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175 results for “built”
Example Microscopy Metadata JSON files produced using Micro-Meta App to document the acquisition of example images using a custom-built TIRF Epifluorescence Structured Illumination Microscope
<p><strong>Example Microscopy Metadata JSON files produced using the <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> documenting an example raw-image file acquired using the custom-built TIRF Epifluorescence Structured Illumination Microscope.</strong></p> <p>For this use case, which is presented in Figure 5 of <a href="http://doi: https://doi.org/10.1101/2021.05.31.446382">Rigano et al., 2021</a>, Micro-Meta App was utilized to document:</p> <p>1) The <strong>Hardware Specifications</strong> of the custom build TIRF Epifluorescence Structured light Microscope (TESM; <a href="https://www.pnas.org/content/109/8/E471.long">Navaroli et al., 2010</a>) developed, built on the basis of the based on Olympus IX71 microscope stand, and owned by the Biomedical Imaging Group (http://big.umassmed.edu/) at the Program in Molecular Medicine of the University of Massachusetts Medical School. Because TESM was custom-built the most appropriate documentation level is <strong>Tier 3</strong> (<em>Manufacturing/Technical Development/Full Documentation</em>) as specified by the <a href="https://doi.org/10.5281/zenodo.4710731">4DN-BINA-OME</a> Microscopy Metadata model (<a href="https://doi.org/10.1101/2021.04.25.441198">Hammer et al., 2021</a>).</p> <p>The TESM Hardware Specifications are stored in: <strong>Rigano et al._Figure 5_UseCase_Biomedical Imaging Group_TESM.JSON</strong></p> <p>2) The <strong>Image Acquisition Settings</strong> that were applied to the TESM microscope for the acquisition of an example image (FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif) obtained by Nicholas Vecchietti and Caterina Strambio-De-Castillia. For this image, TZM-bl human cells were infected with HIV-1 retroviral three-part vector (FSWT+PAX2+pMD2.G). Six hours post-infection cells were fixed for 10 min with 1% formaldehyde in PBS, and permeabilized. Cells were stained with mouse anti-p24 primary antibody followed by DyLight488-anti-Mouse secondary antibody, to detect HIV-1 viral Capsid. In addition, cells were counterstained using rabbit anti-Lamin B1 primary antibody followed by DyLight649-anti-Rabbit secondary antibody, to visualize the nuclear envelope and with DAPI to visualize the nuclear chromosomal DNA.</p> <p>The Image Acquisition Settings used to acquire the FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif image are stored in: <strong>Rigano et al._Figure 5_UseCase_AS_fswt-6hvirus-10minfix-stk_4-epi.tif.JSON</strong></p> <p><em><strong>Instructional video tutorials on how to use these example data files:</strong></em><br> Use these videos to get started with using Micro-Meta App after downloading the example data files available here.</p> <ul> <li><a href="https://vimeo.com/562022222">Part 1/2</a></li> <li><a href="https://vimeo.com/562022281">Part 2/2</a></li> </ul>
Built Open spaces
<p>ESM data subset, generated by extracting band 30 as buildings with the next information:</p> <p>gid integer geom geometry(Polygon,EPSG:3035) albedo real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint hillshade_building real</p> <p>This data is an input for local effects calculation.</p>
Pre-built leave-out-out cross-validation imputation reference panel datasets - LmTag paper
<p>Pre-built leave-out-out cross-validation imputation reference panel datasets - LmTag paper (complement for a tutorial at https://github.com/datngu/LmTag)</p> <p>This repo includes chromosome 10 reference panel data constructed for 3 populations:</p> <p>- EAS</p> <p>- EUR</p> <p>- SAS</p>
Social Innovations for Circularity in the Built Environment – a Scoping Review and Classification. Supplementary Data to the Bibliometric Review.
