Find research datasets worth reusing

Climatopia Analysis (Supplementary Material)

<p>This data set accompanies the article titled <em><span>Defining “Climatopia”: An evaluation framework to support transformational adaptation in climate-inspired utopic design </span></em><span>by Alizé Carrère, Kenneth Broad, and Katharine J Mach.</span></p> <p> </p>

Raw Data and Codes for the Article "Strain-Affected Ferroelastic Domain Walls in RbMnFe Charge-Transfer Materials undergoing collective Jahn-Teller Distortion"

<p>Dataset for the article "Strain-Affected Ferroelastic Domain Walls in RbMnFe Charge-Transfer Materials undergoing collective Jahn-Teller Distortion", containing:</p> <ul> <li>The data and the codes used to generate the figures</li> </ul>

opencc-by-4.0Jul 2024View details →

Supporting material for "Benford's law as debris flow detector in seismic signals"

<p>Supporting material list,</p> <p>0, Source code for Random_Forest_model;</p> <p>1, Random_Forest_model.pkl, trained machine learning model;</p> <p>2, FiguresSI.zip, 58 debris flow events with Benford's law features, and Exp. fitting curve;</p> <p>3, Table2DebrisFlowEventDetails.xlsx, details of 58 debris flow events and expentional fitting parameters.</p> <p>Note, you can check our GitHub repository<br>https://github.com/Nedasd/Benfords_law_as_mass_movements_detector.git</p> <p> </p>

Supplementary material for Proctor et al., "Warm temperature inhibits cytoplasmic incompatibility induced by endosymbiotic Rickettsiella in a spider host"

<p>Included are two supplemental files: SupplementaryDocument1 includes primers and cycling conditions for qPCR quanitification of the symbiont Rickettsiella. SupplementaryDocument2 includes all raw data for the experiments included in this manuscript.</p>

Sound Absorption Coefficients of Natural Materials Database

<p>This database is a working compilation of the sound absorption coefficients (α) of various natural materials for the octave band frequencies (125, 250...4000 Hz), when data is available. Additional physical properties of these materials, including thickness, density, airflow resistivity, and noise reduction coefficient (NRC) are included when available. Examples of materials in the database include kenaf, fibers, wood, food waste products (corn husk, fruit stone wastes, etc.), and animal byproducts (chicken feathers, sheep wool, etc.). Additionally, reference links are also included for each material, for easy reference to the original work/data.</p> <p>The data was gathered from published results in peer-reviewed scientific journals, whether through the papers' graphs/figures, which were then analyzed using the PlotDigitizer tool, or the papers' data tables. The aim of this database is to provide a central location of acoustical data for sustainable materials, in the hopes that researchers, scientists, teachers, students, or interested members of the public can easily access and utilize this data, whether for acoustical, environmental, sustainability, engineering, physics, mathematical, computational, or personal purposes. </p>

opencc-by-4.0Jun 2024View details →

Fracture toughness of mixed-mode anticracks in highly porous materials dataset and data processing

