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302 results for “Microplastics”
Laboratory study on microplastic fiber size and concentration effects on leopard frog (Lithobates pipiens) tadpole survival, development, behavior, and parasite susceptibility
This dataset contains comprehensive raw data from a completed laboratory experiment conducted from May 24 to June 30, 2021 (with additional analysis performed in 2025), investigating the effects of polyester microplastic (MP) fiber exposure on northern leopard frog (Lithobates pipiens) tadpoles and their interactions with echinostome trematodes (Echinostoma sp.). Tadpole egg masses were collected from a wetland in Indiana, USA, and ramshorn snails (Helisoma trivolvis), serving as trematode hosts, were collected from Tioga County, New York, USA. The experiment was conducted under controlled laboratory conditions using a static-renewal design, exposing tadpoles to short (~0.24 mm) or long (~1.50 mm) polyester MP fibers at concentrations of 0, 10, or 40 µg L⁻¹ for 32 days, followed by controlled exposure to echinostome cercariae. The dataset includes measurements of tadpole mortality, developmental traits (mass, snout-to-vent length, Gosner stage), behavioral activity (number of moving pre- and post-parasite exposure), MP fiber ingestion, and susceptibility to trematode infection (metacercarial cyst counts in kidneys). These data provide a resource for studying the ecological and toxicological impacts of microplastics on amphibian health, and host-parasite dynamics in freshwater ecosystems, making the dataset suitable for researchers in ecotoxicology, and disease ecology. The dataset is complete, with no ongoing data collection, and is designed to support analyses of microplastic-mediated effects on aquatic organisms.
Impacts of microplastics on wetland ecosystem dynamics: a mesocosm study of trophic interactions and community responses, 2021-2025
This dataset documents a mesocosm experiment conducted to evaluate the ecological impacts of microplastics (MP) on wetland communities representative of eastern USA wetlands. The study focused on key organisms across trophic levels, including tadpoles (Lithobates pipiens), snails (Helisoma trivolvis), zooplankton (Daphnia pulex), phytoplankton, and periphyton communities, to assess the effects of three microplastic types (low-density polyethylene–LDPE, medium-density polystyrene–PS, and high-density polyester–PES) at two concentrations (1 mg/L and 5 mg/L), alongside a no-microplastic control. Experimental units consisted of 70 mesocosms (19-L buckets) with 10 replicates per treatment, established between July 1–15, 2021, at Binghamton University’s Ecological Research Facility. Response variables included survival and developmental traits (mass, snout-vent length, shell width) of tadpoles and snails, microplastic ingestion, zooplankton abundance, phytoplankton biomass (chlorophyll and phycocyanin concentrations), and periphyton mass. The dataset provides comprehensive measurements of community responses and water quality parameters, offering insights into the ecological consequences of microplastic pollution in wetland ecosystems. This dataset is suitable for researchers studying ecotoxicology, wetland ecology, and the impacts of anthropogenic pollutants on aquatic food webs.
Microplastic Abundance, Shape, and Color in Passerines Captured at Rushton Woods Preserve Bird Banding Station in Newtown Square, Pennsylvania, USA, April-September 2024
Fecal samples were collected from 5 species of passerine birds between April and September 2024 at the Rushton Woods Preserve Bird Banding Station. Samples were chemically digested and filtered for the purpose of extracting, quantifying, and describing microplastics.