<p>To transition the built environment (BE) towards circularity, i.e., maximizing the time resources spend in the BE, thus minimizing negative environmental impacts of resource usage, social innovations (SI) – understood as new ways of doing, organizing, framing, and knowing – are just as important as technological advancements. This article provides an overview of the state of the knowledge regarding SI that contribute to circularity in the BE, and proposes a framework for coherently classifying such SI in terms of their main categories and effects.</p> <p>To identify and understand the current knowledge regarding social innovations (SI) that contribute to circularity in the built environment (BE), a bibliometric review of scientific literature is conducted. It shows that the term social innovation is not frequently used in this contexts although the buzzwords circularity and circular economy are themselves often framed as SI. To assess the characteristics and contribution of SI to circularity in the BE, a scoping review that includes grey literature into the context was conducted and a framework developed to classify predominant SI using concepts from transition studies as well as the systems thinking approach. The framework is designed to help assess the potential of SI and to identify research and/or action gaps. Key findings are (1) There is a broad diversity of SIs that contribute to circularity in the BE already in the focus of research, although they are not always identified as such; (2) Most SI focus on either the design or the demolition phase (i.e., market related phases), whereas user-centered SI are less frequently discussed; and (3) It is crucial to also consider potential sustainability goal conflicts in order to guide policies that address SI as a solution.</p> <p>These datasets are the basis to the bibliometric literature review.</p>
Solar Asset Mapper: A continuously-updated global inventory of solar energy facilities built with satellite data and machine learning
<p><strong>TransitionZero’s Solar Asset Mapper is a global, satellite-derived dataset of utility-scale solar farms generated with a combination of machine learning and human annotation. Our Q1 2024 dataset contains the location and shape of 63,616 assets, along with estimated capacities. We estimate the construction date for over 80% of these assets. The dataset contains over 19,100 square kilometres of solar farms across 183 countries, with a total estimated capacity of 711 GW.</strong></p> <p>Download the dataset, read the explainer and explore our polygon browser UI at <a href="https://www.transitionzero.org/products/solar-asset-mapper" target="_blank" rel="noopener">TransitionZero.org.</a></p> <p><a href="https://blog.transitionzero.org/hubfs/Data%20Products/TZ-SAM/tz-sam-scientific-methodology-Q12024.pdf" target="_blank" rel="noopener">Download our methodology paper here </a></p> <h1><strong>1. Dataset Description</strong></h1> <p>We publish six files.</p> <ul> <li><em>analysis_polygons.gpkg:</em> our “analysis-ready” dataset containing geometries, capacity estimates and construction date estimates.</li> <li><em>analysis_polygons.csv:</em> a version of analysis_polygons.gpkg containing a central latitude and longitude in place of a geometry, to allow parsing without geospatial software.</li> <li><em>sources.csv</em>: a table mapping the IDs of our analysis-ready dataset to the raw geometries that make them up.</li> <li><em>raw_polygons.gpkg:</em> the raw geometries used to compose analysis_polygons.gpkg.</li> <li><em>TZ Solar Asset Mapper Q1 2024.xlsx</em>: an Excel formatted version of the analysis_polygons.csv file.</li> <li><em>tz-sam_scientific_data.pdf</em>: A pre-print aricle that explains the methodology in detail.</li> </ul> <h2><strong>1.1 Analysis-level datasets</strong></h2> <p>Our analysis-level dataset comprises our most complete view of global asset-level solar installations, incorporating our own detections as well as known solar farm geometries from other datasets.</p> <p>The geospatial dataset contains the following fields:</p> <ul> <li>id: unique ID for the asset</li> <li>geometry: Polygon or MultiPolygon defining the asset</li> <li>capacity_mw: estimated capacity of the asset in megawatts</li> <li>constructed_before: upper bound for construction date (estimated date of the image in which the solar plant was first seen in a constructed state)</li> <li>constructed_after: lower bound for construction date (estimated date of the image in which construction began for the solar plant)</li> </ul> <p>The CSV version replaces the Geometry column with:</p> <ul> <li>latitude: the latitude of the centroid of the asset</li> <li>longitude: the longitude of the centroid of the asset</li> <li>country: administrative country name</li> </ul> <h2><strong>1.2 Raw datasets and sources</strong></h2> <p>The analysis-level datasets hide some complexity in the underlying data that we expose in the <em>raw_polygons</em> and <em>sources</em> file.</p> <ul> <li>We produce new sets of polygons for each run. Often these overlap, sometimes in complicated ways.</li> <li>We cluster together overlapping and nearby geometries from both our detections and external sources. Currently these sources are:</li> <li>Large solar farms scraped from OpenStreetMap (OSM)</li> <li>Validated geometries from <a href="../records/5005868">Kruitwagen et. al., A global inventory of solar photovoltaic generating units</a>.