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7,378 results for “case”
Baseline data for SDM of the SIM4NEXUS case of the Netherlands
<p>This dataset consists of the baseline data for the SDM of the SIM4NEXUS case of the Netherlands. Most data is based on year data that has been equally distributed over 12 months per year. For optional extension to monthly data, the data display already monthly data starting at December 2009 until December 2050 i.e. 493 points in time. The data include time series for socio-economic indicators, land use, food production, energy use, climate and water. The socio-economic system includes data on population and gross domestic product (GDP) per capita. The land system includes data on four main land uses, namely built-up areas, agriculture (with areas for food, energy crops and fodder crops production) , nature areas (non-forest, forest not for biomass production, forest for biomass production) , and areas for renewable energy production like wind mills and solar power fields. The food system includes plant-based (food crops like cereals and vegetables and fruit) and animal-based food production (based on the herds of cattle, pigs and poultry) in terms of protein. In addition, the animal- and plant-based protein requirements of Dutch consumers are also estimated. The energy system has an energy production and an energy demand part. Energy demand is determined for the domestic sector (i.e. households) based upon population and households’ demands for renewable and non-renewable energy. For the other economic sectors (agriculture, manufacturing industry, transportation and services sector) the demands for renewable and non-renewable energy are determined by GDP per sector and the energy intensity of the sectors. Energy supply is divided into non-renewable energy production and renewable energy production. Non-renewable energy sources include energy from coal, natural gas, oil and nuclear. The renewable energy consist of energy from wind (onshore and offshore), solar (on buildings and solar power fields), biomass and other sources (innovations like hydrogen or geothermic power). The energy of biomass there are 7 sources of biomass: energy crops, crop residues, manure, organic household waste, organic waste from public areas, waste water and timber residues. Timber residues are largely imported for large-scale use of co-firing in coal power plants and bio-based activities in the manufacturing industries. The water system has two parts: water quality which are the agricultural emissions of nitrogen and phosphorus to water, and water quantity i.e. agricultural water demand for irrigation and livestock drinking water. Finally, the climate system is divided into non-agricultural GHG emissions, and agricultural emissions. The non-agricultural GHG emissions are based on the GHG emissions from non-renewable energy production and non-energy related GHG emissions per economic sector except for agriculture. The agricultural GHG emissions relate to GHG emissions from livestock production, crop production and wetlands.</p>
Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"
<p>The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration.</p> <p>Country codes: ALPHA-3<br> Unit: Megawatt (electric) (interval average values, i.e. MWh/h)</p> <p><strong>Generation technology types</strong></p> <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> <p><strong>Consumption technology types</strong></p> <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>
Data and Code Accompanying the Study on "Benefits of reflex prediction: A case study of Western Kho-Bwa"
<p>Cite the source of the dataset as:</p> <blockquote> <p>Timotheus A. Bodt and Johann-Mattis List (to appear): Benefits of reflex prediction: A case study of Western Kho-Bwa. Diachronica.</p> </blockquote>
Dataset: Effects of high altitude reservoirs on the structure and function of lotic ecosystems: a case study in Italy
<p>Supporting data for "Effects of high altitude reservoirs on the structure and function of lotic ecosystems: a case study in Italy"</p> <p>Macroinvertebrate community composition</p> <p>Water chemical characteristics</p> <p>Daily mean water temperature and daily temperature variations</p> <p>Results of the leaf bags experiments</p>
Volcano-Independent Seismic Recognition (VI.VSR): case studies with 'geoStudio' graphical interface
