Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
19
datasets available to search
ShareScore release 0.9.0
Dataset results
19 results for “material intensity”
Supplementary Material of the Manuscript: "The effects of blank size and knapping strategy on the estimation of core's reduction intensity"
<p>This repository hosts the R-markdown and dataset that allow reproducibility and replicability of the statistical analyses implemented in the paper: Lombao, D., Cueva-Temprana, A., Rabuñal, J.R., Morales, J.I., Mosquera, M. The effects of blank size and knapping strategy on the estimation of core’s reduction intensity. <em>Archaeol Anthropol Sci</em> <strong>11</strong>, 5445–5461 (2019). https://doi.org/10.1007/s12520-019-00879-4<br> For further information, please open first the readme.txt. document.</p>
Regional Assessment of buildings' Material Intensities (RASMI): Version 20230905: first public release B - data only
<p><strong>Version 20230905: first public release of RASMI (Regional Assessment of buildings' Material Intensities).</strong></p> <p>This Zenodo version contains two files:</p> <ul> <li><code>MI_ranges_20230905.xlsx</code> is the dataset of the estimated MI ranges. <em><strong>This is probably the file you're looking for.</strong></em></li> <li><code>MI_data_20230905.xlsx</code> is the raw pools of MI used to create the MI ranges. This is mostly for reproducability.</li> </ul> <p>Please refer to the GitHub readme.md in <a href="https://github.com/TomerFishman/MaterialIntensityEstimator">https://github.com/TomerFishman/MaterialIntensityEstimator</a> for details and how to use.</p> <p>Please cite both the Data Descriptor and the specific data version used:</p> <p>Data Descriptor: Tomer Fishman, Alessio Mastrucci, Yoav Peled, Shoshanna Saxe, Bas van Ruijven. <em>RASMI: Global Ranges of Building Material Intensities Differentiated by Region, Structure, and Function</em>. Scientific Data 2024, 11 (1), 418. <a href="https://doi.org/10.1038/s41597-024-03190-7" rel="nofollow">https://doi.org/10.1038/s41597-024-03190-7</a>.</p> <p>Data version: preferably use the DOI of the Zeonodo release. Refer to the release number (on the right)</p> <p>This work was conducted with support by the IIASA-Israel program, and by the Israel Science Foundation project RUSTY (grant no. 2706/19). Funding was also provided by the Horizon Europe research and innovation programme under grant agreement no. 101056868 (CIRCOMOD) for TF and grant agreement No 101056810 (CircEUlar) for AM. Opinions are those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for this. BvR and AM have been supported by the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, which is an initiative coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and the International Institute for Applied Systems Analysis (IIASA), and funded by the Ministry of Economy, Trade, and Industry (METI), Japan. SS was supported by the Canada Research Chair in Sustainable Infrastructure, Grant Number: 232970.</p>
Supplementary Materials for: Intense alteration on early Mars revealed by high-aluminum rocks at Jezero crater
<p>Supplementary tables S3 and S4 for "Intense alteration on early Mars revealed by high-aluminum rocks at Jezero crater" published in Nature Communications Earth & Environment.</p>
Supplementary materials of the manuscript "Establishing a new workflow in the study of core reduction intensity and distribution".
