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Mitigation of urbanisation effects on aquatic ecosystems by synchronous ecological restoration
<p>Ecosystem degradation and biodiversity loss have been caused by economic booms in developing countries over recent decades. In response, ecosystem restoration projects have been advanced in some countries but the effectiveness of different approaches and indicators at large spatio-temporal scales (i.e., whole catchments) remains poorly understood. Our datasets with a diverse array of 440 aquatic restoration projects including wastewater treatment, constructed wetlands, plant/algae salvage, and dredging of contaminated sediments implemented and maintained from 2007 to 2017 across more than 2000km2 of the northwest Taihu basin (Yixing, China). Synchronized investigations of water quality and invertebrate communities were conducted before and after restoration. Our datasets showed that even though there was rapid urbanization at this time, nutrient concentrations (NH<sub>4</sub><sup>+</sup>-N, TN, TP) and biological indices of benthic invertebrates (taxonomic richness, Shannon diversity, sensitive taxon density) improved significantly across most of the study area. Improvements were associated with the type of restoration project, with projects targeting pollution sources leading to the clearest ecosystem responses compared with those remediating pollution sinks. However, in some locations, the recovery of biotic communities appears to lag behind nutrients (e.g. nitrogen and phosphorus), likely reflecting long-distance re-colonization routes for invertebrates given the level of pre-restoration degradation of the catchment.</p>
Effective seed sterilization methods require optimization across maize genotypes
<p>Studies of plant-microbe interactions using synthetic microbial communities (SynComs) often require the removal of seed-associated microbes by seed sterilization before inoculation to provide gnotobiotic growth conditions. A diversity of seed sterilization protocols have been developed in the past and have been used on different plant species with various amounts of validation. From these studies, it has become clear that each plant species requires its own optimized sterilization protocol. It has, however, so far not been tested if the same protocol works equally well for different varieties and seed sources of one plant species. We evaluated six seed sterilization protocols on two different varieties (Sugar Bun & B73) of maize. All unsterilized maize seeds showed fungal growth upon germination on filter paper, highlighting the need for a sterilization protocol. A short sterilization protocol with hypochlorite and ethanol was sufficient to prevent fungal growth on Sugar Bun germinants, however, a longer protocol with heat treatment and germination in fungicide was needed to obtain clean B73 germinants. This difference may have arisen from the effect of either genotype or seed source. We then tested the protocol that performed best for B73 on three additional maize genotypes from four sources. Seed germination rates and fungal contamination levels varied widely by genotype and geographic source of seeds. Our study shows that consideration of both variety and seed source is important when optimizing sterilization protocols and highlights the importance of including seed source information in plant-microbe interaction studies that use sterilized seeds.</p>
Data for Hogan et al. 2024: "Basal bark herbicide treatment of Lonicera maackii (Amur honeysuckle) is effective regardless of application timing, with limited nontarget effects on native plant diversity"
<p>Data to accompany the following publication, currently in press:</p> <p>Hogan, K.F.E., K.Baker, E.M. Bach, N.A. Barber. Basal bark herbicide treatment of Lonicera maackii (Amur honeysuckle) is e ective regardless of application timing, with limited nontarget e ects on native plant diversity. In press, Ecological Solutions and Evidence</p> <p> </p> <p>Paper abstract: </p> <p><span>Managers tasked with controlling invasive species require effective methods that are quick and easy to use without inflicting extensive nontarget damage, while also being compatible with other scheduled management responsibilities. <em>Lonicera maackii</em> (Amur honeysuckle) is a non-native shrub that has invaded eastern and midwestern North American deciduous forests, altering ecosystem function and reducing biodiversity. </span><span>This study explores prescribed fire and seasonal basal applications of triclopyr ester as control methods and examines the extent of nontarget damage. We used paired-split