<p>Video-documentation of the <strong><em><a href="https://zenodo.org/record/3594080#.X9JP-XVudQJ">geoStudio</a></em> Volcano-Independent Seismic Recognition (VI.VSR) software</strong>, supported by the <a href="https://cordis.europa.eu/project/id/749249"><strong><em>VULCAN.ears</em></strong></a> EU-funded project (H2020-MSCA-IF-2016 Grant) and referenced in the <em>"Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears project" - </em>(Cortés et al., Frontiers in Earth Sciences, 2021) article. <em><strong>VI.VSR aim</strong></em> is to automatically detect and classify volcano-seismic events in any volcano 'V' of the world by models built by other volcanoes data. This provides volcano-seismic catalogs of the given volcano 'V', without the fuss of designing a custom recognition system for it, being specially useful in real-time monitoring scenarios.</p> <p>The material includes 2 VDs:</p> <ol> <li><em>"VI.VSR+geoStudio_intro.mp4"</em> -> introducing the main idea and concepts behind the Volcano-Independent Seismic Recognition (VI.VSR) and presenting <em>geoStudio</em> and its role in the whole <em>VULCAN.ears</em> platform.</li> <li><em>"VI.VSR.by.geoStudio_case.studies.mp4"</em> -> running the VI.VSR case studies presented in the <em>(Cortés et al., 2021)</em> manuscript.</li> </ol> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).</p>
Massive Open Online Courses(MOOCs) and why they fail. A case of HarvardX
<p>Massive open online courses (MOOCs) are free online courses open for everyone to enroll. MOOCs are attractive because they attempt to level the playing field and provide opportunities for students from all walks of life and from different countries to study "together" at the comfort of their own homes and this is facilitated by leading professors from renowned universities at little to no cost. They are sold as an on-demand online education to accommodate conflicting family and work responsibilities which offer students the flexibility to study and do life. Schools like University of Maryland University College were among the first universities to accept MOOCs for credit, since then more institutions have welcomed the idea of providing online education for free. MOOCs sadly have simply not lived up to their mission to transform the educational system around the world and once again we see how a good free initiative fails.<br> <br> Despite of the popularity of MOOCs, employers do not regard certifications from MOOCs as valid credentials neither as an alternative to traditional educational institutions, in essence, it is pretty useless, unless you already possess a degree from an accredited traditional educational institution. We will analyze the first two years of Harvard's attempt at offering open and free courses to all, we will look at its successes, failures and the way forward.</p> <p>The first two years 2012, 2013 Harvard offered only 5 courses. Out of the <strong>290,498</strong> registrations, <strong>49.2%</strong> of the students registered for Introduction to Computer Science, <strong>16.9%</strong> for Justice, <strong>13% </strong>for Health in numbers, <strong>12.2%</strong> for Human Health and Global environmental change and finally <strong>8.7%</strong> registered for the Ancient Greek Hero. When we break this down by gender, males outnumber females in overall registration by <strong>106,149</strong>. Analyzing individual course registration, males still outnumber females, we see this in all the courses listed and even more significantly in the Computer science subject.</p> <p>The goal of MOOCs is to make world class education accessible to all persons including students from developing countries. We do see however that the United States leads the pack in student enrollments (which is a developed country) instead of the targeted countries from the global south. <strong>78.8%</strong> of students who registered for the courseware were between ages <strong>16 and 34</strong>, we can be reasonably confident and say that most of the students are either millennials or generation z. <strong>68%</strong> of the students who registered for the courseware already possess either a bachelor's, master's or a doctorate degree(s).</p> <p>MOOCs have grown in number and popularity but failed to live up to its mission of democratizing education around the world. Out of the <strong>290948</strong> students who registered for the HarvardX open courseware in 2012 and 2013, only <strong>65,261</strong> completed the courseware representing <strong>22.4%</strong> of total student enrollments. Out of the <strong>65,261</strong> who completed the courseware only <strong>5728</strong> received certifications, distressingly representing <strong>2%</strong> of total student enrollments.</p> <p>Certification rate in general is disappointingly low but we want to see if this is true for all demographics in this dataset. We will analyze individual courses, student age, level of education as well as the country of origin to perhaps diagnose the problem of low completion and low certification. First, out of <strong>143,266</strong> students who registered for Introduction to Computer Science, only <strong>1%</strong> were certified. Out of <strong>49,293</strong> students who registered for Justice, <strong>4%</strong> were certified. Out of <strong>37709</strong> students who registered for Health in numbers, <strong>5%</strong> were certified. Out of the <strong>35509</strong> students who registered for Human health and global environmental change, <strong>2%</strong> were certified. Out of the <strong>25171</strong> students who registered for the Ancient Greek hero, only <strong>1%</strong> received certifications.