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5,738 results for “Standards”
The biome inventory – standardizing global biogeographical land units
<p class="MsoNormal">The subdivision of the Earth's terrestrial surface into different biomes, ecozones, bio-climatic realms or other large ecological land units is an essential reference for global biogeographical and ecological studies. Various classification schemes exist. They differ significantly in terms of the considered criteria for classification and the underlying methodology of class assignments. Evident divergences between global biome concepts are elusive, weakening hereon based analyses and assumptions. Compilation and standardization are essential for obtaining a framework that enables the comparison of different products. To address this need, we created a catalogue of standardized categorial maps comprising 31 different global products based on various methodological approaches. Those were processed individually to facilitate their use in large-scale biogeographical and ecological analyses.</p>
Chains from Ni58 Standard Sample
<p>Markov chain Monte Carlo chains from the analysis of neutron reflectometry data form a Ni58 sample. This has been used in the ISIS Neutron and Muon Source Training School multiple times. </p>
Methane production data for fed-batch assays amended with graphene oxide and two standard substrates
<p>The spreadsheet contains all the data generated using the Automatic Methane Potential Tests System (AMPTS) for fed-batch experiments containing graphene oxide (GO) at 0, 5, 10, and 20 mg of GO per g of volatile solids (VS).</p> <p>Also, the dataset is divided accordingly to the two substrates used, i.e., glucose (G) and microcrystalline cellulose (C).</p>
Updating splits, lumps, and shuffles: Reconciling GenBank names with standardized avian taxonomies
Abstract Biodiversity research has advanced by testing expectations of ecological and evolutionary hypotheses through the linking of large-scale genetic, distributional, and trait datasets. The rise of molecular systematics over the past 30 years has resulted in a wealth of DNA sequences from around the globe. Yet, advances in molecular systematics also have created taxonomic instability, as new estimates of evolutionary relationships and interpretations of species limits have required widespread scientific name changes. Taxonomic instability, colloquially "splits, lumps, and shuffles," presents logistical challenges to large-scale biodiversity research because (1) the same species or sets of populations may be listed under different names in different data sources, or (2) the same name may apply to different sets of populations representing different taxonomic concepts. Consequently, distributional and trait data are often difficult to link directly to primary DNA sequence data without extensive and time-consuming curation. Here, we present RANT: Reconciliation of Avian NCBI Taxonomy. RANT applies taxonomic reconciliation to standardize avian taxon names in use in NCBI GenBank, a primary source of genetic data, to a widely used and regularly updated avian taxonomy: eBird/Clements. Of 14,341 avian species/subspecies names in GenBank, 11,031 directly matched an eBird/Clements; these link to more than 6 million nucleotide sequences. For the remaining unmatched avian names in GenBank, we used Avibase's system of taxonomic concepts, taxonomic descriptions in Cornell's Birds of the World, and DNA sequence metadata to identify corresponding eBird/Clements names. Reconciled names linked to more than 600,000 nucleotide sequences, ~9% of all avian sequences on GenBank. Nearly 10% of eBird/Clements names had nucleotide sequences listed under 2 or more GenBank names. Our taxonomic reconciliation is a first step towards rigorous and open-source curation of avian GenBank sequences and is available at GitHub, where it can be updated to correspond to future annual eBird/Clements taxonomic updates.