<p>This repository hosts the R code scripts and datasets that allow reproducibility and replicability of the statistical analyses implemented in the paper: Lombao et al. (2022). Establishing a new workflow in the study of core reduction distribution. Journal of Lithic Studies.</p> <p> </p> <p>The analytical and statistical protocols applied for this study were implemented in in R (version 3.6.3) (R Core Team, 2021).</p> <p>Contents:<br> 1. Script.txt: The R- Script with all the packages and functions used and all the steps followed in the statistical analyses.<br> 2. vrm_experiment_database.xlsx: the database with the data used in this manuscript. The data comes from an experiment presented in Lombao et al., 2020. A new approach to measure reduction intensity on cores and tools on cobbles: the Volumetric Reconstruction Method. Archaeological and Anthropological Sciences 12(9)<br> DOI: 10.1007/s12520-020-01154-7<br> 3. Supplementary_table_S1.docx<br> 4. Supplementary_table_S2.docx<br> 5. Supplementary_table_s3.xlsx This database is used in the R-script. </p>
Material Intensity data for Renewable Energy Technologies
<p>The file contains detailed data on material intensities (measured in tons per gigawatt) collected from various literature sources. This data encompasses the material intensity of 30 metals through a range of technologies, including offshore and onshore wind power, solar energy, nuclear power, heat pumps, hydroelectric power, hydrogen production via electrolyzers, biomass energy, and biomass with carbon capture and storage (CCS).</p> <p>The variables are:</p> <ul> <li><strong>tech:</strong> Technology (<em>offshore_wind, onshore_wind, Solar, heat_pumps, hydrogen, biomass, biomassccs, hydroelectric, nuclear</em>).</li> <li><strong>material: </strong>Metal the material intensity refers to (<em>Aluminium, Arsenic, Bismuth, Boron, Cadmium, Chromium, Copper, Gallium, Germanium, Indium, Iron, Lead, Molybdenum, Nickel, Selenium, Silicon, Silver, Tellurium, Tin, Vanadium, Zinc, Dysprosium, Manganese, Neodymium, Niobium, Praseodymium, Terbium, Titanium, Iridium, Platinum</em>).</li> <li><strong>type: </strong>for Solar defines whether it is <em>roof_mounted</em> or <em>open_field.</em></li> <li><strong>m_int:</strong> The value of material intensity for the corresponding metal in the corresponding technology.</li> <li><strong>classification: </strong>specific classification for Solar, Wind and Heat pumps' technologies: e.g. for solar, defines the type of photovoltaic cell.</li> <li><strong>year: </strong>year in which it is assumed the data was collected, usually defined as the year of publication of the source paper.</li> </ul> <p>This dataset is part of the Master Thesis <em><span>Mining Industry and Energy Transition </span><span>Scenarios for Europe: Promoting a </span><span>Pluriversal Approach, </span></em><span>developed in the TISE (Transition, Innovation, and Sustainability Environments) program with collaboration with the Complexity Science Hub.</span></p>
CBMICD1.0: China's building material intensity coefficient dataset (1949-2015)
<p>The building material intensity coefficients is the most commonly used method to estimated building material stock and resource recycling potential in the urban building system at various spatial-temporal scales because of its high accuracy, efficiency and conciseness. With the circular economy become the research hotspot, some building material intensity coefficients datasets have been reported in diverse type of literature (e.g., journals, books and reports). These scattered datasets are being compiled, and building material intensity coefficients are currently for some countries (e.g., Japan, Germany and Sweden) except for in an important country of world, China which has the most rapid urbanization. Therefore, in this study, we carried out an extensive survey and critical review of the literature (from 1949-2017) on building material intensity coefficients in China and developed China’s building material intensity coefficient dataset (CBMICD1.0). This dataset consists of 813 different aged buildings with different structures located in 30 provinces in China and their associated background information (e.g. geographical location, building application and building structure).The dataset which provides essential supporting information for researchers on circular economy, is helping to provide a better understanding of the availability of resources in the city and its future potential supply for recycling as well as to develop strategies for the management of construction and demolition waste. </p>