</li> </ul> <p>Each cluster comprises one row in the analysis-level dataset. In order to enable tracking raw detections from run to run, as well as to provide detailed sourcing information, we provide all of these raw polygons, along with a source file that lists all of the raw polygons contained in each analysis-level polygon.</p> <p>raw_polygons.gpkg contains the following fields:</p> <ul> <li>id: ID of the raw source polygon</li> <li>geometry<strong>: </strong>Polygon or MultiPolygon defining the asset</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <p>Sources.csv contains the following fields:</p> <ul> <li>cluster_id: ID of the corresponding item in the analysis-level dataset</li> <li>source_id: ID of the raw source polygon</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <h2><strong>1.3 Caveats and limitations</strong></h2> <h3><strong>1.3.1 Capacity Estimates</strong></h3> <p>While we have made every effort to remove false positives from the published dataset, some will remain due to the difficulty of manually validating detections in 10-metre satellite imagery. To estimate false positive prevalence throughout the data a subset of approximately 2000 detections were selected at random from our positively labelled solar assets. Each of these were validated through a higher degree of scrutiny utilising high-resolution imagery. This analysis yielded an expected rate of false positives of around 1%.</p> <h3>1.3.2 Plant Shapes</h3> <p>Our plant outlines are not perfect. They will occasionally be much smaller or larger than the underlying plant. Our tests show that on average, these effects average out.</p> <h3>1.3.3 Capacity Updates</h3> <p>Our capacity estimation model should produce relatively unbiased country-level aggregates, since it is trained to learn the typical ground coverage ratio of plants by country. The model has no way to distinguish between a very dense and a very sparse (e.g. dual-axis-tracking) plant in the same country. Plants with unusually high or low ground coverage ratios will not have accurate capacity estimates.</p> <h3>1.3.4 Construction Date Estimates</h3> <p>We are not able to directly estimate the construction date of a plant. We estimate an upper bound (the date of the image in which the plant was first seen in constructed state) and a lower bound (the date of the image in which the plant was last seen in an unconstructed state). For plants that were constructed before the launch date of Sentinel-2 in 2017, we produce only an upper bound.</p> <p>We leave it to consumers of the data to interpret these bounds and/or estimate likely grid connection dates.</p> <p><strong>2. Attribution</strong></p> <p>TZ-SAM is made available under a Creative Commons Attribution Non-Commercial 4.0 International License (CC-BY-NC-4.0). Attribution to TransitionZero is required. You must also clearly indicate if you have made any changes to the TZ-SAM dataset and what these are. Please refer to the suggested citation formats:</p> <ul> <li>“TransitionZero Solar Asset Mapper, TransitionZero, May 2024 release.”</li> <li>“TZ-SAM, TransitionZero, May 2024 release.”</li> <li>“TransitionZero (2024) Solar Asset Mapper.”</li> </ul>
Symphony pre-built single-cell reference atlases
<p>Pre-built Symphony reference objects that can be downloaded and used to map new query datasets.</p> <p>The Symphony algorithm is used to perform reference mapping to these atlases. </p> <ul> <li>Preprint: <a href="https://www.biorxiv.org/content/10.1101/2020.11.18.389189v2">https://www.biorxiv.org/content/10.1101/2020.11.18.389189v2</a></li> <li>Usage: <a href="https://github.com/immunogenomics/symphony">https://github.com/immunogenomics/symphony</a></li> </ul> <p><strong>References available for download:</strong></p> <ol> <li>10x PBMCs Atlas (pbmcs_10x_reference.rds)</li> <li>Pancreatic Islet Cells Atlas (pancreas_plate-based_reference.rds)</li> <li>Fetal Liver Hematopoiesis Atlas (fetal_liver_reference_3p.rds)</li> <li>Healthy Fetal Kidney Atlas (kidney_healthy_fetal_reference.rds)</li> <li>T cell CITE-seq atlas (tbru_ref.rds)</li> <li>Cross-tissue Fibroblast Atlas (see <a href="https://sandbox.zenodo.org/record/772596#.YOqMiBNKhTY">here</a>)</li> <li>Cross-tissue Inflammatory Immune Atlas (<a href="https://sandbox.zenodo.org/record/888445#.YV9In2ZKijB">here</a>)</li> <li>Tabula Muris Senis (FACS) Atlas (TMS_facs_reference.rds)</li> </ol> <p>To read in a reference into R, one may simply execute: reference = readRDS('path/to/reference_name.rds')</p> <p>Note: To be able to map query datasets into the reference UMAP coordinates, you must also download the corresponding 'uwot_model' file and set the reference$save_uwot_path.</p>
TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision
<p>This repository contains the silver standard dataset and code for the paper "TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision".</p> <p>The file "heuristic_uniq_terms_nd.txt" contains the list of terms used as the heuristic and the file "natural_disasters_ssd_tweetids.tsv" contains the tweet ids in the silver standard dataset. </p> <p>To hydrate the tweets, you can use tools like twarc or Social Media Mining toolkit - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362951/</p>