LMAS Test Dataset - BMock12 Community Standards
<p>The twelve bacterial replicons of the <a href="https://www.nature.com/articles/s41597-019-0287-z#Abs1">BMock12 Community Standards</a> were used as reference. It includes the following strains:</p> <table> <thead> <tr> <th>Species</th> <th>Sample ID</th> <th>Depth of Coverage (x)</th> </tr> </thead> <tbody> <tr> <td>Muricauda sp. ES.050</td> <td>2615840527</td> <td>618.76</td> </tr> <tr> <td>Thioclava sp. ES.032</td> <td>2615840533</td> <td>78.32</td> </tr> <tr> <td>Cohaesibacter sp. ES.047</td> <td>2615840601</td> <td>170.59</td> </tr> <tr> <td>Propionibacteriaceae bacterium</td> <td>2615840646</td> <td>31.90</td> </tr> <tr> <td>Marinobacter sp. LV10R510-8</td> <td>2615840697</td> <td>447.83</td> </tr> <tr> <td>Marinobacter sp. LV10MA510-1</td> <td>2616644829</td> <td>135.05</td> </tr> <tr> <td>Psychrobacter sp. LV10R520-6</td> <td>2617270709</td> <td>425.47</td> </tr> <tr> <td>Micromonospora echinaurantiaca</td> <td>2623620557</td> <td>14.91</td> </tr> <tr> <td>Micromonospora echinofusca</td> <td>2623620567</td> <td>18.19</td> </tr> <tr> <td>Micromonospora coxensis</td> <td>2623620609</td> <td>0.02</td> </tr> <tr> <td>Halomonas sp. HL-4</td> <td>2623620617</td> <td>507.08</td> </tr> <tr> <td>Halomonas sp. HL-93</td> <td>2623620618</td> <td>579.87</td> </tr> </tbody> </table> <p>The raw sequence data of the mock communities, with an even distribution of species, is available at:</p> <ul> <li><a href="https://www.ebi.ac.uk/ena/browser/view/SRR8073716?show=reads">SRR8073716</a></li> </ul> <p>For the sake of a less resource-intense evaluation we downsampled to have 20% of the reads available in the original sample and further processed only these. The main challenges of this data set are possibly to assemble the genomes of the three <em>Micromonospora</em> spp. and the two <em>Halomonas</em> spp. strains, which have an ANIb >0.84 and 0.98, respectively. The two <em>Marinobacter</em> spp. have an ANIb of 0.78.</p>
LMAS Test Dataset - ZymoBIOMICS Microbial Community Standards
<p>The <a href="https://zenodo.org/record/4588970#.YEeA83X7RhE">eight bacterial genomes and four plasmids of the ZymoBIOMICS Microbial Community Standards</a> were used as reference. It contains tripled complete sequences for the following species:</p> <ul> <li><em>Bacillus subtilis</em></li> <li><em>Enterococcus faecalis</em></li> <li><em>Escherichia coli</em> <ul> <li><em>Escherichia coli</em> plasmid</li> </ul> </li> <li><em>Lactobacillus fermentum</em></li> <li><em>Listeria monocytogenes</em></li> <li><em>Pseudomonas aeruginosa</em></li> <li><em>Salmonella enterica</em></li> <li><em>Staphylococcus aureus</em> <ul> <li><em>Staphylococcus aureus</em> plasmid 1</li> <li><em>Staphylococcus aureus</em> plasmid 2</li> <li><em>Staphylococcus aureus</em> plasmid 3</li> </ul> </li> </ul> <p>It also downloads the raw sequence data of the mock communities, with an even and logarithmic distribution of species:</p> <ul> <li><a href="https://www.ebi.ac.uk/ena/browser/view/ERR2984773">ERR2984773</a> - Evenly distributed real sample</li> <li><a href="https://www.ebi.ac.uk/ena/browser/view/ERR2935805">ERR2935805</a> - Log distributed real sample</li> </ul> <p>A set of simulated samples were generated from the genomes in the ZymoBIOMICS standard though the <a href="https://github.com/HadrienG/InSilicoSeq">InSilicoSeq sequence simulator</a> (version 1.5.2), including both even and logarithmic distribution, with and without Illumina error model. The number of read pairs generated matches the number of read pairs in the real data for each distribution. The following samples are available in Zenodo:</p> <ul> <li><strong>ENN</strong> - Evenly distributed sample with no error model</li> <li><strong>EMS</strong> - Evenly distributed sample with Illumina MiSeq error model</li> <li><strong>LNN</strong> - Log distributed sample with no error model</li> <li><strong>LHS</strong> - Log distributed sample with Illumina HiSeq error model</li> </ul> <p><a href="https://doi.org/10.5281/zenodo.4588970">DOI Dataset</a></p>
MEDDOPROF corpus: complete gold standard annotations for occupation detection in medical documents in Spanish