<blockquote> <div>This repository contains the code and datasets used in the data analysis for "Fracture toughness of mixed-mode anticracks in highly porous materials". The analysis is implemented in Python, using Jupyter Notebooks.</div> </blockquote> <h2>Contents</h2> <ul> <li><code>main.ipynb</code>: Jupyter notebook with the main data analysis workflow.</li> <li><code>energy.py</code>: Methods for the calculation of energy release rates.</li> <li><code>regression.py</code>: Methods for the regression analyses.</li> <li><code>visualization.py</code>: Methods for generating visualizations.</li> <li><code>df_mmft.pkl</code>: Pickled DataFrame with experimental data gathered in the present work.</li> <li><code>df_legacy.pkl</code>: Pickled DataFrame with literature data.</li> </ul> <h2>Prerequisites</h2> <ul> <li>To run the scripts and notebooks, you need:</li> <li>Python 3.12 or higher</li> <li>Jupyter Notebook or JupyterLab</li> <li>Libraries: <code>pandas</code>, <code>matplotlib</code>, <code>numpy</code>, <code>scipy</code>, <code>tqdm</code>, <code>uncertainties</code>, <code>weac</code></li> </ul> <h2>Setup</h2> <ol> <li>Download the zip file or clone this repository to your local machine.</li> <li>Ensure that Python and Jupyter are installed.</li> <li>Install required Python libraries using <code>pip install -r requirements.txt</code>.</li> </ol> <h2>Running the Analysis</h2> <ol> <li>Open the <code>main.ipynb</code> notebook in Jupyter Notebook or JupyterLab.</li> <li>Execute the cells in sequence to reproduce the analysis.</li> </ol> <h2>Data Description</h2> <div>The data included in this repository is encapsulated in two pickled DataFrame files, <code>df_mmft.pkl</code> and <code>df_legacy.pkl</code>, which contain experimental measurements and corresponding parameters. Below are the descriptions for each column in these DataFrames:</div> <h3><code>df_mmft.pkl</code></h3> <div>Includes data such as experiment identifiers, datetime, and physical measurements like slope inclination and critical cut lengths.</div> <ul> <li><code>exp_id</code>: Unique identifier for each experiment.</li> <li><code>datestring</code>: Date of the experiment as a string.</li> <li><code>datetime</code>: Timestamp of the experiment.</li> <li><code>bunker</code>: Field site of the experiment. Bunker IDs 1 and 2 correspond to field sites A and B, respectively.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h_sledge_top</code>: Distance from sample top surface to the sled in mm.</li> <li><code>h_wl_top</code>: Distance from sample top surface to weak layer in mm.</li> <li><code>h_wl_notch</code>: Distance from the notch root to the weak layer in mm.</li> <li><code>rc_right</code>: Critical cut length in mm, measured on the front side of the sample.</li> <li><code>rc_left</code>: Critical cut length in mm, measured on the back side of the sample.</li> <li><code>rc</code>: Mean of <code>rc_right</code> and <code>rc_left</code>.</li> <li><code>densities</code>: List of density measurements in kg/m^3 for each distinct slab layer of each sample.</li> <li><code>densities_mean</code>: Daily mean of <code>densities</code>.</li> <li><code>layers</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>layers_mean</code>: Daily mean of <code>layers</code>.</li> <li><code>surface_lineload</code>: Surface line load of added surface weights in N/mm.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>notes</code>: Additional notes regarding the experiment or observations.</li> <li><code>L</code>: Length of the slab–weak-layer assembly in mm.</li> </ul> <h3><code>df_legacy.pkl</code></h3> <div>Contains robustness data such as radii of curvature, slope inclination, and various geometrical measurements.</div> <ul> <li><code>#</code>: Record number.</li> <li><code>rc</code>: Critical cut length in mm.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h</code>: Slab height in mm.</li> <li><code>density</code>: Mean slab density in kg/m^3.</li> <li><code>L</code>: Lenght of the slab–weak-layer assembly in mm.</li> <li><code>collapse_height</code>: Weak-layer height reduction through collapse.</li> <li><code>layers_mean</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>surface_lineload</code>: Surface line load from added weights in N/mm.</li> </ul> <p>For more detailed information on the datasets, refer to the paper or the documentation provided within the Jupyter notebook.</p> <h2>License</h2> <div>This work is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</div> <p> </p> <div>You are free to:</div> <ul> <li><strong>Share</strong> — copy and redistribute the material in any medium or format</li> <li><strong>Adapt</strong> — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <div>Under the following terms:</div> <div> <ul> <li><strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</li> </ul> </div> <h2>Citation</h2> <div>Please cite the following paper if you use this analysis or the accompanying datasets:</div> <div> <ul> <li>Adam, V., Bergfeld, B., Weißgraeber, P. van Herwijnen, A., Rosendahl, P.L., Fracture toughness of mixed-mode anticracks in highly porous materials. <em>Nature Communincations</em> <strong>15</strong>, 7379 (2024). https://doi.org/10.1038/s41467-024-51491-7</li> </ul> </div>

Letters have weight (supplementary material)

<p>This repository contains the supplementary material of the article 'Letters have weight: weighted <em>k</em>-shells in a Neo-Assyrian co-attestation network' by Ellie Bennett, Lena Tambs and Krister Lindén (2024), published in the <em>Journal of Historical Network Research </em>10: 150-197 (https://doi.org/10.25517/jhnr.v10i1.95).</p> <p>In addition to graph files and high resolution images, the repository holds three appendices and relavant supporting information:</p> <ul> <li>A ReadMe file</li> <li>A folder containing the networks, saved as .gexf files (9 files) </li> <li>A folder with high-resolution images of the network visualizations (7 files), as well as a folder with labeled versions of Figs. 7-10 (4 files), saved as .png image files</li> <li>App. B, which presents the characteristics of the network Models and analysis of N3</li> <li>App. C, which lists the texts included in the data as an .excel file</li> <li>App. D, which tests the robustness levels of six centrality measures</li> <li>A file with supporting information for App. D, saved as an .excel file</li> </ul>