plots to implement basal bark treatments in different seasons within burned and unburned units, and we tracked individual<em> L. maackii</em> to determine mortality and hyperlocal impacts of management. </span><span>Basal bark treatments killed 98.4% of <em>L. maackii</em> across seasonal timings. Nontarget plant cover immediately declined similarly for all herbicide application seasons, but the early- and late-spring treatments showing signs of recovery within four years. Meanwhile, species richness showed biologically small but statistically different declines across all treatment times. Prescribed fire did not impact <em>L. maackii </em>mortality or interact with herbicide efficacy. </span><span>Basal bark applications of triclopyr are an effective means of <em>L. maackii</em> control regardless of application timing, which allows managers to implement it at their convenience to avoid interfering with other management tasks that have time constraints.</span></p>
Data from: Sorting states of environmental DNA: Effects of isolation method and water matrix on recovery of membrane-bound, dissolved, and adsorbed states of eDNA
<p>Environmental DNA (eDNA) once shed can exist in numerous states with varying behaviors including degradation rates and transport potential. In this study we consider three states of eDNA: 1) a membrane-bound state referring to DNA enveloped in a cellular or organellar membrane, 2) a dissolved state defined as the extracellular DNA molecule in the environment without any interaction with other particles, and 3) an adsorbed state defined as extracellular DNA adsorbed to a particle surface in the environment. Capturing, isolating, and analyzing a target state of eDNA provides utility for better interpretation of eDNA degradation rates and transport potential. While methods for separating different states of DNA have been developed, they remain poorly evaluated due to the lack of state-controlled experimentation. We evaluated the methods for separating states of eDNA from a single sample by spiking DNA from three different species to represent the three states of eDNA as state-specific controls. We used chicken DNA to represent the dissolved state, cultured mouse cells for the membrane-bound state, and salmon DNA adsorbed to clay particles as the adsorbed state. We performed the separation in three water matrices, two environmental and one synthetic, spiked with the three eDNA states. The membrane-bound state was the only state that was isolated with minimal contamination from non-target states. The membrane-bound state also had the highest recovery (54.11 ± 19.24 %), followed by the adsorbed state (5.08 ± 2.28 %), and the dissolved state had the lowest total recovery (2.21 ± 2.36 %). This study highlights the potential to sort the states of eDNA from a single sample and independently analyze them for more informed biodiversity assessments. However, further method development is needed to improve recovery and reduce cross-contamination.</p>
Figure 4 in Effects of temperature and duration of storage on the hatching behaviour of Heterodera latipons (Nematoda: Heteroderidae)
Figure 4. Effect of storage temperature at 20 °C, for 8 prior incubations at 10 and 15 °C for 12 weeks, on the cumulative hatching of H. latipons populations. Cumulative percentages of hatched populations. Vertical bars indicate the standard deviation of the means.
Figure 3 in Effects of temperature and duration of storage on the hatching behaviour of Heterodera latipons (Nematoda: Heteroderidae)
Figure 3. Effect of storage temperature at 5 °C, for 8 prior incubations at 10 and 15 °C for 12 weeks, on the cumulative hatching of H. latipons populations. Cumulative percentages of hatched populations. Vertical bars indicate the standard deviation of the means.
Figure 2 in Effects of temperature and duration of storage on the hatching behaviour of Heterodera latipons (Nematoda: Heteroderidae)
Figure 2. Effect of various temperatures on the emergence of J2s (J2s) of H. latiponspopulations. Cumulative percentages of hatched J2s followed by the same letter were not significantly different according to the Tukey HSD test at P ≤ 0.05. Vertical bars indicate standard deviation of the means.
Figure 1 in Effects of temperature and duration of storage on the hatching behaviour of Heterodera latipons (Nematoda: Heteroderidae)
Figure 1. Neighbour-joining tree constructed based on the ITS sequence alignment of the H. latipons populations from this study and corresponding populations representing species from GenBank. Numbers on the branches represent bootstrap values obtained from 1000 bootstrap replications.