<p><strong>UPDATE 27/09/2022: </strong>A complete normalization of all mentions in the corpus to SNOMED CT has been added to the 'meddoprof-norm.tsv' file.</p> <p><strong>Description</strong></p> <p>This repository contains the complete MEDDOPROF Gold Standard, a collection of 1,844 clinical cases in Spanish with annotations for occupations, working statuses and activities. MEDDOPROF is a Shared Task celebrated in 2021 that explores the application of natural language processing to occupational health. If you'd like to learn more, please visit: <a href="https://temu.bsc.es/meddoprof">https://temu.bsc.es/meddoprof</a>.</p> <p><strong>Folder and File Structure</strong></p> <p>The corpus' files are presented in the format used by the annotation tool brat. That is, for each clinical case there is a .txt file with the text and a .ann file with its corresponding annotations.</p> <p><em>- meddoprof-ner/</em></p> <p>Clinical cases annotated with these labels: PROFESION (PROFESSION), SITUACION_LABORAL (WORKING_STATUS) or ACTIVIDAD (ACTIVIDAD).</p> <p><em>- meddoprof-class/</em></p> <p>Clinical cases with the same annotations as 'meddoprof-ner' but with these labels instead: PACIENTE (patient), FAMILIAR (family member), SANITARIO (health professional) or OTRO (other).</p> <p><em>- ner_class_joint/</em></p> <p>Clinical cases with both levels of annotation (ner and class) joint (that is, a mention classified as as PROFESOR in meddoprof-ner and as PACIENTE in meddoprof-class would be PROFESION-PACIENTE here).</p> <p><em>- meddoprof-norm.tsv</em></p> <p>Tab-separated file (.tsv) with the mapping of each mention in the corpus to ESCO and SNOMED CT. The file has five columns: filename, mention text, span, ESCO code and SNOMED code.</p> <p>Additionally, two files with the filenames of the train and test partitions are included.</p> <p> </p> <p><strong>Please cite if you use this resource:</strong></p> <p>Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and Martin Krallinger. NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. In Procesamiento del Lenguaje Natural, 67. 2021.</p> <pre><code>@article{meddoprof, title={NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts}, author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Miranda-Escalada, Antonio and Brivá-Iglesias, Vicent and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, volume = {67}, year={2021}, issn = {1989-7553}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6393}, pages = {243--256} }</code></pre> <p><strong>Related Resources:</strong></p> <p>- <a href="http://temu.bsc.es/meddoprof">Web</a></p> <p>- <a href="https://doi.org/10.5281/zenodo.4694768">Training Data</a></p> <p>- <a href="https://doi.org/10.5281/zenodo.4889776">Test set</a></p> <p>- <a href="https://zenodo.org/record/4722741">Codes Reference List</a> (for MEDDOPROF-NORM)</p> <p>- <a href="https://zenodo.org/record/4720833">Annotation Guidelines</a></p> <p>- <a href="https://doi.org/10.5281/zenodo.4524658">Occupations Gazetteer</a></p> <p> </p> <blockquote> <p>MEDDOPROF is part of the IberLEF 2021 workshop, which is co-located with the SEPLN 2021 conference. For further information, please visit <a href="https://temu.bsc.es/meddoprof/">https://temu.bsc.es/meddoprof/</a> or email us at encargo-pln-life@bsc.es</p> <p>MEDDOPROF is promoted by the Plan de Impulso de las Tecnologías del Lenguaje de la Agenda Digital (Plan TL) and the Spanish government's 2020 Proyectos de I+D+i RTI Tipo A (AI4PROFHEALTH - DESCIFRANDO EL PAPEL DE LAS PROFESIONES EN LA SALUD DE LOS PACIENTES A TRAVES DE LA MINERIA DE TEXTOS (PID2020-119266RA-I00)).</p> </blockquote>
Dataset for use with "Technology standards vs. carbon taxes: comparing emissions reductions and system costs for US decarbonization"
<p>This sqlite file contains the input data and results used in "Technology standards vs. carbon taxes: comparing emissions reductions and system costs for US decarbonization." The file is a version of the nine region Open Energy Outlook database for use with the Temoa model. The database is continually updated. Updated versions can be found in the Temoa GitHub repository, https://github.com/TemoaProject/oeo.</p>
Isobaric specific heat of gases and vapors under standard conditions