Microplastics' Characteristics for Water Samples
<p>The characteristics of microplastics by the abundance, color, and size are shown for water samples collected in plastic bottles by VLPF, PONIKVE, THAMES 21, UNIPER, and DOW COMPANY Partners, on June 2022, as part of In-No-Plastic Project. Three replicates were collected at each sampling site. Water samples were filtrated by stacked stainless steel sieves with 4 mm and 32 μm mesh sizes. After the organic matter was destroyed, the microplastics were observed by using an optical microscope.</p>
Data for article: Microplastics in Arctic Waters of the Finnish Sámi Area
<p><strong>Description of the dataset:</strong></p> <p>This dataset was created in 2021 and 2023. It was first made open in 2023 and later updated in 2024. </p> <p>Lapland_Sampling.xlsx contains all sampling dates and coordinates alongside with sample names, volumes, area descriptions and codes used to describe the samples in the article. </p> <p>Lapland_RawData.xlsx contains all the raw data for all the samples. This includes polymer types, particle sizes and an estimation of particle mass. Furthermore, the file contains a table with all the polymer types and their counts from all samples. </p> <p> </p> <p><strong>Data creation and processing:</strong></p> <p>The raw data was created by measuring filtered water samples with FPA-FTIR . The spectral analysis was done with siMPle (Primpke et al. 2020, <em>Applied Spectroscopy</em>, 74, 1127-1138, <a href="https://doi.org/10.1177/0003702820917760">https://doi.org/10.1177/0003702820917760</a>).</p> <p> </p>
Dataset for journal article: Nava et al. (2021) "Microalgae colonization of different microplastic polymers in experimental mesocosms across an environmental gradient"
<p>Dataset and R script for the article "Microalgae colonization of different microplastic polymers in experimental mesocosms across an environmental gradient" by Nava V., Matias M., Castillo-Escrivà A., Messyasz B. and Leoni B. accepted by Global Change Biology.</p>
Mud and organic content are strongly correlated with microplastic contamination in a meandering riverbed - Data sets
<p>This is the dataset relating to publication "Microplastics distribution in a meandering riverbed reveasl mud content as a universal normalizer for microplastic contamination in aquatic environments" by Van Daele, M., Van Bastelaere, B., de Clercq, J., Meyer, I., Vercauteren, M. and Asselman, J.</p> <p>It contains the microplastic and sedimentological data that support the findings of that publication, with a seperate file for data obtained from riberbed sediments and the water column. It further contains a file with all source data for the graphs in the figures of the main manuscript and the Supplementary Information<span><span>.</span></span></p>
Data and supplementary files of the publication: "What Goes Around Should Not Move Around: Immobilizing Microplastics as a New Approach for Analytical Ring Trials"
<p>Accompanying materials for the publication: <a href="https://doi.org/10.1021/acs.est.4c09427">https://doi.org/10.1021/acs.est.4c09427</a></p>
Data from: Microplastic and organic carbon storage in sediments of intertidal and subtidal seagrass meadows
<p>This datasets support the scientific article (submitted) " Microplastic and organic carbon storage in sediments of intertidal and subtidal seagrass meadows." It contains detailed data on sedimentary organic carbon content and the abundance of microplastics in both intertidal and subtidal seagrass meadows within the Ria Formosa lagoon (Southern Portugal). The datasets are accompanied by analysis code, available at GitHub repository, allowing for reproducibility and further exploration of the data.</p> <p>The data is composed by 4 datasets with the following variables:</p> <p><strong>data_cores.csv. </strong>Contains properties related to the sampling of the sediment cores.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>species [character] - species of the seagrass meadow.</li> <li>replicate [character] - replicate number of the core in each seagrass meadow.</li> <li>core_depth [numeric] - depth sampled with the core (in centimeters).</li> <li>sample_length [numeric] - length of the sampled core measured in the laboratory (in centimeters).</li> <li>compaction_factor [numeric] - fraction of the sample depth interval reduced due to compaction. It is calculated by dividing the core length by the core depth.</li> <li>compaction_perc [numeric] - core compaction in percentage (%). It is calculated as 100*(1 - compaction_factor).</li> </ul> <p><br><strong>data_samples.csv. </strong>Contains properties of the sediment samples.