Unmanned Aerial Vehicle Image Dataset of the Built Environment for 3D reconstruction (UAVID3D)
<p>Unmanned Aerial Vehicles (UAV) provide increased access to unique types of urban imagery traditionally not available. Advanced machine learning and computer vision techniques when applied to UAV RGB image data can be used for automated extraction of building asset information and if applied to UAV thermal imagery data can detect potential thermal anomalies. However, these UAV datasets are not easily available to researchers, thereby creating a barrier to accelerating research in this area. </p> <p>To assist researchers with added data to develop machine learning algorithms, we present UAVID3D (Unmanned Aerial Vehicle (UAV) Image Dataset of the Built Environment for 3D reconstruction). The raw images for our dataset were recorded with a Zenmuse XT2 visual (RGB) and a FLIR Tau 2 (thermal, https://flir.netx.net/file/asset/15598/original/) camera on a DJI Mavic 2 pro drone (https://www.dji.com/matrice-200-series). The thermal camera is factory calibrated. All data is organized and structured to comply with FAIR principles, i.e. being findable, accessible, interoperable, and reusable. It is publicly available and can be downloaded from the Zenodo data repository. </p> <p>RGB images were recorded during UAV fly-overs of two different commercial buildings in Northern California. In addition, thermographic images were recorded during 2 subsequent UAV fly-overs of the same two buildings. UAV flights were recorded at flight heights between 60–80 m above ground with a flight speed of 1 m s and contain GPS information. All images were recorded during drone flights on May 10, 2021 between 8:45 am and 10:30 am and on May 19, 2021 between 2:15 pm and 4:30 pm. Outdoor air temperatures on these two days during the flights were between 78 and 83 degree fahrenheit and between 58 and 65 degree fahrenheit respectively. </p> <p>For the RGB flights, UAV path was planned and captured using an orbital flight plan in PIX4D capture at normal flight speed and overlap angle of 10 degree. Thermal images were captured by manual flights approximately 5 m away from each building facade. Due to the high overlap of images, similarities from feature points identified in each image can be extracted to conduct photogrammetry. Photogrammetry allows estimation of the three-dimensional coordinates of points on an object in a generated 3D space involving measurements made on images taken with a high overlap rate. Photogrammetry can be used to create a 3D point cloud model of the recorded region. UAVID3D dataset is a series of compressed archive files totaling 21GB. Useful pipelines to process these images can be found at these two repositories <a href="https://github.com/LBNL-ETA/a3dbr">https://github.com/LBNL-ETA/a3dbr</a>, and <a href="https://github.com/LBNL-ETA/AutoBFE">https://github.com/LBNL-ETA/AutoBFE</a></p> <p>This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Program, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. </p> <p> </p> <p> </p>
Raisen रायसेन (Madhya Pradesh). Carved lintel built into the tank.
<p>Raisen रायसेन (Madhya Pradesh). Carved lintel built into the central tank, probably 8th century.</p>
A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement
<p>This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.</p>
A word2vec model file built from the French Wikipedia XML Dump using gensim.
<p>A word2vec model file built from the French Wikipedia XML dump using gensim. The data published here includes three model files (you need all three of them in the same folder) as well as the Python script used to build the model (for documentation). The Wikipedia dump was downloaded on October 7, 2016 from https://dumps.wikimedia.org/. Before building the model, plain text was extracted from the dump. The size of that dataset is about 500 million words or 3.6 GB of plain text. The principal parameters for building the model were the following: no lemmatization was performed, tokenization was done using the "\W" regular expression (any non-word character splits tokens), and the model was built with 500 dimensions.</p>
Code and data to support 'Street view imagery for built environment auditing: a systematic review'
<p>Code and data to support the manuscript entitled 'Street view imagery for built environment auditing: a systematic review'</p>
Gene embeddings used in GenePT: A Simple But Hard-to-Beat Foundation Model for Genes and Cells Built From ChatGPT
<p>These are the pulled NCBI (and UniProt, when applicable) summaries of genes, as well as the corresponding OpenAI text embeddings (text-embedding-ada-002 and text-embedding-3-large) computed on the summaries. See methods details in Chen and Zou (2024+).</p> <p>The unzipped folder contains four different files: </p> <ol> <li>NCBI_summary_of_genes.json (NCBI gene card summary of human genes)</li> <li>NCBI_UniProt_summary_of_genes.json (NCBI gene card and UniProt protein (when applicable) summary of human genes)</li> <li>GenePT_gene_embedding_ada_text.pickle (a dictionary of numpy array where gene names (upper case) are keys and text-embedding-ada-002 embeddings of the summary in 1. are the values)</li> <li>GenePT_gene_protein_embedding_model_3_text.pickle (a dictionary of numpy array where gene names (upper case) are keys and text-embedding-3-large embeddings of the summary in 1. are the values)</li> </ol> <p>Reference:</p> <p>Chen YT, Zou J. (2024+) GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPT. bioRxiv preprint: <a href="https://www.biorxiv.org/content/10.1101/2023.10.16.562533v1">https://www.biorxiv.org/content/10.1101/2023.10.16.562533v1</a>.</p>