(U)SAXS data (ID02 beamline, ESRF): Effects of pH on the fibrous structure formation of plant proteins during high-moisture extrusion
<p>Due to health and environmental factors, the food industry is looking for ways to introduce meat replacers made from plant-based proteins to consumer markets. The presence of structural anisotropy in the form of fibre is a prerequisite for meat analogues. Structure formation ability depends on the protein ingredients used, which leads to plant protein products with varying texture hardness and extent of fibre alignment. In the current study, we will test if it is possible to tune these properties based on the hypothesis that plant proteins have different structure formation abilities under varying pH conditions.</p>
Data from: Effect of ambient conditions in friction surfacing
<p>This dataset contains the data for the publication "Effect of ambient conditions in friction surfacing".</p>
AdaptFerm: Bioprocess Monitoring Using FTIR spectroscopy: Insights into Substrate Effects and Domain Adaptation
<h2> </h2> <h2><strong>1. Introduction</strong></h2> <p>The AdaptFerm dataset is designed to support the development of a monitoring framework for lactic acid production fermentation using Fourier Transform Infrared (FTIR) spectroscopy. Its primary goal is to facilitate the control strategies for continuous fermentation processes to maximize the lactic acid production. The AdaptFerm encompasses data from two distinct batch fermentation environments: one employing simple sugar (glucose) as the substrate and the other utilizing complex sugars derived from bio-waste. The study focuses on developing accurate predictive models for glucose and lactic acid concentrations, with an emphasis on applying classical machine learning techniques and enhancing domain generalization capabilities.</p> <h2><strong>2. Prediction Model for Different Substrate Environments</strong></h2> <p>The chemical composition of substrates are presented in <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Table 1 [1]</a>. The dataset is utilized to train and test models within the same substrate domain. For instance, data from a single fermentation environment (e.g., glucose substrate) is used for both training and testing phases. The applied machine learning models showed accurate prediction within the same domain <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">[1]</a>. For more details on the methods applied, please refer to the following link: <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">https://doi.org/10.1016/j.heliyon.2024.e38791</a>. In this study, the MIR results correspond to the AdaptFerm dataset. The spectra of the glucose and biowaste hydrolysate fermentation process are presented in <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Figure 3</a> and <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Figure 4</a>.</p> <h2><strong>3. Domain Adaptation</strong></h2> <p>The dataset was also used to address the challenge posed by shifts in FTIR data when substrates change. Transitioning from simple sugar (glucose) to complex sugar (bio-waste) causes significant variations in the FTIR spectra, making it difficult for models trained on glucose fermentation data to maintain prediction accuracy in the complex sugar fermentation environment. This results in reduced robustness and performance when applied to out-of-distribution data. To address these challenges, we explore methods that improve the generalization ability and robustness of models in such scenarios without using labels from complex sugar fermentation <a title="Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation" href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">[2]</a>. It shows the application of machine learning interpretation to find domain invariant features for glucose and lactic acid. For more details on the methods applied, please refer to the following link: <a title="Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation" href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">https://dx.doi.org/10.2139/ssrn.5012080</a>. The code is available at <a title="ShapFS" href="https://github.com/shl-shawn/ShapFS" target="_blank" rel="noopener">https://github.com/shl-shawn/ShapFS</a>.</p> <h2><strong>4. Real-World Use Cases</strong></h2> <h3><strong>4.1. Regression Task</strong></h3> <p>AdaptFerm serves as a benchmark for machine learning model applications in fermentation processes, specifically for predicting glucose and lactic acid concentrations, measured in g/L (grams per liter), while considering issues of out-of-distribution generalization.