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>depth_middle [numeric] - middle depth of a sampling increment, calculating as the average of depth_min and depth_max (in centimeters).</li> <li>depth_min [numeric] - minimum depth of a sampling increment, corrected for compaction (in centimeters).</li> <li>depth_max [numeric] - maximum depth of a sampling increment, corrected for compaction (in centimeters).</li> <li>sample_volume [numeric] - volume of the sediment sample, corrected for compaction (in cubic centimeters).</li> <li>sample_dw [numeric] - dry mass of the sample (in grams of dry weight).</li> <li>percentage_organic_matter [numeric] - mass of organic matter relative to sample dry mass, obtained by loss-on-ignition (in percentage of dry weight).</li> <li>percentage_organic_carbon [numeric] - mass of organic carbon relative to sample dry mass, obtained by a local organic carbon to organic carbon ratio (as a percentage of dry weight).</li> <li>bag_id [character] - identification code for the aluminium envelope containing the sample.</li> <li>weight_sample_mp [numeric] - dry mass of the sample used for the microplastic extraction (in grams of dry weight).</li> <li>dry_bulk_density [numeric] - dry mass per unit volume of the sample. This is calculated as the sample_dw divided by the sample_dw (in grams of dry weight per cubic centimeter). </li> </ul> <p><br><strong>data_particles_visual.csv. </strong>Contains properties of the suspected microplastic particles found in the sediment samples and the negative controls, based on visual inspection.</p> <ul> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>species [character] - species of the seagrass meadow.</li> <li>habitat_label [character] - text for labelling purposes regarding the habitat.</li> <li>type [factor] - whether the particle comes from a sediment sample ("sediment") or a control sample ("control"). </li> <li>cycle [character] - cycle in which samples were analysed.</li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>bag_id [character] - identification code for the aluminium envelope containing the sample.</li> <li>filter_id [character] - identification code of the filter used for the particle extraction.</li> <li>filter_area [numeric] - area of the filter that was screened for microplastics (in fraction of total).</li> <li>visual_id [character] - unique particle identification code based on visual identification.</li> <li>colour [factor] - particle colour category: black_grey, blue_green, brown_tan, opaque, orange_pink_red, transparent, white_cream, yellow.</li> <li>shape [factor] - particle shape category: film, foam, fragment, line, pellet.</li> <li>major [numeric] - longest dimension of the particle, analysed in ImageJ (in micrometers).</li> <li>minor [numeric] - Longest dimension perpendicular to major, analysed in ImageJ (in micrometers).</li> </ul> <p><br><strong>data_particles_ftir.csv. </strong>Contains properties of the suspected microplastic particles found in the sediment samples and the negative controls, based on the FTIR analysis.</p> <ul> <li>species [character] - species of the seagrass meadow.</li> <li>habitat_label [character] - text for labelling purposes regarding the habitat.</li> <li>core_id [character] - unique core identification code used in the field.</li> <li>core_id_new [character] - unique core identification code used in the article.</li> <li>filter_id [character] - identification code of the filter used for the particle extraction.</li> <li>filter_area [numeric] - area of the filter that was screened for microplastics (in fraction of total).</li> <li>cycle [character] - cycle in which samples were analysed.</li> <li>type [factor] - whether the particle comes from a sediment sample ("sediment") or a control sample ("control"). </li> <li>sample_id [character] - unique sample identification code (obtained by concatenating the core id and the minimum non-corrected depth of the sample).</li> <li>num_ftir [numeric] - numerical order in which particles were identified within a filter.</li> <li>colour [factor] - particle colour category: black_grey, blue_green, brown_tan, opaque, orange_pink_red, transparent, white_cream, yellow.</li> <li>shape [factor] - particle shape category: film, foam, fragment, line, pellet.</li> <li>ref_analysis [boolean] - whether the reflection analysis was preformed or not.</li> <li>atr_analysis [boolean] - whether the ATR analysis was preformed or not.</li> <li>ftir_match_ref [character] - name of the polymer with the highest match found using µFTIR for reflection analysis.