Supplmental Material to Nature of Intense Magnetism and Differential Rotation In Convective Dynamos of M-Dwarf Stars With Tachoclines
<p>Inlists and source files used for the MESA model in the paper "Nature of Intense Magnetism and Differential Rotation In Convective Dynamos of M-Dwarf Stars With Tachoclines"</p> <p>This work employed MESA version 10398.</p>
Supplementary information to "Material intensity and embodied CO2 benchmark for reinforced concrete structures in Brazil"
<p>This Excel file contains the electronic supplementary information to the manuscript " Material intensity and embodied CO<sub>2</sub> benchmark for reinforced concrete structures in Brazil", including detailed structural design data for the 53 analyzed buildings and the calculation of the structural material quantity and embodied CO2 indicators.</p>
Supplementary material 1 from: Francis B, Gilman RT (2019) Light intensity affects leaf morphology in a wild population of Adenostyles alliariae (Asteraceae). Italian Botanist 8: 35-45. https://doi.org/10.3897/italianbotanist.8.39393
: Data type: morphometric data
Supplementary material 3 from: Palmas P, Gouyet R, Oedin M, Millon A, Cassan J-J, Kowi J, Bonnaud E, Vidal E (2020) Rapid recolonisation of feral cats following intensive culling in a semi-isolated context. NeoBiota 63: 177-200. https://doi.org/10.3897/neobiota.63.58005
Figure S3
Supplementary material 2 from: Palmas P, Gouyet R, Oedin M, Millon A, Cassan J-J, Kowi J, Bonnaud E, Vidal E (2020) Rapid recolonisation of feral cats following intensive culling in a semi-isolated context. NeoBiota 63: 177-200. https://doi.org/10.3897/neobiota.63.58005
Figure S2
Supplementary material 1 from: Palmas P, Gouyet R, Oedin M, Millon A, Cassan J-J, Kowi J, Bonnaud E, Vidal E (2020) Rapid recolonisation of feral cats following intensive culling in a semi-isolated context. NeoBiota 63: 177-200. https://doi.org/10.3897/neobiota.63.58005
Figure S1>
Supplementary Material for Ph.D. thesis: "Development of a data-intensive centralized system for surveillance and outbreak investigation of bacterial pathogens using whole-genome sequencing""
<p>Supplementary material for Ph.D. thesis.</p>
Supplementary material 1 from: Jagodziński AM, Dyderski MK, Horodecki P, Knight KS, Rawlik K, Szmyt J (2019) Light and propagule pressure affect invasion intensity of Prunus serotina in a 14-tree species forest common garden experiment. NeoBiota 46: 1-21. https://doi.org/10.3897/neobiota.46.30413
: Data type: measurement
Supplementary material 2 from: Jagodziński AM, Dyderski MK, Horodecki P, Knight KS, Rawlik K, Szmyt J (2019) Light and propagule pressure affect invasion intensity of Prunus serotina in a 14-tree species forest common garden experiment. NeoBiota 46: 1-21. https://doi.org/10.3897/neobiota.46.30413
: Data type: measurement
Supplementary material 3 from: Jagodziński AM, Dyderski MK, Horodecki P, Knight KS, Rawlik K, Szmyt J (2019) Light and propagule pressure affect invasion intensity of Prunus serotina in a 14-tree species forest common garden experiment. NeoBiota 46: 1-21. https://doi.org/10.3897/neobiota.46.30413
: Data type: measurement
Supplementary material 4 from: Jagodziński AM, Dyderski MK, Horodecki P, Knight KS, Rawlik K, Szmyt J (2019) Light and propagule pressure affect invasion intensity of Prunus serotina in a 14-tree species forest common garden experiment. NeoBiota 46: 1-21. https://doi.org/10.3897/neobiota.46.30413
: Data type: measurement
Supplementary material 2 from: Standovár T, Horváth S, Aszalós R (2017) Temporal changes in vegetation of a virgin beech woodland remnant: stand-scale stability with intensive fine-scale dynamics governed by stand dynamic events. Nature Conservation 17: 35-56. https://doi.org/10.3897/natureconservation.17.12251
Map showing canopy trees in the study area. :
Supplementary material 1 from: Standovár T, Horváth S, Aszalós R (2017) Temporal changes in vegetation of a virgin beech woodland remnant: stand-scale stability with intensive fine-scale dynamics governed by stand dynamic events. Nature Conservation 17: 35-56. https://doi.org/10.3897/natureconservation.17.12251
Map showing the position of sampling plots in 1996 and 2013 :
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.