<p><strong>Isobaric specific heat of gases and vapors under standard conditions</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Gases are treated as a large collection of tiny particles subject to the laws of physics. Their properties are attributed primarily to the motion of the molecules and can be explained by the kinetic theory of gases. It is not obvious that this should be the case, and for many years a static picture of gases was instead espoused, in which the pressure, for instance, was attributed to repulsive forces between essentially stationary particles pushing on the container walls. How the kinetic-molecular picture finally came to be universally accepted is a fascinating piece of scientific history and is discussed briefly below in the section Kinetic theory of gases. Any theory of gas behaviour based on this kinetic model must also be a statistical one because of the enormous numbers of particles involved. The kinetic theory of gases is now a classical part of statistical physics and is indeed a sort of miniature display case for many of the fundamental concepts and methods of science. Such important modern concepts as distribution functions, cross sections, microscopic reversibility, and time-reversal invariance have their historical roots in kinetic theory, as does the entire atomistic view of matter.</p> <p>Gases and vapors, Isobaric specific heat (kilojoules per kelvin per kilogram)</p> <p>Acetone 1.47</p> <p>Acetylene 1.69</p> <p>Air 1.01</p> <p>Alcohol (ethanol) 1.88</p> <p>Alcohol (methanol) 1.93</p> <p>Ammonia 2.19</p> <p>Argon 0.520</p> <p>Benzene 1.09</p> <p>Blast furnace gas 1.03</p> <p>Bromine 0.25</p> <p>Butane 1.67</p> <p>Carbon dioxide 0.844</p> <p>Carbon monoxide 1.02</p> <p>Carbon disulphide 0.67</p> <p>Chlorine 0.48</p> <p>Chloroform 0.63</p> <p>Ethane 1.75</p> <p>Ether (diethyl ether) 1.95</p> <p>Ethylene 1.53</p> <p>Helium 5.19</p> <p>Hydrochloric acid 0.795</p> <p>Hydrogen 14.32</p> <p>Hydrogen chloride 0.8</p> <p>Hydroxyl 1.76</p> <p>Krypton 0.25</p> <p>Methane 2.22</p> <p>Natural gas 2.34</p> <p>Neon 1.03</p> <p>Nitric oxide 0.995</p> <p>Nitrogen 1.04</p> <p>Nitrogen tetroxide 4.69</p> <p>Nitrous oxide 0.88</p> <p>Oxygen 0.919</p> <p>Propane 1.67</p> <p>Propene (propylene) 1.5</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Isobaric specific heat of metals and metalloids under standard conditions
<p><strong>Isobaric specific heat of metals and metalloids under standard conditions</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>A metal is any of a class of substances characterized by high electrical and thermal conductivity as well as by malleability, ductility, and high reflectivity of light. Approximately three-quarters of all known chemical elements are metals. The most abundant varieties in the Earth's crust are aluminum, iron, calcium, sodium, potassium, and magnesium. The vast majority of metals are found in ores (mineral-bearing substances), but a few such as copper, gold, platinum, and silver frequently occur in the free state because they do not readily react with other elements. Metals are usually crystalline solids. In most cases, they have a relatively simple crystal structure distinguished by a close packing of atoms and a high degree of symmetry. Typically, the atoms of metals contain less than half the full complement of electrons in their outermost shell. Because of this characteristic, metals tend not to form compounds with each other. They do, however, combine more readily with nonmetals (for example, oxygen and sulfur), which generally have more than half the maximum number of valence electrons. Metals differ widely in their chemical reactivity. The most reactive include lithium, potassium, and radium, whereas those of low reactivity are gold, silver, palladium, and platinum. The high electrical and thermal conductivities of the simple metals (namely the non-transition metals of the periodic table) are best explained by reference to the free-electron theory. According to this concept, the individual atoms in such metals have lost their valence electrons to the entire solid, and these free electrons that give rise to conductivity move as a group throughout the solid. In the case of the more complex metals (namely the transition elements), conductivities are better explained by the band theory, which takes into account not only the presence of free electrons but also their interaction with so-called d electrons. The mechanical properties of metals, such as hardness, ability to resist repeated stressing (fatigue strength), ductility, and malleability, are often attributed to defects or imperfections in their crystal structure. The absence of a layer of atoms in its densely packed structure, for example, enables a metal to deform plastically, and prevents it from being brittle. A metalloid, in chemistry, is an imprecise term used to describe a chemical