</li> <li>match_ref [numeric] - percentage of match corresponding to highest match for reflection analysis.</li> <li>ftir_match_atr [character] - name of the polymer with the highest match found using µFTIR for ATR analysis.</li> <li>match_atr [numeric] - percentage of match corresponding to highest match for ATR analysis.</li> <li>plastic_ref [boolean] - whether the particle is classified as having a plastic composition or not, based on the reflection analysis.</li> <li>polymer_group_ref [factor] - polymer group based on the reflection analysis: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> <li>plastic_atr [boolean] - whether the particle is classified as having a plastic composition or not, based on the ATR analysis.</li> <li>polymer_group_atr [factor] - polymer group based on the ATR analysis: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> <li>ftir_match_final [character] - final decision on the polymer composition, including the option "unclear".</li> <li>plastic_final [factor] - whether the particle is classified as having a plastic composition or not, based on final decision "ftir_match_final", includes categories: yes, no, unclear.</li> <li>final_analysis [character] - the analysis performed and used for the final decision, includes categories: ref (reflection analysis), atr (ATR analysis), both-but-atr-more-conclusive, both-but-ref-more-conclusive, both-unclear.</li> <li>polymer_group_final [character] - polymer group based final decision: Non-plastic, Nylon-polyamides, Poluacrylamides, Polyacrylates, Polyesters, Polyethylene, Polyglycols, Polyhaloolefins, Polymethylmethacrylate, Polypropylene, Polystyrene, Polyurethane, Polyvinylalcohol, Silicones, Others.</li> </ul>
Dataset: Correlative Light, Electron Microscopy and Raman Spectroscopy Workflow to Detect and Observe Microplastic Interactions with Whole Jellyfish
<p>ABSTRACT</p> <p>Many researchers have turned their attention to understanding microplastic interaction with marine fauna. Efforts are being made to monitor exposure pathways and concentrations, and to assess the impact such interactions may have. To answer these questions, it is important to select appropriate experimental parameters and analytical protocols. This study focuses on medusae of <em>Cassiopea andromeda</em> jellyfish: a unique benthic jellyfish known to favor (sub-)tropical coastal regions which are potentially exposed to plastic waste from land-based sources. Juvenile medusae were exposed to fluorescent poly(ethylene terephthalate) and polypropylene microplastics (< 300 µm), resin embedded, and sectioned before analysis with confocal laser scanning microscopy as well as transmission electron microscopy and Raman Spectroscopy. Results show the fluorescent microplastics were stable enough to be detected with the optimized analytical protocol presented, and that their observed interaction with medusae occurs in a manner which is likely driven by the microplastic properties (<em>e.g.</em> density, hydrophobicity).</p>
FLEXPART 10.4 output for "Occurrence and backtracking of microplastic mass loads including tire wear particles in Northern Atlantic air"
<p>The dataset consists of three (3) files:</p> <p>-- track3h.txt shows the position of the research vessel in each of the seven (7) ship tracks/campaigns in 3-hour resolution in ascii format structured in columns as follows:</p> <p>YEAR, MONTH, DAY, HOUR, MINUTE, SECOND, LONGITUDE, LATITUDE, SHIP TRACK NUMBER</p> <p>-- FLEXPART_720x360_fine.tar.gz shows the footprint emission sensitivities for fine particles (as described in the paper) in a gridded netCDF format of 0.5 degrees resolution for 80 release points matching the coordinates and times in the track3h.txt file.</p> <p>-- FLEXPART_720x360_coarse.tar.gz shows the footprint emission sensitivities for coarse particles (as described in the paper) in a gridded netCDF format of 0.5 degrees resolution for 80 release points matching the coordinates and times in the track3h.txt file.</p>
Microplastics in Como Creek, Green Lake 4, and Solifluction Lobes 2023.
Microplastics are known to persist in remote regions of the Colorado Front Range through atmospheric deposition. Although plastics are introduced by wet deposition, high rates of deposition are driven by dry deposition. With a warming climate, microplastic deposition may increase with unknown consequences for the ecological health of remote ecosystems. To assess the risk of microplastic distribution in remote regions, monitoring of environmental compartments is needed. To this end, we sampled three surface waters within the Niwot LTER for microplastics using a peristaltic in-line filtration sampling pump.