Displacement time series from Foamquake and Gelquake in single- and double asperity configurations: Supplementary material to "Scaled seismotectonic models of megathrust seismic cycles through the lens of dynamical system theory"

<p><span>This dataset includes displacement data from 4 experiments performed with Foamquake and Gelquake (Mastella et al. 2022, Corbi et al., 2013), two scaled seismotectonic models reproducing the megathrust seismic cycle running at the Laboratory of Experimental Tectonics LET (Univ. Roma Tre). These models enable the generation of hundreds of quasi-periodic cycles of stress accumulation and sudden release through the spontaneous nucleation of frictional instabilities within one or many analog seismic asperities. Models are monitored by the means of a high-resolution top-view monitoring camera acquiring images at 7.5 and 50 frames per second for Gelquake and Foamquake, respectively. This dataset has been created with particle image velocimetry (PIV, using MatPIV (Sveen 2004)) through the cross-correlation between consecutive images. The PIV provides us with velocity field time series. These are integrated to obtain displacement time series. From the whole model surface, in each experiment we selected data from a cross-section striking parallel to the trench and located at the downdip center of the asperities. Cross sections are discretized in 28 and 29 target points in Gelquake and Foamquake, respectively. </span></p> <p><span>Displacement time series have been normalized to zero mean and unit variance to ensure the same level of magnitude for comparison between different experiments. Linear and second order polynomial trends have been removed to make the stick-slip confined in a given range and avoid non-stationary behavior. Time series data are not passed through filters (e.g., smoothing or moving average).</span></p> <p><span>Filename informs about the nature of the analog upper plate (i.e., foam and gel) and geometrical configuration of asperities (i.e., mono and twin). Together with individual files for each experiment, this dataset includes a Matlab script (i.e., all_timeseries.m) that allows visualization of displacement time series from individual target points. </span></p> <p><span>This dataset is supplementary to the paper in SEISMICA "Scaled seismotectonic models of megathrust seismic cycles through the lens of dynamical system theory” by Corbi et al. (2024), where detailed descriptions of models and experimental results can be found.</span></p>

Gold, Lanthanum, Gallium, Cobalt and Tantalum quantification performed with relative standardization-NAA in candidate electronic waste (LED) reference material within the METROCYCLEEU project

<p>Datasets containing uncertainty budgets of measurements performed with Neutron Activation Analysis (NAA) on electronic waste materials (specifically light-emitting diodes, LED).</p> <p>Results reported in the datasets are part of the characterization of CRM candidate material LED.</p> <p>Data are elaborated and resulting dataset files are obtained with INAA-INRIM 3.1 software, pre-release development version.</p>

Supplementary Material for "Target-capture Phylogenetics of the Paper Daisy Stoebe clade (Gnaphalieae: Asteraceae) Using Compositae1061 Baits"

<p>This upload contains supporting information for a manuscript in preparation titled "Target-capture Phylogenetics of the Paper Daisy Stoebe clade (Gnaphalieae: Asteraceae) Using Compositae1061 Baits". The folder contains sequence alignments for the Stoebe clade of paper daisies (Gnaphalieae: Asteraceae), as well as species trees estimated using these data. Additional files are described in the accompanying README file.</p>

Implementation of Aerosol Mie Scattering in POSEIDON with Application to the hot Jupiter HD 189733 b's Transmission, Emission, and Reflected Light Spectrum - Supplementary Material