</p> <h3><strong>4.2. Domain Adaptation Regression Task</strong></h3> <p>The dataset is also suitable for evaluating different domain adaptation methods. In particular, the glucose substrate fermentation data can be used as the source domain, while the complex sugar fermentation data from bio-waste serves as the target domain. For semi-supervised domain adaptation approaches, it is recommended to use the initial data points (i.e., those collected at the beginning of the fermentation process) from the target domain, as the dataset is organized chronologically by collection day. These approaches aim to improve the robustness of models by transferring knowledge across domains and mitigating the effects of out-of-distribution data.</p> <h3><strong>4.3. Anomaly Detection</strong></h3> <p>The dataset can be used to train anomaly detection models to identify outliers or deviations from normal fermentation behavior. This could be valuable in industrial bioprocessing, where early detection of issues like contamination or process failure is crucial. Techniques like Isolation Forests, One-Class SVM, or Autoencoders could be applied to identify unusual patterns in FTIR spectra.</p> <h3><strong>4.4. Classification Task</strong></h3> <p>Although the main task is regression, the dataset could also be used in classification tasks by discretizing the concentrations of glucose and lactic acid into categories (e.g., low, medium, high). This would allow for the application of classification algorithms like Support Vector Machines (SVM), Random Forests, or Neural Networks for predicting the fermentation phase or identifying specific operational conditions.</p> <h3><strong>4.5. Transfer Learning</strong></h3> <p>Given the nature of the domain adaptation approach in this dataset, transfer learning models can be explored. Models pre-trained on glucose fermentation data can be fine-tuned on complex sugar fermentation data, enabling quicker model convergence and improved performance in data-scarce environments.</p> <h3><strong>4.6. Multi-Task Learning</strong></h3> <p>In a multi-task learning scenario, models could simultaneously predict both glucose and lactic acid concentrations from the same FTIR data. This could help in improving model accuracy by leveraging shared representations across the two tasks.</p> <h3><strong>4.7. Feature Selection</strong></h3> <p>The FTIR spectral data contains a large number of features (wavelengths), and feature selection techniques such as Recursive Feature Elimination (RFE), Lasso regression, or mutual information could be applied to identify the most relevant wavelengths for predicting glucose and lactic acid concentrations, improving model performance and interpretability.</p> <h2><strong>5. Dataset Structure and Meta Information</strong></h2> <p>The dataset is organized into four Excel files, corresponding to two main fermentation domains (different substrates) and two key process variables:</p> <p><strong>a) Simple Sugar Substrate</strong><br>This domain contains data for the fermentation process using glucose as the substrate to produce lactic acid. It includes two files—one for glucose concentrations and one for lactic acid concentrations. Both are measured in g/L.</p> <p><strong>b) Complex Sugar Substrate</strong><br>This doman contains data for the fermentation process using bio-waste as the substrate to produce lactic acid. Similar to the previous domain, it includes two files—one for glucose concentrations and one for lactic acid concentrations. Both are measured in g/L.</p> <p>Each file is structured as follows:</p> <ul> <li>The first column contains the<strong> </strong>sample ID, which serves as the timeline of measurements (Sample ID 1 represents the first measurement in the fermentation process).</li> <li>From the second column onwards, the FTIR data is provided, covering the spectral range from 549.6 cm-1 to 3999.6 cm-1 comprising 3,579 features.</li> <li>The final column contains the ground truth data, the chemical measurements of fermentation variables such as glucose and lactic acid concentrations, both measured in g/L.</li> </ul> <h2><strong>6. Conclusion</strong></h2> <p>The AdaptFerm features FTIR spectra data from two distinct fermentation environments: simple sugar (glucose) and complex sugar (bio-waste). The dataset is designed to be used in regression tasks, including domain adaptation, and can be applied in machine learning model development for fermentation process monitoring, with a focus on enhancing model robustness and handling out-of-distribution data. This dataset provides a valuable resource for exploring<strong> </strong>domain shift and improving the robustness of machine learning models in bioengineering and fermentation processes. It enables further research into domain generalization techniques and offers a wide range of possibilities for machine learning applications.</p> <h2>References</h2> <p> [1] Arman Arefi, Barbara Sturm, Majharulislam Babor, Michael Horf, Thomas Hoffmann, Marina Höhne, Kathleen Friedrich, Linda Schroedter, Joachim Venus, Agata Olszewska-Widdrat, Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates, Heliyon, Volume 10, Issue 19, 2024, e38791, ISSN 2405-8440, DOI: 10.1016/j.heliyon.2024.e38791, <a href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">https://doi.org/10.1016/j.heliyon.2024.e38791</a>.</p> <p>[2] Majharulislam Babor, Shanghua Liu, Arman Arefi, Agata Olszewska-Widdrat, Barbara Sturm, Joachim Venus, and Marina M.-C. Höhne, Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation. Available at <a href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">http://dx.doi.org/10.2139/ssrn.5012080.</a></p>