Pre-built Swiss-Calliope sector-coupled energy model
<div> <h3>Swiss-Calliope prebuilt model</h3> <div>The model consists of Switzerland and its neighbours, as described in <a href="https://doi.org/10.1016/j.enconman.2024.118426" target="_blank" rel="noopener">Mellot et al., 2024</a>. Switzerland's heating and transport sectors are modelled on top of its electricity sector.</div> <br> <div>The model is ready to be loaded into Calliope, for 2016--2018. This prebuilt was specifically designed for the study of Switzerland's winter deficit, but is easily modifiable for any other analysis. Refer to Calliope's <a href="https://calliope.readthedocs.io/en/stable/" target="_blank" rel="noopener">documentation</a> for information on how to do this.</div> <br> <div>To run the same scenarios as for the Swiss winter deficit analysis, you need to do the following steps. Note that these scenarios were ran on ETH's Euler cluster which uses the slurm batch system. You can otherwise just adapt the following shell scripts to run the scenarios on other systems.</div> <div>1. Set up the conda environment with the correct version of calliope <code>conda env create -f environment.yaml</code>. On slurm systems you may also need to load gurobi <code>module load new gurobi/9.0.0</code>.</div> <div>2. Run the baseline scenarios, i.e. those corresponding to the EP2050+ configuration, by running <code>sh run_baselines 4</code>, where 4 corresponds to the time resolution.</div> <div>3. Once these runs are finished, run the python file <code>python read_baselines_and_fix_neighbours.py</code>. This will fix Switzerland's neighbouring countries' installed capacities for the next scenarios.</div> <div>4. Then you may run the study's scenarios by running <code>sh run_initial_scenarios.sh 4</code>, and the sensitivity analysis scenarios by running <code>sh run_sensitivies.sh 4</code>.</div> <div> </div> <div>The model's units are GW, GWh, Million euros, and Million kilometers.</div> </div>
Word2Vec Models built from a Collection of French 20th-Century Novels
<p>The models were trained using the Gensim library for Python, developed by Radim Rehurek, in 2017. All models are based on the same collection of 20th century French novels that covers the period from 1900 to 2010, with a large range of authors and genres respresented. The collection contains approximately 1,200 novels and about 60 million tokens.</p> <p>The models were created using the SGNS (Skip-Gram with Negative Sampling) architecture, the context window was always of size of 6 + 6 around the target word, and the texts were lemmatised and POS-tagged beforehand. POS-Tags remain attached to each token (as in "souris_nom"). Other parameters vary by model: some have 200, some have 300 dimensional vectors; the minimum frequency of the words in the model varies with values of 50, 100 and 200, something which influences the size of the vocabulary and the size of the model. </p>
Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options
<p><strong>Customised pre-built Sector-coupled Euro-Calliope Model - Focus on the power sector and additional SPORES options</strong></p> <p><em>Based on the <a href="https://zenodo.org/record/5774988#.YqwqYDJByUk">pre-built Sector-coupled Euro-Calliope model</a> developed by Bryn Pickering</em></p> <p>This model is pre-packaged and ready to be loaded into Calliope, based on 2015 input data. To run the model as done in the associated publication you will need to do the following:</p> <ol> <li>Install a specific conda environment to be working with the correct version of Calliope ( <code>conda env create -f requirements.yml</code> )</li> <li>Run the model including only those scenarios that relate to the power sector and SPORES</li> </ol> <p> </p> <p><strong>Main and parallel batches of SPORES</strong></p> <p>To facilitate this second point and the reproduction of results, you'll find some pre-packaged python script with all and only those model scenarios that allow you to run either the "main batch" of SPORES (<code>spores_model_run.py</code>) or any of the "parallel batches" of SPORES (e.g., <code>excl_bio</code> and <code>max_bio</code>, which generate SPORES while minimising and, respectively, maximising bioenergy deployment).</p> <p> </p> <p><strong>Strength of the anchoring to extremes of the decision space</strong></p> <p>To tweak the strength of the anchoring to a specific technology feature, as we do in the paper, you need to modify the <code>euro_calliope/spores.yaml</code> override file. More precisely, you need to change the <code>excl_score</code> parameter in the objective function at the end of the file:</p> <pre><code class="language-bash">max_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': -1} excl_mode.run.spores_options.objective_cost_class: {'spores_score': 1, 'monetary': 0, 'excl_score': 1}</code></pre> <p>A value of 1 (for maximisation) or -1 (for minimisation) is the default by which we generate the primary results in the paper. By changing it to 0.1, you can reproduce as well the secondary results that we use as a sensitivity for a "weaker anchoring" to extreme technology features of the decision space.