<p>Supplementary material to 'Implementation of Aerosol Mie Scattering in POSEIDON with Application to the hot Jupiter HD 189733 b’s Transmission, Emission, and Reflected Light Spectrum'</p> <p>This zenodo repository corresponds to the updates to POSEIDON presented in Mullens et al 2024, as well as the benchmark retrievals preformed on archival HD 189733 b. </p> <p>In the upper level of the repository we have data products mentioned in the paper:</p> <p>aersosol_database.pdf <br>Aerosol-Database-Readme.txt<br>aerosol_database.hdf5</p> <p>There are then three directories (here as zip files): </p> <p>Aerosol-Datbase<br>- This folder contains the aerosol properties for aerosols in Table 1 in the paper. It contains the aerosol-database.hdf5 which contains the precomputed aerosol properties that POSEIDON utilizes in forward models and retrievals, a folder containing the npy files that have individual precomputed aerosol properties (used to generate the hdf5 file), a pdf containing the refractive indices + precomputed aerosol properties for each aerosol in the database (png versions of these figures are available in the Aerosol-Optical-Properties-Pngs folder), the refractive index txt files for each aerosol, and a readme file that contains information on each aerosol in the database (such as aerosol name, polymorph, crystalline or amorphous, crystal shape, information on samples used in refractive index references, and exoplanet/planetary science specific references). </p> <p>HD-189733b-Retrievals<br>- Contains retrieval scripts and retrieval results for the transmission, emission, and emission+reflection retrievals in the paper. Also has the notebooks used to make the figures in the paper.</p> <p>POSEIDO-V1-2<br>- Contains the POSEIDON module used to run retrievals and generate figures, and corresponds to POSEIDON version 1.2. Also contains a folder containing tutorial notebooks for new features introduced into POSEIDON version 1.2. Included in this repository for posterity.</p>

Myers et al. - Two material analogue experiments of lava dome extrusion

<p>Here we provide the full dataset to accompany the experiments presented in Myers et al. (2024) Lava dome morphology controlled by viscosity and cohesion: analogue experiments of episodic dome growth. We systematically explored the effect of cohesion and viscosity by carrying out experiments using (1) only granular material (sand, sand with 10 wt.% gypsum, and sand with 20 wt.% gypsum); (2) each granular material with a low viscosity syrup-sugar suspension, with 30 wt.% sugar; and (3) each granular material with a high viscosity syrup-sugar suspension, with 50 wt.% sugar.</p> <p>Solid only (paper Section 3.1)<br>1. Sand<br>2. Sand with 10% gypsum<br>3. Sand with 20% gypsum</p> <p>Granular material with a low viscosity fluid (paper Section 3.2)<br>4. Sand, low viscosity fluid<br>5. Sand with 10% gypsum, low viscosity fluid<br>6. Sand with 20% gypsum, low viscosity fluid</p> <p>Granular material with a high viscosity fluid (paper Section 3.3)<br>7. Sand, high viscosity fluid<br>8. Sand with 10% gypsum, high viscosity fluid<br>9. Sand with 20% gypsum, high viscosity fluid</p>

Dataset related to "Material recycling in energy system modeling: a review and showcase"

<p>This dataset has been generated and used for the publication:</p> <blockquote> <p>Zwickl-Bernhard, S., 2024. Material recycling in energy system modeling: a review and showcase. ... DOI: ...</p> </blockquote> <p>The corresponding code is published on *Github. #add link after publication</p> <p>The dataset includes:</p> <ol> <li>Compiled data for manufacturing costs of Solar Modules and Wind Turbines in the EU (manufacturing costs in the EU.xlsx)</li> <li>Compiled data for modified prices (modified prices.xlsx)</li> <li>Additional Data (scalars.xlsx)</li> <li>Sources (sources.txt)</li> <li>Compiled data for yearly capacity of Solar Modules and Wind Turbines (vectors_capacity.xlsx)</li> <li>Compiled data for yearly costs of Solar Modules and Wind Turbines (vectors_costs.xlsx)</li> <li>Compiled data for yearly manufacturing costs of Solar Modules and Wind Turbines (vectors_manufacturing.xlsx)</li> </ol>

Research Data for "In-vacuo scratching yields undisturbed insight into the bulk of lithium-ion battery positive electrode materials"

<p>This are the datasets supporting the figures and tables in the manuscript and supporting information of the publication "In-vacuo scratching yields undisturbed insight into the bulk of lithium-ion battery positive electrode materials".</p> <p>Available in ACS Energy Letters under <span><a href="https://doi.org/10.1021/acsenergylett.4c02106"><span>https://doi.org/10.1021/acsenergylett.4c02106</span></a></span></p>

Replication materials for "Regional Warming from Urbanization is Disproportionate to Urban Expansion Rate"

<p>The datasets are replication materials for the research "Regional Warming from Urbanization is Disproportionate to Urban Expansion Rate". They show simulated global near-surface air temperature (TSA), specific humidity (Q2M), sensible heat flux (FSH), and latent heat flux (LHFLX) during the warm season over 50 years for four experiments: URB1985, URB2018, URB2018_0, and URB2018_×2. </p>

Supproting Materials for "Cross-Linked Selenoctanoic Acid Nanoplatform Enables Bidirectional Regulation of Intra- and Extracellular ROS: A New Avenue for Enhanced Cancer Immunotherapy" - section A