Seasonal orographic effect of North American Mountain Range at different levels and its remote control on tropical climate
<p><a name="OLE_LINK36"></a><a name="OLE_LINK37"></a><a name="OLE_LINK81"></a><a name="OLE_LINK6"></a><span><span><span><span>Orography significantly influences global climate patterns. </span></span></span></span><a name="OLE_LINK32"></a><a name="OLE_LINK33"></a><span><span><span><span><span><span>Previous studies show the North American Mountain Range (NAMR) impacts regional climates seasonally but have not thoroughly illustrated the seasonally different atmospheric responses in the lower and upper troposphere, respectively. </span></span></span></span></span></span><span><span><span><span><span>Using the Community Earth System Model version 1.2 with a slab ocean configuration, we investigate the NAMR’s seasonal impacts by simulating scenarios with and without the mountain range. Our findings reveal that the NAMR induces contrasting responses in sea surface temperature (SST) and precipitation off California in different seasons, indicating different underlying mechanisms. Through analysis of large-scale circulation and local energy budgets, we find that in summer, the NAMR reinforces the North Pacific High causing SST cooling and drying off California. This cooling propagates to the equatorial Pacific via anomalous northeasterlies, influencing the Intertropical Convergence Zone and initiating a climatic signal through the Pacific Meridional Mode, which crosses the equator and affects Southern Hemisphere temperatures. In winter, the NAMR reduces wind speed and evaporation, leading to SST warming off California, amplified by SST-cloud feedback. In the upper troposphere, we observe seasonal shifts in jet stream patterns: during winter, a weakened, equatorward-shifted jet over the Pacific and a strengthened, poleward-shifted branch over the Atlantic; in summer, the jet stream intensifies over and downstream of the mountains while weakening upstream. Our research highlights distinct seasonal mechanisms by which the NAMR influence climate patterns, linking mid-latitude climate variations to equatorial, cross-hemispheric and global changes.</span></span></span></span></span></p>
On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection
<p>Additional resources for the paper:</p> <h3><strong><a href="https://ieeexplore.ieee.org/abstract/document/10826088">On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection.</a></strong></h3> <p>Lewis, N., Cavalcante, C. C., Boukouvalas, Z., & Corizzo, R.</p> <p><em>2024 IEEE International Conference on Big Data (BigData)</em> (pp. 3277-3281). IEEE.</p> <pre> </pre> <p> </p> <p>MMHS150K [1] is a manually labeled multimodal dataset that contains $150000$ tweets with two modalities: text, and corresponding image. Tweets are collected from September 2018 until February 2019 and are labeled according to different types of hate speech: no attacks to any community, racist, sexist, homophobic, religion-based attacks, or attacks to other communities. </p> <p>We extract vector embeddings leveraging different text (BERT, OpenAI) and image (ResNet, PVT, ViT) modele backbones and assess their effectiveness in the hate speech detection task.</p> <p> </p> <h2>Citation:</h2> <pre>@inproceedings{lewis2024effectiveness, title={On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection}, author={Lewis, Nora and Cavalcante, Charles C and Boukouvalas, Zois and Corizzo, Roberto}, booktitle={2024 IEEE International Conference on Big Data (BigData)}, pages={3277--3281}, year={2024}, organization={IEEE} }</pre>
Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks
<p>This dataset comes from the following paper:</p> <p>Matteo Arricca, Nicoletta Inverardi, Stefano Pandini, Maurizio Toselli, Massimo Messori, Giulia Scalet, Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks, Journal of the Mechanics and Physics of Solids, 105955, 2024. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmps.2024.105955" target="_blank" rel="noopener"><span><span>https://doi.org/10.1016/j.jmps.2024.105955</span></span></a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. txt experimental data</li> </ul>