</p> <p><br> <strong>Weight-assignment method</strong></p> <p>Finally, to change the weight-assignment method, you need to modify the <code>euro_calliope/eurospores/model.yaml</code> file. More precisely, the <code>scoring_method</code> parameter, which can be one of the following: <code>integer</code>, <code>relative_deployment</code>, <code>random</code> or <code>evolving_average</code>.</p> <pre><code class="language-bash">run.spores_options.scoring_method: integer</code></pre> <p> </p> <p><strong>Hard-coded changes to be aware of</strong></p> <p>The files in this model theoretically allow accounting for all energy sectors (power, heat, transport, industry). Yet, we subset the analysis in the associated publication to only the power sector. To this end, we have modified the original electricity demand file (<code>euro_calliope/eurospores/electricity-demand.csv</code>).</p> <p>In fact, the original file did not account for the fraction of electricity associated with heat, transport or industry consumption, which was instead allocated to sector-specific demand files. In such a way, the model was free to decide whether to electrify these sectoral demands or not. In the present study, instead, we wanted to run our analysis based on the current electricity demand, inclusive of the currently electrified sector-specific demand. Therefore, we have replaced the original file with a new one that includes the present-day electricity demand, with no subtractions.</p> <p>If you want to run the analysis for all sectors, unlike we do in the study, you'll first need to recover the original file. You'll quickly find it in the same folder, named as <code>__electricity-demand.csv</code>.</p> <p><br> <strong>Summary of results from the paper</strong></p> <p>The folder <code>paper_summary_results</code> features some CSV files that summarise the results we obtained for our study across all the different tested search strategies.</p>
Рис. 7. ГнезΑо ΑаΛьневосточного аиста, распоΛоженное на гнезΑовой пΛатформе, установΛенной на опоре ΑЭП-500 в Серышевском районе Амурской обΛасти Fig. 7. The Oriental stork nest, built on the special platform installed on a 500 kV power transmission pole in the Seryshevsky district of the Amur region in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019
Рис. 7. ГнезΑо ΑаΛьневосточного аиста, распоΛоженное на гнезΑовой пΛатформе, установΛенной на опоре ΑЭП-500 в Серышевском районе Амурской обΛасти Fig. 7. The Oriental stork nest, built on the special platform installed on a 500 kV power transmission pole in the Seryshevsky district of the Amur region
Mixed salt systems in the built environment - charge balance calculations
<p>A large database of salt mixtures found in the built environment and a charge balance calculations toolkit. Output of the <a href="http://predict.kikirpa.be/">PREDICT</a> project predominantly including measurements of Belgian historic sites collected by KIK-IRPA Monuments Lab.</p> <p>Contents:</p> <ul> <li>Full integrated database and charge balance calculation sheet, including raw ion concentrations and balanced outputs (.xlsx)</li> <li>Sample integrated database (25 mixtures) and charge balance calculation sheet, including raw ion concentrations and balanced outputs (.xlsx)</li> <li>R scripts for charge balance calculations (.R)</li> <li>Full set of raw ion concentrations for the R scripts (.txt)</li> <li>Sample of 25 raw ion concentrations for the R scripts (.txt)</li> <li>Full set of balanced outputs from the R script (.txt)</li> <li>Sample of 25 balanced outputs from the R script (.txt)</li> </ul>
Map of built-up expansion ("nedbygging") over Norway 2017-2022 version 2
<p>Version 2 of the dataset https://zenodo.org/records/10566644</p> <p>Changes from first version include:</p> <ul> <li>added crowdsourced verification labels to the dataset gathered from the interactive app (link below) explained here: https://www.nina.no/Om-NINA/Aktuelt/Nyheter/article/kartlegg-nedbygging-av-natur-selv</li> <li>added the year of change crowdsourced labels</li> <li>added the type of built-up expansion as labelled by the NRK team</li> </ul> <p>Data can be viewed interactively here: <a href="https://nina.earthengine.app/view/nedbygging">https://nina.earthengine.app/view/nedbygging</a></p> <h4>(see Norwegian description below)</h4> <h2> 1. Dataset Information</h2> <p> - Title: Map of built-up expansion over Norway 2017-2022</p> <p> - Author(s): Zander Venter (NINA), Mads Nyborg Støstad (NRK), Ruben Solvang (NRK), Anne Linn Kumano-Ensby (NRK), Su Thet Mon (NRK)</p> <p> - Contact Information: zander.venter@nina.no</p> <p> - Date of Data Generation: 06.01.2024</p> <p> - Version: 1</p> <p> - Description: This is the dataset used in the <a href="https://www.nrk.no/dokumentar/xl/nrk-avslorer_-44.000-inngrep-i-norsk-natur-pa-fem-ar-1.16573560">NRK article</a> published on 06.01.2024. The data contains polygons outlining potential “nedbygging” (hereafter translated to “built-up expansion” in English) events between 2017 and 2022 over Norway. The built-up expansion polygons were identified using a combination of Sentinel-2 satellite imagery, a fully convolutional neural network (a type of AI model) from Google called <a href="https://dynamicworld.app/">Dynamic World</a> and NINA’s time series