<p><strong>Table of Content</strong></p> <h1>1.     Synthesis and characterization</h1> <h2>            1.1.     SeLA</h2> <h3>                      1.1.0.     Lab book pages</h3> <h3>                      1.1.1.     NMR</h3> <h3>                      1.1.2.     HR-MS</h3> <h3>                      1.1.3.     UV</h3> <h3>                      1.1.4.     IR</h3> <h2>            1.2.     cSeLAN</h2> <h3>                      1.2.0.     Lab book pages</h3> <h3>                      1.2.1.     NMR</h3> <h3>                      1.2.2.     UV</h3> <h3>                      1.2.3.     IR</h3> <h3>                      1.2.4.     GPC</h3> <h3>                      1.2.5.     MALDI-TOF-MS</h3> <h3>                      1.2.6.     DLS</h3> <h3>                      1.2.7.     Critical melle cncentration</h3> <h3>                      1.2.8.     ICP-MS</h3> <h3>                      1.2.9.     TEM</h3> <h2>            1.3.     cLAN</h2> <h3>                      1.3.0.     Lab book pages</h3> <h3>                      1.3.1.     NMR</h3> <h3>                      1.3.2.     UV</h3> <h3>                      1.3.3.     DLS</h3> <h3>                      1.3.4.     GPC</h3> <h1>2.     <em>In vitro</em> intra- and extracellular ROS regulation</h1> <h2>            2.0.     Lab book pages</h2> <h2>            2.1.     Raman scattering spectra</h2> <h2>            2.2.     HPLC</h2> <h3>                      2.2.1.     Standard peaking</h3> <h3>                      2.2.2.     HPLC analysis</h3> <h2>            2.3.     ICP-MS</h2> <h2>            2.4.     MTT</h2> <h2>            2.5.     Intracellular ROS generation</h2> <h3>                      2.5.1.  Fluorescence images</h3> <h3>                      2.5.2.  Flow fluorescence quantification</h3> <h2>            2.6.     Intracellular pro-oxidation mechanism</h2> <h3>                      2.6.1.  Quantification of intracellular ATP</h3> <h3>                      2.6.2.  Quantification of intracellular NADH</h3> <h3>                      2.6.3.  Quantification of intracellular <strong>·</strong>OH</h3> <h3>                      2.6.4.  Quantification of intracellular ROS</h3> <h2>            2.7.     Quantification of extracellular ROS</h2> <h1>3.     <em>In vitro</em> ICD-inducing ability</h1> <h2>            3.0.   Lab book pages</h2> <h2>            3.1.     ATP release</h2> <h2>            3.2.     HMGB1</h2> <h2>            3.3.     CRT</h2> <h1>4.     T cell proliferation and activation</h1> <h2>            4.0.   Lab book pages</h2> <h2>            4.1.     Flow cytometry analysis of the T cell proliferation</h2> <h2>            4.2.     Quantification of IFN-γ secretions</h2> <h1>5.     Biosafety evaluation of cSeLAN</h1> <h2>            5.0.       Lab book pages</h2> <h2>            5.1.       Hemolytic and hemagglutination assay</h2> <h3>                      5.1.1.  Hemolytic assay</h3> <h3>                      5.1.2.  Hemagglutination assay</h3> <h2>            5.2.       Acute toxicity test</h2> <h3>                      5.2.1.  The amounts of dead mice</h3> <h3>                      5.2.2.  Body weight of mice</h3> <h3>                      5.2.3.  Hematological assay</h3> <h3>                      5.2.4.  Blood biochemistry</h3> <h3>                      5.2.5.  H&E staining of main organs</h3> <h1>6.     Pharmacokinetic evaluation of cSeLAN</h1> <h2>           6.0.   Lab book pages</h2> <h2>           6.1    Pharmacokinetic evaluation of cSeLAN</h2> <h1>7.     <em>In vivo</em> anti-tumor immune response</h1> <h2>           7.0.   Lab book pages</h2> <h2>           7.1.   Chemiluminescence images</h2> <h3>                    7.1.1.  Chemiluminescence images of isoluminol</h3> <h3>                    7.1.2.  Chemiluminescence images of luminol</h3> <h3>                    7.1.3.  Related quantifications upon various treatments</h3> <h2>           7.2.   Immunofluorescence images</h2> <h2>           7.3.   T cell proliferation</h2> <h2>           7.4.   DC proliferation</h2> <h1>8.     <em>In vivo</em> anti-tumor efficacy</h1> <h2>           8.0.   Lab book pages</h2> <h2>           8.1.   Tumor growth</h2> <h3>                    8.1.1.  Tumor volume</h3> <h3>                    8.1.2.  Tumor weight</h3> <h3>                    8.1.3.  Tumor images</h3> <h2>           8.2.   Body weight</h2> <h2>           8.3.   Lung tissues with metastatic nodules</h2> <h3>                    8.3.1.  Lung tissues images</h3> <h3>                    8.3.2. Number of pulmonary metastatic nodules</h3> <h3>                    8.3.3.  H&E</h3> <h2>           8.4.   H&E of tumor and main organs</h2> <h2>           8.5.   Cytokine secretions</h2> <h2>           8.6.   Kaplan−Meier survival curves</h2> <h1>9.     <em>In vitro</em> NK cytotoxicity and activity</h1> <h2>           9.0.   Lab book pages</h2> <h2>           9.1.   NK cytotoxicity</h2> <h2>           9.2.   NK activity</h2>