Data and code for the publication "Tracing the horizontal transport of microplastics on rough surfaces"
<p><strong>Background</strong></p> <p>The data set contains images of fluorescent PMMA (Polymethyl methacrylate) particles that are moved by water on rough surfaces in an irrigation experiment. The experiments were done in the laboratory at the Institute of Geography, University of Cologne, Germany, in Septembre 2020. The images were taken with an sCMOS (advanced scientific complementary metal-oxide-semiconductor) high resolution pco.panda 4.2 camera (PCO AG, Kehlheim, Germany).</p> <p>The data set was analysed in the publication: Laermanns, H., Lehmann, M., Klee, M., Löder, M.G.J., Gekle, S. and Bogner, C., 2021, “Tracing the horizontal transport of microplastics on rough surfaces,” Microplastics and Nanoplastics, <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a></p> <p>Additionally to the data, this collection of files contains the Python and R scripts/notebooks used to analyse the images and create graphics for the publication. The code for the simulation of flow patterns can be obtained from the authors upon request.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p>Experimental parameters</p> <ul> <li> <p>Surface roughness: two levels, fine and course</p> </li> <li> <p>Inclination: 6 levels, 2.5°, 5°, 7.5°, 10°, 12.5° and 15°</p> </li> <li> <p>Irrigation: three levels, 4.8, 7.2 and 10.44 L/h</p> </li> <li> <p>Repetitions: three</p> </li> </ul> <p>More details on the experimental setup are given in the publication.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The folder <strong>images.zip</strong> contains the images. They are organized as follows:</p> <ul> <li><strong>Feinsand_10_Partikel</strong>: images of PMMA particles on the fine surface</li> <li><strong>Grobsand_10_Partikel</strong>: images of PMMA particles on the rough surface <ul> <li> <p>Both folders contain six subfolders <strong>_XX_Grad_Gefaelle</strong>, XX being 2_5, 5, 7_5, 10, 12_5, 15. These folders refer to inclinations of 2.5°, 5°, 7.5°, 10°, 12.5° and 15° of the rough surfaces, respectively.</p> </li> <li> <p>every folder _XX_Grad_Gefaelle contains three subfolders <strong>Fliessgeschwindigkeit_YY</strong>, with YY being 20mlx4, 30mlx4 and 43_5mlx4, the parameters of the peristaltic pump, corresponding to irrigation rates of 4.8, 7.2 or 10.44 L/h, respectively.</p> </li> <li> <p>every folder Fliessgeschwindigkeit_YY contains three subfolders <strong>Z_Durchgang</strong> with Z being 1, 2 or 3 corresponding to the tree repetitions of the experiment.</p> </li> </ul> </li> <li><strong>stained_flow_patterns</strong>: images of flow patterns of the fluorescent dye Nile Red (in methanol), an mp4 video and a text file with parameters to produce the video based on the images. The images were produced for the following experimental parameters: <ul> <li> <p><strong>Feinsand_2_5_Grad_20_ml</strong>: fine surface, inclined by 2.5° and irrigated with 7.2 L/h</p> </li> <li> <p><strong>Grobsand_7_5_Grad_20_ml</strong>: coarse surface, inclined by 7.5° and irrigated with 7.2 L/h</p> </li> </ul> </li> </ul> <p>The file <strong>experimental_data.csv</strong> links the concatenated folder names to experimental parameters.</p> <p> </p> <p><strong>Description of the code</strong></p> <p>The images were first processed in Python to locate the PMMA particles and calculate particle sizes. The Python code is located in the <strong>py_scripts.zip</strong> folder. It contains the following files:</p> <ul> <li> <p><strong>find_XYZ</strong>: locates PMMA particles. XYZ stands for different experimental parameters (see above). Scripts containing the string <strong>_problems</strong> locate PMMA particles for images with possible artefacts (smeared particles, residual light etc.). You need to uncomment the appropriate lines in the files to rerun the code because it was run piece by piece.</p> </li> <li> <p><strong>pickle_to_csv.py</strong>: converts pickle files to csv files</p> </li> <li> <p><strong>calculate_sizes.py</strong>: calculates the sizes of PMMA particles from the first image of each experiment</p> </li> <li> <p><strong>py_functions_new.py</strong>: contains custom functions</p> </li> </ul> <p>Further analysis run in a mixture of R and Pyhton in one working document (R Notebook):</p> <ul> <li> <p><strong>Analysis_with_loops.Rmd</strong>: tracking of the PMMA particles by PtrakPy version 0.4.2 (Allan et al. 2019). Python 3.8 (Python Software Foundation, <a href="https://www.python.org/">https://www.python.org/</a>) was called directly from R using the R package reticulate (<a href="https://rstudio.github.io/reticulate/">https://rstudio.github.io/reticulate/</a>) in RStudio (<a href="https://www.rstudio.com/">https://www.rstudio.com/</a>).