element that forms a simple substance having properties intermediate between those of a typical metal and a typical nonmetal. The term is normally applied to a group of between six and nine elements (boron, silicon, germanium, arsenic, antimony, tellurium, and possibly bismuth, polonium, astatine) found near the center of the P-block or main block of the periodic table. There is no single property which can be used to unambiguously identify an element as a metalloid. Since most metalloids tend to display semiconducting properties in at least one of their allomorphic modifications, the class might reasonably be extended to also include gray silicon (which, unlike white silicon, is a semiconductor rather than a metal) and the graphite form of carbon (which, unlike the diamond form, is a semimetal rather than an insulator). Chemically, metalloids correspond to atoms having intermediate electronegativities and an ability to display a range of both positive and negative oxidation states in their compounds.</p> <p>Metals and metalloids, Isobaric specific heat (kilojoules per kelvin per kilogram)</p> <p>Aluminum 0.91</p> <p>Antimony 0.21</p> <p>Barium 0.20</p> <p>Beryllium 1.83</p> <p>Bismuth 0.13</p> <p>Cadmium 0.23</p> <p>Calsium 0.63</p> <p>Carbon Steel 0.49</p> <p>Cast Iron 0.46</p> <p>Cesium 0.24</p> <p>Chromium 0.46</p> <p>Cobalt 0.42</p> <p>Copper 0.39</p> <p>Gallium 0.37</p> <p>Germanium 0.32</p> <p>Gold 0.13</p> <p>Hafnium 0.14</p> <p>Indium 0.24</p> <p>Iridium 0.13</p> <p>Iron 0.45</p> <p>Lanthanum 0.195</p> <p>Lead 0.13</p> <p>Lithium 3.57</p> <p>Lutetium 0.15</p> <p>Magnesium 1.05</p> <p>Manganese 0.48</p> <p>Mercury 0.14</p> <p>Molybdenum 0.25</p> <p>Nickel 0.44</p> <p>Niobium 0.27</p> <p>Osmium 0.13</p> <p>Palladium 0.24</p> <p>Platinum 0.13</p> <p>Plutonium 0.13</p> <p>Potassium 0.75</p> <p>Rhenium 0.14</p> <p>Rhodium 0.24</p> <p>Rubidium 0.36</p> <p>Ruthenium 0.24</p> <p>Scandium 0.57</p> <p>Selenium 0.32</p> <p>Silicon 0.71</p> <p>Silver 0.23</p> <p>Sodium 1.21</p> <p>Strontium 0.30</p> <p>Tantalum 0.14</p> <p>Thallium 0.13</p> <p>Thorium 0.13</p> <p>Tin 0.21</p> <p>Titanium 0.54</p> <p>Tungsten 0.13</p> <p>Uranium 0.12</p> <p>Vanadium 0.39</p> <p>Yttrium 0.30</p> <p>Zinc 0.39</p> <p>Zirconium 0.27</p> <p>Wrought Iron 0.50</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Anatolian Ceremonial Standard from Alacahoyuk
Ceremonial Standard from Alacahoyuk. 2nd hal of 3rd Millenium BC. 3D model made by photogrammetry with a repoduction of the standart from the Museum of Anatolian Civilizations in Ankara. Source: Objaverse 1.0 / Sketchfab
Standardizing ID-Labels for seaweed samples used for chemical composition analyses and refinery processes in Nordic and European research projects
<p><strong>Introduction</strong></p> <p>Seaweed samples can be divided into two groups:</p> <ol> <li>Small samples (½-3 kg wet weight (ww)) often used for chemical content analyses including seasonal variation and testing different cultivation conditions or preliminary lab scale experiments on storage, extraction, separation, fermentation, etc.</li> <li>Larger samples (>3 kg ww) for lab- or pilot scale experiments on storage, extraction, separation, fermentation, etc.</li> </ol> <p>Seaweed samples will always have the following information-tracks:</p> <ol> <li><strong>Sample Code: </strong>A ID containing the most important information and the sample code will follow the sampled biomass from harvest to final research results.</li> <li><strong>Seaweed Processing Code:</strong> The sample code will be extended with 8 digits and 1 letter if processing of biomass occurs.</li> <li><strong>Batch Number:</strong> A code describing details about the harvest and origin of the seaweed.</li> <li><strong>Sample Overview:</strong> An Excel file describing all details about the sample: first <strong>sample code</strong>, then species, grinding, freezing/drying specifications, seeding and harvesting information, planed aim of the sample (e.g. polysaccharides), place stored, seaweed processing details, analyse results, etc. Maintained by the sample provider.</li> </ol>
Synthesis and scaled-up of standard and active Belite-Alite-Ye'elimite clinker (BAY)