A Systematic Mapping Study on Student Reflection in Software Engineering Project-Based Learning (supplementary material)

<p>This repository contains supplementary material for the manuscript "A Systematic Mapping Study on Student Reflection in Software Engineering Project-Based Learning". The CSV file is structured as follows:</p> <ul> <li>Column A represents the paper citation key generated by Zotero that we used as a unique identifier for all papers in this study.</li> <li>Columns B-I include the results of our data extraction as per the data extraction form.</li> <li>Columns J-AB include metadata about each paper.</li> </ul>

The role of chemical properties of the material deposited in nests of white stork in shaping enzymatic activity and fungal diversity - dataset

<p>Dataset to paper: Błońska E., Jankowiak R., Lasota J., Krzemińska N., Zbyryt A., Ciach M. 2024. The role of chemical properties of the material deposited in nests of white stork in shaping enzymatic activity and fungal diversity. Environmental Science and Pollution Research 31, 2: 2583-2594. https://doi.org/10.1007/s11356-023-31383-x</p> <p>This study was financially supported by the National Science Centre, Poland (grant no. 2021/41/B/NZ8/03456).</p>

Sorption strategies for recovering critical raw materials: Extracting trace elements from saltworks brines

<p><span>Rising population, industrialisation, and resource demand intensify resource limitations, particularly critical for European Union (EU) industries dependent on raw materials. Based on economic importance and supply risk, 32 Critical Raw Materials (CRMs) and two strategic raw materials were identified in 2023. With most CRMs sourced outside the EU, sea mining emerges as a promising secondary resource for CRMs extraction from seawater, although the harvest of Trace Elements (TEs) will require energy-intensive processes. Several EU-funded projects explore circular economy and resource recovery, considering sorption methods’ potential to extract TEs from brines. Commercial polymeric (IRC747, S940, MTX8010) and inorganic (SbTreat, SrTreat) sorbents, plus another synthesised inorganic one (CuHCF), were assessed for recovering TEs (cobalt, gallium, germanium, rubidium, strontium, caesium) from saltworks brines (bitterns). Polymeric sorbents, containing chelating functional groups, effectively targeted cobalt, gallium, and strontium, commercial inorganic sorbents favoured cobalt, gallium, germanium and strontium, and CuHCF targeted rubidium and caesium. Kinetic batch experiments demonstrated rapid element retention (≤30 min) by most sorbents. In dynamic column experiments, high sorption capacities were observed for cobalt and gallium with the polymeric sorbents, particularly for aminophosphonic sorbents (IRC747 and S940) (≥2.1 mg/g). SrTreat exhibited also a high sorption capacity of 7 mg/g for strontium, SbTreat achieved 20 mg/g for gallium and germanium and CuHCF retained rubidium (10 mg/g) and caesium (70 mg/g). Acidic desorption effectively recovered (>70 %) most of the elements from the sorbents, achieving concentration factors up to 708 for cobalt with IRC747 and S940, highlighting the potential valorisation of saltworks bitterns.</span></p>

Deliverable D1.2 - Supplementary material : Datasets on substance prioritization for subsequent development of target analytical methods for relevant iPM(T) substances in waters in case studies CS#1, CS#2, CS#3 & CS#7.

<p>Additional information on deliverable D1.2. Data sets for substance prioritization and subsequent development of target analytical methods for relevant iPM(T) substances in waters in case studies CS#1, CS#2, CS#3 and CS#7.</p>