Programming Assessment Systems (APASs). The dataset includes survey responses from 27 participants based on the Technology Acceptance Model (TAM), performance metrics such as memory usage, and qualitative user feedback. The study highlights challenges in integrating online IDEs with APASs, such as memory efficiency, load balancing, and user experience. The dataset supports further research in developing scalable, effective, and user-friendly programming education tools. Here you can find the code changes required for the online IDE in Artemis: <a href="https://github.com/ls1intum/Artemis/pull/6706/files" target="_blank" rel="noopener">Github</a>

opencc-by-4.0Oct 2024View details →

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Model fields supporting the publication "Integrated Assessment of the Risks to Ocean Acidification in the Northern High Latitudes: Regional Comparison of Exposure, Sensitivity and Adaptive Capacity of Pelagic Calcifiers"

These are the model outputs supporting the described manuscript. They include monthly averaged output of aragonite saturation state for each year during the 10-year hindcast. Also included is the particle tracking output, for both the Bering Sea and the Gulf of Alaska, as described in the manuscript.

opencc-by-4.0Jul 2021View details →

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Penothypic integration: assessing the value of study replication

This repository contains the databases and scripts used to analyze the phenotypic integration of different species, populations, and sexes and assess which structural paths were generally (vs. conditionally) supported (vs. unsupported). These data were used in the following study: Irene Gaona-Gordillo, Benedikt Holtmann, Alexia Mouchet, Alexander Hutfluss, Alfredo Sánchez-Tójar, and Niels J. Dingemanse. Unpublished manuscript. Are animal personality, body condition, physiology, and structural size integrated? A comparison of species, populations, and sexes, and the value of study replication. J Anim Ecol. For any further information, please contact:  Irene Gaona-Gordillo, email: gaona-gordillo@bio.lmu.de Niels Dingemanse, email: n.dingemanse@lmu.de

opencc-by-4.0May 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record