<p><strong>ABSTRACT:</strong> Clinkers with the coexistence of belite-alite-ye´elimite (BAY) are considered as eco-friendly cements due to the reduced CO<sub>2</sub> emissions released during their manufacture. All BAY clinkering parameters have been optimized: raw materials, mineralizers/activators, clinkering conditions and scaled-up methodology. Two types of clinkers were prepared and scaled-up to have ~2 kg: i) standard-BAY (28.4 wt% of b-belite, 41.5 wt% of alite and 17.4 wt% of ye'elimite) by adding CaF<sub>2</sub> and ZnO to the raw mixture; and ii) active-BAY (18.1 wt% of α'<sub>H</sub>-belite, 42.1 wt% of b-belite, 15.8(2) wt% of alite, and 7.7(2) wt% of ye'elimite) by using optimized amounts of CaF<sub>2</sub>, ZnO and B<sub>2</sub>O<sub>3</sub>. The microscopy[P1] study has revealed that primary particle size of belite is larger (~10 µm) in the active clinker than in the standard one (~5 µm). Moreover, ferrite and alite have incorporated most of the Zn and F in their structures in both clinkers, while belite (b and α’<sub>H</sub> forms) has preferentially incorporated sulphur.</p>
Extended computing integrated curricula scored for K-12 CS standards
<p>Integrated computing curricula combine learning objectives in computing with those in another discipline, like literacy, math, or science, to give all students experience with computing, typically before they must decide whether to take standalone CS courses. One goal of integrated computing curricula is to provide an accessible path to an introductory computing course by introducing computing concepts and practices in required courses. This paper analyzed integrated computing curricula to determine which CS practices and concepts they teach and how extensively and, thus, how they prepare students for later computing courses. The authors conducted a content analysis to examine primary and lower secondary (i.e., K-8) curricula that are taught in non-CS classrooms, have explicit CS learning objectives (i.e., CS+X), and that took 5+ hours to complete. Lesson plans, descriptions, and resources were scored based on frameworks developed from the K-12 CS Framework, including programming concepts, non-programming CS concepts, and CS practices. The results found that curricula most extensively taught introductory concepts and practices, such as sequences, and rarely taught more advanced content, such as conditionals. Students who engage with most of these curricula would have no experience working with fundamental concepts, like variables, operators, data collection or storage, or abstraction in the context of a program. While this focus might be appropriate for integrated curricula, it has implications for the prior knowledge that students should be expected to have when starting standalone computing courses.</p>
MFMET webinar - 01. The role of Metrology and Standardization in microfluidic technology development
<p>This video presents the MFMET project, and it introduces metrology and standardization in the context of microfluidics.<br> <br>Further readings can be found:<br> <br><a href="https://mfmet.eu">https://mfmet.eu</a><br> <br><a href="../communities/mfmet">https://zenodo.org/communities/mfmet</a></p> <p> </p> <p>The project (20NRM02 MFMET) have received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
Replication data for "Learning When to Quit: An Empirical Model of Experimentation in Standards Development"
<p>This is the replication data for the article "Learning When To Quit: An Empirical Model of Experimentation in Standards Development," published in the <em>American Economic Journal: Microeconomics</em>. </p> <p>A detailed description of the data construction can be found in the Data Appendix (Section E in the Online Appendix published with the article's supplementary material).</p> <p>The file _readme-LWTQ-data.xls contains a description of the variables.</p> <p>If you use the data, cite the paper!</p> <p><strong>Ganglmair, Bernhard, Timothy Simcoe, and Emanuele Taranntino (2024): "Learning When To Quit: An Empirical Model of Experimentation in Standards Development," <em>American Economic Journal: Microeconomics</em>, forthcoming<em>.</em></strong></p>
Circular economy standards for automotive electronics: from a state of the art analysis to a new pre-standardization document