Supplementary data to "The effects of small-scale heterogeneity on biomonitoring of desmid phytobenthos in Central European temperate mountain peatlands"
<p>The supplementary data consist of the files including the species-in-samples data and their associated NCV scores used for the analyses described in the manuscript submitted to hydrobiologia. In addition, two R scripts used for the analyses are included, too.</p> <p> </p>
Measured and analyzed raw data for publication "Nanoscale spin ordering and spin screening effects in tunnel ferromagnetic Josephson junctions" (doi: https://doi.org/10.1038/s43246-024-00497-1)
<p>The data provided by this dataset are the raw data published in the paper "Nanoscale spin ordering and spin screening effects in tunnel ferromagnetic Josephson junctions" (doi: https://www.nature.com/articles/s43246-024-00497-1). </p> <p>It can be found:</p> <p>-In the folder figure2_IV, the current-voltage characteristics (IV) of standard Superconductor-Insulator-Superconductor Josephson Junctions (SIS JJ) and of Superconductor-Insulator-Ferromagnet-thin superconductor- Superconductor Josephson Junctions (SIsFS JJ) at 10 mK </p> <p>-In the folder figure2_IVH, the magnetic dependence of the critical current of the SIsS and SIsFS at 10 mK</p> <p>-In the folder figure3_IVHT, the magnetic dependence of the critical current of the SIsFS as a function of the temperature T</p> <p>-In the figure4_gamma, the experimental and theoretical dependence of \gamma, i.e., the magnetic moment of the S-layers normalized to the F-layer in absolute value, as a function of the characteristic energy of the inverse proximity effect</p>
Evaluation of high-resolution WRF simulation in urban areas - Effect of different physics schemes on simulation performance in the Rhine-Main-Neckar area
<p>This dataset contains data sampled from a WRF sensitivity study saved in NetCDF format. The study was run over 4 months of the year 2020. The folders contain the following data:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Datasets</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>wrf_met_sample_full.nc</td> <td>all</td> <td>WRF meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>wrf_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>wrf_met_sample_full_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_met_sample_full_and_quant_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full_and_quant.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_pblh_sample_full.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain</td> </tr> <tr> <td>wrf_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_met_sample_full.nc</td> <td>all</td> <td>ERA5 meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>era5_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain, resampled onto the measured meteorology and with summary statistics</td> </tr> </tbody> </table> <p>Each of these files contains the samples and statistics as NetCDF Variables. These Variables have multiple dimensions, which describe the individual datapoints. For the WRF samples, these dimensions are:</p> <table> <tbody> <tr> <td><strong>Dimension</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Time</td> <td>time since start of simulation</td> </tr> <tr> <td>station_id</td> <td>the ID of the station where the sample was taken (meteo - length 19, PBLH - length 2)</td> </tr> <tr> <td>pbl</td> <td>Planetary Boundary Layer scheme (Bou-Lac / MYJ / YSU)</td> </tr> <tr> <td>lsm</td> <td>Land Surface Model scheme (N / NMP)</td> </tr> <tr> <td>slm</td> <td>Surface Layer Model scheme (MM5 / MO)</td> </tr> <tr> <td>urb</td> <td>Urban Parametrization scheme (SLUCM / BEP)</td> </tr> </tbody> </table> <p>Not all combinations between different simulation schemes exist, so some values in the NetCDF Variables are NaNs.</p>
Data and figure reproduction for paper titled 'Lateral quantum-confined Stark effect for integrated quantum dot electroabsorption modulators'
<p>The dataset contains the data behind, and instructions to reproduce, the figures in the paper titled 'Lateral quantum-confined Stark effect for integrated quantum dot electroabsorption modulators' written by the named dataset creators.</p>
Dataset of the publication: Effect of Nonconstituent Additive Ions on the Controlled Crystallization of Lanthanide-Based Preyssler Polyoxometalates
<p>Dataset of the publication: Effect of Nonconstituent Additive Ions on the Controlled Crystallization of Lanthanide-Based Preyssler Polyoxometalates</p> <p>DOI: 10.1021/acs.cgd.3c00046</p> <p>Gómez-Muñoz, I; Dey, C; Coronado, E</p> <p>Cryst. Growth Des. 2023, 23, 5, 3544–3548</p>
Chemical Effects Induced by Relativistic Precipitating Electrons
<p>Figures, data, and code used in my paper describing "Chemical Effects Induced by Relativistic Precipitating Electrons"</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.