analysis thereof. The method to create the map will be published by NINA at a later date. The original map was created by NINA, but NRK performed some post-processing which included joining some polygons which were part of the same built-up expansion event (e.g. a long road). It is important to note that the map is a result of AI and has errors in it. Therefore, users are encouraged to read the sections on data quality and usage information below. Users can refer to <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a> for details on the scientific best practice which the NRK journalists followed to ensure that their reported area estimates in the article were not biased. In summary, the map is wrong 18% of the time. Users should expect to find that on average 1 in 5 square meter is incorrectly identified as built-up expansion. There are also many instances of built-up expansion which will be missed in the map such as forestry road development, building of small cabins etc. </p> <h2> 2. File Details</h2> <p> - Format: Shapefile (.shp, .shx, .dbf, .prj)</p> <p> - Size: 13.27 MB</p> <h2> 3. Geospatial Information</h2> <p> - Coordinate System: EPSG:32632, UTM zone 32N</p> <p> - Spatial Resolution: 10m</p> <p> - Geographical Coverage: Norway mainland (excludes Svalbard)</p> <p> - Temporal Coverage: 2017 to 2022</p> <h2> 4. Data Content</h2> <p> - Attributes Included:</p> <p> - *id*: unique identity number for each polygon</p> <p> - *undersøkt*: whether the polygon has been investigated manually using visual interpretation of orthophotos. “ja” = “yes” and “nei” = “no”</p> <p> - *<span><span>undersøkt_source*: whether the data was collected by the NRK team or the crowdsourcing effort</span></span></p> <p><span><span> - *</span></span><span><span>kategori_1*: the type of built-up expansion labelled by the NRK team - see Google Translate for translations</span></span></p> <p><span><span> - *year*: the year in which the built-up expansion occurred as defined by the crowdsourcing volunteers</span></span></p> <p> - *ai_feil*: whether the AI model method correctly (“riktig”) or incorrectly (“feil”) identified natural habitat conversion to built-up surface. Values where *undersøkt* == “nei” are labelled as “ikke_verifisert” </p> <h2> 5. Data Quality</h2> <p> - Accuracy: As described above, the false positive rate of the map was 18% based on 500 locations used for map validation and accuracy assessment. We did not quantify a false negative rate and balanced accuracy estimates because this would have required a denser sample for manual verification. Therefore, it is likely that there are many instances of built-up expansion that our map does not capture. After the formal accuracy assessment using the 500 stratified random points, NRK verified additional polygons (total of 3875) in the dataset during their investigative journalism workflow. Although these were not collected in a systematic manner, then can still be useful for some downstream tasks such as exploring what causes the AI model to misidentify built-up expansion.</p> <p> - Validation Methods: A design-based approach was used to quantify map accuracy and estimate uncertainty around the resulting area estimate reported in the NRK article. The details of this method are reported in <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>. This approach quantifies the error in the AI-derived map, and corrects for this using a stratified area estimator. Therefore, the total built-up expansion of 208 km<2> reported in the NRK article has been bias-corrected. We also quantified 95% confidence intervals around this are estimate of 9.8 km<2>. It is important to note that the validation approach was conducted on individual Sentinel-2 pixels of 10x10m and not at the polygon level. Therefore, we did not quantify the error in the precision of the polygon shape in terms of capturing the full extent of a given built-up expansion event.</p> <h2> 6. Usage Information</h2> <p> - Use Limitations: Considering the map error described above, users should proceed with caution when analysing the map to derive area statistics or overlays with other maps. As described in <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>, simply adding the areas of the polygons (or “pixel counting” with maps formatted as images) without accounting for the error in the map will lead to incorrect area statistics. We recommend that users validate the map for their municipality or study area before proceeding with analysis. It is likely that the margin of error is highly variable between municipalities. For example, although we have not quantified it, we noticed many AI mistakes in mountainous regions due to snow and ice interference and therefore high-altitude municipalities might have more errors than low-altitude ones.