<p>The Standardization Toolkit, developed by <a href="https://www.uni.com/en/" target="_blank" rel="noopener">UNI (Italian Standards Body)</a> within the TREASURE project, is an open reserach tool designed to simplify reserach of international and european standards (EN and ISO) relevant to circular economy strategies in the automotive sector.</p> <p>This dynamic tool, powered by Microsoft Power BI, provides an extensive list of national, European, and international standards, detailing key information such as standard numbers, URLs for document retrieval, titles, publication years, and scopes.</p> <p>The Toolkit maps out 73 standards, including 61 current standards and 12 works in progress, overseen by prominent technical committees such as ISO and CEN. It covers crucial areas like life-cycle assessment, substance determination, and decision support frameworks, offering a valuable resource for stakeholders aiming to implement effective and sustainable practices.</p> <p>The Toolkit is freely available at <a href="https://www.treasureproject.eu/standardization-toolkit/">this link</a>.</p> <p>This publication includes the dateset behind the Standardization Toolkit, while D8.4 "Standardization Toolkit" describes the previous version of the mapping and the methodology behind the state of the art analysis.</p> <p>It has also been uploaded the D8.5 "Strategic standardization roadmap" which includes a description of the Standardization Toolkit, and the new pre-standardization document (CEN Workshop Agreement - CWA) developed within Treasure project. </p> <p> </p> <p><strong>Disclaimer:</strong> The standards retrieved from the Standardization Toolkit cannot be fully downloaded. The Toolkit serves to simplify the search for these standards and to provide an overview of what has been mapped during the TREASURE project’s standardization activities.</p>
FHIRed MOTU data. FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients.
<h3>Dataset presented in the article "MOTU on FHIR: A 10-year data collection on the clinical rehabilitation pathway of 1006 trans-femoral amputees".</h3> <p>Data has been anonymised prior the publication. The data has been standardized in Fast Healthcare Interoperability Resources (FHIR) data standard. </p> <p>This work has been conducted within the framework of the MOTU++ project (PR19-PAI-P2).</p> <p>This research was co-funded by the Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, DARE - DigitAl lifelong pRevEntion initiative, code PNC0000002, CUP: (B53C22006450001) and by the Italian National Institute for Insurance against Accidents at Work (INAIL) within the MOTU++ project (PR19-PAI-P2).</p> <p>Authors express their gratitude to all the AlmaHealthDB Team.</p> <h2>Instruction MOTU-to-FHIR Importer</h2> <div> <div> <p>The repository includes a Docker Compose setup for importing the MOTU dataset into a HAPI FHIR server, formatted as NDJSON following the HL7 FHIR R4 standards.</p> <h3>Prerequisites</h3> <p>Before you begin, ensure you have the following installed:</p> <ul> <li><a href="https://www.docker.com/get-started/">Docker </a></li> <li><a href="https://docs.docker.com/compose/install/">Docker Compose</a></li> <li><a href="https://www.python.org/downloads/">Python >=3.7</a> </li> <li><a href="https://pypi.org/project/requests/">Requests Python Library</a></li> </ul> </div> <div> <h3>How to run</h3> <ol> <li>First, unzip the <code>dataset</code> directory containing the NDJSON files.</li> <li>Open a terminal or command prompt in the root directory of this repository.</li> <li>Run the command <code>docker-compose up</code> in the terminal to start the Docker containers.</li> <li>Once the containers are up and running, open another terminal window in the root directory of this repository.</li> <li>Run the command <code>python main.py</code> in the terminal to start the data import process.</li> <li>After the import process is complete, you can access the HAPI FHIR server by opening a web browser and navigating to <a href="http://localhost:8082" target="_blank" rel="nofollow noreferrer noopener">http://localhost:8082</a>.</li> </ol> <p> </p> </div> </div>
GCAM-China-v6 standard test scenarios (Mar 2024)
<p>GCAM-China-v6 test scenarios, including a reference scenario and a net-zero scenario till 2100.</p> <p>These are xml outputs of original scenarios, users can import them into GCAM scenario database using the "import scenario" function in model interface.</p>
Data for LIGO operates with quantum noise below the Standard Quantum Limit
<p>This repository hosts data sets used to create all the figures in the paper "LIGO operates with quantum noise below the Standard Quantum Limit". </p> <p> </p> <p>Version 1: initial upload</p> <p>Version 2: Add README.txt and MCMC set-up code</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.