</p> </li> <li> <p><strong>Analysis_for_paper.Rmd</strong>: R code for analysis of tracking, statistical analysis, plotting. We used the R version 4.0.3 (R Core Team 2020).</p> </li> <li> <p><strong>helper_function.R</strong>: contains custom R functions for the analysis</p> </li> </ul> <p> </p> <p><strong>Results</strong></p> <p>The file <strong>results.zip</strong> contains the folders:</p> <ul> <li> <p><strong>data</strong>: *.pickle files produced by Python containing the trajectories of PMMA particles</p> </li> <li> <p><strong>data_csv</strong>: *.pickle files converted to *.csv files</p> </li> <li> <p><strong>figures</strong>: figures produced by the code during the analysis, organized in different subfolders</p> </li> <li> <p><strong>RData</strong>: large computational results produced and saved during analysis</p> </li> <li> <p><strong>sizes_csv</strong>: *.csv files containing PMMA particle sizes and further morphological characteristics; produced during analysis</p> </li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors thank Julia Horn for support in the laboratory and Florian Steininger for technical assistance.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 391977956, SFB 1357, subprojects B04 and B06.</p> <p> </p> <p><strong>References</strong></p> <p>Allan, Dan, Casper van der Wel, Nathan Keim, Thomas A Caswell, Devin Wieker, Ruben Verweij, Chaz Reid, et al. 2019. <em>Soft-Matter/Trackpy: Trackpy V0.4.2</em> (version v0.4.2). Zenodo. <a href="https://doi.org/10.5281/zenodo.3492186">https://doi.org/10.5281/zenodo.3492186</a>.</p> <p>Laermanns, Hannes, Moritz Lehmann, Marcel Klee, Martin GJ Löder, Stephan Gekle, and Christina Bogner. 2021. “Tracing the Horizontal Transport of Microplastics on Rough Surfaces.” <em>Microplastics and Nanoplastics</em>. <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a>.</p> <p>R Core Team. 2020. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.</p>
Raw data for the submitted manuscript: Effects of microplastics on enchytraeid multigeneration reproduction and soil physicochemical properties
<p>Survival and reproduction data from multigenerational single species tests involving four types of plastic materials.</p> <p> </p>
Data for: Raman microspectroscopy and laser-induced breakdown spectroscopy for the analysis of polyethylene microplastics in human soft tissues
<p>Data from Raman microspectrometry, LIBS, XRF, and particle sizer analysis supports the findings in the published article named Raman microspectroscopy and laser-induced breakdown spectroscopy to analyze polyethylene microplastics in human soft tissues. The aim of this research is to present the optimized protocol for the detection and analysis of microplastics in biological samples.</p> <p><strong> </strong></p> <p>The tonsil tissue is used for this experiment, and the workflow consists of a few steps: 1. digestion, 2. filtration, 3. analysis. </p> <p>The presented dataset includes the data for verifying the validity of this proposed protocol and the data from clinical experiments done on tonsils where the protocol is applied. We are focusing only on PE microplastics as they are one of the most frequent plastic types in the environment. </p> <p>Firstly, the data from the particle sizer show the size distribution before and after KOH treatment, which is necessary for digestion. We are testing if the particles are not affected by the KOH solution. The data are listed in an Excel sheet where the individual detected PE particle size [µm] and their frequencies [%] are annotated. The data are separated into 2 tables in one sheet - 1st represents data collected before KOH and the 2nd after KOH treatment. </p> <p>To test the limits of our selected systems for microplastic detection, we included the data from Raman and LIBS under the file ‘limitations.’ The different sizes of PE particles, from tens to 1 µm, were analyzed, and the spectra can be retrieved in the folders. The signal intensity can be observed to see the detection limits. For the Raman analysis, the particles were located on the filter. For LIBS, the particles were embedded in epoxy to enable the detection of PE particles in tens of microns. The Raman data are in .txt files and can be opened in any adequate software (Matlab, R, Python, etc.). LIBS data are in specific .libsdata format, which can be opened by LibsAnalyzer software by Lightigo. </p> <p><strong> </strong></p> <p>The clinical experiment was done on tonsil tissue. The tissue was disgusted and filtered. Then, the filters were analyzed. The dataset presents two sample groups: 1. control-native tissue and 2. test-spiked tissue with PE particles. The data from Raman analysis include both, with the aim to confirm the presence of PE particles in the test sample and to exclude the contamination in the control sample. The spectra are again in .txt files. In the case of control, spectra from unclassified particles are presented. For these reasons, the LIBS and XRF were run to exclude the possibility of the presence of polymeric material on the filter of the control sample. The analyzed chemical elements by LIBS for both samples are in the ASC file. Furthermore, individual PE particles were also analyzed on LIBS to obtain reference results for test samples with added PE microplastics. In the case of XRF, data from the empty filter, control, and test samples are included, each in a .txt file. Individual detected chemical elements and their intensities can be retrieved in the tables. </p> <p> </p>
The more microplastic types pollute the soil, the stronger the growth suppression of invasive alien and native plants
<p>The ecological consequences of microplastic pollution for plants remain largely unknown, and the few studies that tested the effects usually focused on a single type of microplastic and a single plant species. However, most plants will be exposed to multiple microplastic types simultaneously, and the effects may vary among species.</p> <p>To test the effects of microplastic diversity on plants, we grew single plants of eight invasive and eight native species in pots with substrate polluted with 0, 1, 3 and 6 types of microplastics.</p> <p>We found that the growth suppression by microplastic pollution became stronger with the number of microplastic types the plants were exposed to. This tended to be particularly the case for invasive species, as their biomass advantage over natives diminished with the number of microplastic types. The biomass responses coincided with a positive effect of the number of microplastic types on root allocation and thickness, which was also stronger for invasive than for native species. In addition, the results of hierarchical diversity-interaction models suggest that the negative impact of microplastic diversity on the total biomass of invasive plant species was influenced by both the identities of the microplastic and certain types of microplastic with strong pairwise interactions. In contrast, the effect on native species was determined solely by the microplastic identities.</p> <p><em>Synthesis: </em>Our multi-species study thus shows for the first time that the negative effects of microplastic pollution on plant growth increase with the number of microplastic types. We also found tentative evidence that the negative impacts of microplastic diversity were more pronounced for invasive plants compared to native plants, and that this might be due to differences in the responses of root allocation and thickness.</p>
Microplastics Data from Bangshi River
<p>Microplastic abundance data were presented with their characterization. </p>
Development of a Low-Cost Method for Quantifying Microplastics in Soils and Compost Using Near-Infrared Spectroscopy
<p>Datasets and scripts for data evaluation</p>
River-groundwater Interaction and Recharge Effects on Microplastics Contamination of Groundwater in Confined Alluvial Aquifers: Original dataset
<p>The dataset includes all the microplastics analysed for the manuscript "River–Groundwater Interaction and Recharge Effects on Microplastics Contamination of Groundwater in Confined Alluvial Aquifers" by Edoardo Severini, Laura Ducci, Alessandra Sutti, Stuart Robottom, Sandro Sutti and Fulvio Celico (https://doi.org/10.3390/w14121913). The dataset "MP (ctrl and sample).txt" contains the raw data of environmental and ctrl samples. The values of the ctrl samples were used to analyse the statistical differences between ctrl and environmental samples and subsequently used to define the identification parameters (threshold) for the microplastics identification in surface water and groundwater, contained in the file "MP (after ctrl sample).txt".</p>
In-depth characterization revealed polymer type and chemical content specific effects of microplastic on Dreissena bugensis
<p>The files contain datasets that were generated during laboratory-based real-time valvometry, and laser doppler anemometry measurements. The article was published in Journal of Hazardous Materials (accepted June 8, 2022).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.