</p> <p> </p> <h1> Norwegian description: </h1> <h2> 1. Datasettinformasjon</h2> <p> - Tittel: Kart over nedbygging over Norge 2017-2022</p> <p> - Forfatter(e): Zander Venter (NINA), Mads Nyborg Støstad (NRK), Ruben Solvang (NRK), Anne Linn Kumano-Ensby (NRK), Su Thet Mon (NRK)</p> <p> - Kontaktinformasjon: zander.venter@nina.no</p> <p> - Dato for datagenerering: 06.01.2024</p> <p> - Versjon: 1</p> <p> - Beskrivelse: Dette er datasettet som brukes i <a href="https://www.nrk.no/dokumentar/xl/nrk-avslorer_-44.000-inngrep-i-norsk-natur-pa-fem%20-ar-1.16573560">NRK-artikkelen</a> publisert 06.01.2024. Dataene inneholder polygoner som skisserer potensiell nedbygging mellom 2017 og 2022 over Norge. Nedbyggingsområdene ble identifisert ved hjelp av en kombinasjon av Sentinel-2 satellittbilder, et fullstendig konvolusjonelt nevralt nettverk (en type KI-modell) fra Google kalt <a href="https://dynamicworld.app/">Dynamic World</a> og NINAs tidsserie-analyse av dette. Metoden for å lage kartet vil bli publisert av NINA på et senere tidspunkt. Det originale kartet ble laget av NINA, men NRK utførte en del etterbehandling som inkluderte sammenføyning av noen polygoner som var en del av den samme oppbygde utvidelseshendelsen (f.eks. en lang vei). Det er viktig å merke seg at kartet er produsert ved hjelp av kunstig intelligens og inneholder feil. Derfor oppfordres brukere til å lese avsnittene om datakvalitet og bruksinformasjon nedenfor. Brukere kan referere til <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024) </a>for detaljer om den vitenskapelige beste praksisen som NRK-journalistene fulgte for å sikre at deres rapporterte arealstatistikk i artikkelen er korrekt. Oppsummert er 18 % av arealet i kartet feil. Brukere bør forvente å finne at i gjennomsnitt 1 av 5 kvadratmeter er feilaktig identifisert som nedbygging. Det er også mange tilfeller av nedbygging som som ikke vil vises i kartet, som skogsveiutbygging, bygging av småhytter mm.</p> <h2> 2. Fildetaljer</h2> <p> - Format: Shapefil (.shp, .shx, .dbf, .prj)</p> <p> - Størrelse: 13,27 MB</p> <h2> 3. Geospatial informasjon</h2> <p> - Koordinatsystem: EPSG:32632, UTM-sone 32N</p> <p> - Rolig oppløsning: 10m</p> <p> - Geografisk dekning: Norges fastland (ekskluderer Svalbard)</p> <p> - Tidlig dekning: 2017 til 2022</p> <h2> 4. Datainnhold</h2> <p> - Attributter inkludert:</p> <p> - *id*: unikt identitetsnummer for hver polygon</p> <p> - *undersøkt*: om polygonet er undersøkt manuelt ved bruk av visuell tolkning av ortofoto.</p> <p> - *undersøkt_source*: om dataene er samlet inn av NRK-teamet eller crowdsourcing-innsatsen</p> <p> - *kategori_1*: typen nedbygging merket av NRK-teamet </p> <p> - *year*: året hvor nedbygging skjedde som definert av crowdsourcing</p> <p> - *ai_feil*: om AI-modellmetoden var “riktig” eller “feil”. Verdier der *undersøkt* == «nei» er merket som «ikke_verifisert»</p> <h2> 5. Datakvalitet</h2> <p> - Nøyaktighet: Som beskrevet ovenfor var andelen falske positive punkter i kartet 18 % basert på 500 steder (prøveflater) brukt for kartvalidering og nøyaktighetsvurdering. Vi kvantifiserte ikke andelen falske negative punkter og balanserte nøyaktighetsestimater, fordi dette ville ha krevd en tettere stikkprøvedensitet for manuell verifisering. Derfor er det sannsynlig at det er mange tilfeller av nedbygging som kartet vårt ikke fanger opp. Etter den formelle nøyaktighetsvurderingen ved bruk av 500 stratifiserte tilfeldige prøveflater, verifiserte NRK ytterligere polygoner (totalt 3875) i datasettet i løpet av deres journalistiske undersøkelser. Selv om disse ikke ble samlet inn på en systematisk måte, kan de fortsatt være nyttige for noen oppfølgingsanalyser som å utforske hva som får AI-modellen til å feilidentifisere nedbygging.</p> <p> - Valideringsmetoder: En designbasert tilnærming («design-based area estimation» på engelsk) ble brukt for å kvantifisere kartnøyaktighet og estimere usikkerhet rundt det resulterende arealestimatet rapportert i NRK-artikkelen. Detaljene ved denne metoden er forklart i <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>. Denne tilnærmingen kvantifiserer feilen i det KI-avledede kartet, og korrigerer for dette ved å bruke en stratifisert arealestimator. Derfor er den totale bebygde utvidelsen på 208 km<2> som er rapportert i NRK-artikkelen, skjevhetskorrigert. Vi kvantifiserte også 95 % konfidensintervaller rundt dette er estimat på 9,8 km<2>. Det er viktig å merke seg at valideringstilnærmingen ble utført på individuelle Sentinel-2 piksler på 10x10m og ikke på polygonnivå. Derfor kvantifiserte vi ikke feilen i presisjonen til polygonformen når det gjelder å fange opp hele omfanget av en gitt oppbygget ekspansjonshendelse.</p> <h2> 6. Bruksinformasjon</h2> <p> - Bruksbegrensninger: Med tanke på kartfeilen beskrevet ovenfor, bør brukere fortsette med forsiktighet når de analyserer kartet for å beregne områdestatistikk eller overlegg med andre kart. Som beskrevet i <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>, ved å legge til områdene til polygonene (eller «pikseltelling» med kart formatert som bilder) uten å ta hensyn for feilen i kartet vil føre til feil arealstatistikk. Vi anbefaler at brukere validerer kartet for sin kommune eller studieområde før de går videre med analyse. Det er sannsynlig at feilmarginen varierer mye mellom kommuner. For eksempel, selv om vi ikke har kvantifisert det, la vi merke til mange AI-feil i fjellområder på grunn av snø- og is, og derfor kan kommune med mye fjell ha flere feil enn de langs kysten (for eksempel).</p>
Figure 1. Experimental unit model built with a 150 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)
Figure 1. Experimental unit model built with a 150 mm PVC pipe and coupling, used in the maintenance of yellow clam (Amarilladesma mactroides) broodstock in the laboratory.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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