</p> <p>Secondly, about 73% of the students who completed the courseware and received certification already have a bachelor's, master's or doctorate degree(s). This shows that students accessing the courseware regard it merely as a resource to fill in gaps to supplement skills already learnt in a four year traditional institution. Additionally about 67% of those who received certifications fall within ages 16 to 30 which is the targeted age group, the only problem is that there are simple not enough certifications for students within this age group compared to the number of enrollments. Out of the 229301 students enrollments who fall within this age group, only 3822 received certifications representing 1.67% of enrollments.</p> <p>We can agree that MOOCs began conceptually as a great idea but fails in implementation, the simple truth is that attitudes towards initiatives labelled 'free' in certain parts of the world encourage apathy. We see that in most countries from every continent except for Europe. Secondly, some students need external discipline to stay committed and complete the courseware but because the stakes are low and there is nothing to lose, most students simply do not see the reason to do so. Thirdly, a large number of those who registered for these courses as well as those who received certifications were from developed countries, failing to democratize education for those who need it the most, students from the global south.</p>
Connecting U.S. Supreme Court Case Information and Opinion Authorship (SCDB) to Full Case Text Data (CAP), 1791-2011
<p>This dataset was constructed to connect the rich metadata created by the Supreme Court Database (SCDB) to the Caselaw Access Project (CAP) full-text court opinion data. Since the SCDB includes only substantive opinions, it is necessarily a subset of the full range of opinions available through CAP.</p> <p>There are two parts to this data: the map connecting each SCDB ID to its corresponding CAP case number, and a more advanced (but error-prone) version in which the authorship of each opinion text identified for the case in CAP is attributed to the Justice who wrote it. Each of these data products have been hand-corrected to the best of this author's ability.</p> <p><strong>SCDB-CAP map</strong></p> <p>The SCDB->CAP map began as a relatively straightforward automated matching process, based on the US Reports citation for each case as expressed in both SCDB and CAP. Slightly over 80% of SCDB entries found a single CAP data match this way. From there, the data was entirely hand-corrected, with non-matches or duplicate matches individually investigated and manually corrected.</p> <p>Some SCDB entries simply could not be matched to an appropriate CAP text. Initially, the entirety of US Reports volume 44 was missing, but with the help of CAP staff, the volume was located as having been filed in the New York jurisdiction rather that the United States jurisdiction. The case numbers were then added to the map, but until the volume is relocated to the United States jurisdiction, it may be necessary to also incorporate the New York jurisdiction in full text analysis so that the cases from volume 44 can be searched. 108 more missing cases are from US Reports volume 131, which was a "catch up" volume published in the 19th century. These catch-up cases, many heard by the Supreme Court decades prior, were numbered with lowercase roman numerals instead of the ordinary numbers, which is almost certainly why CAP's software dismissed the catch-up section as prefatory material. Many of the rest of the errors seem largely to be examples where the SCDB project recognized a separate court action that CAP did not. Perhaps most of these seem to have been later rehearings for a case previously decided, which in the 19th century particularly were commonly reported out at the end of the first decision text. While SCDB sometimes gave these subsequent but related actions a separate SCDB entry, CAP seems to have largely incorporated them as part of the text of the main case. Additionally, there were a few that simply could not be found, despite a careful look through each database as well as the original US Reports and sometimes adjacent volumes. Finally, the cases were only matched up through the 2011 court term. After the 2011 term, the mismatches between CAP and SCDB were extensive and frequently seemed impossible to resolve.</p> <p>Even so, with the manual correction, the overall error rate is low. Of 28,304 cases, only 191 do not have a match, and of those, 108 are contained within the vol. 131 "catch up" volume. Since most of the rest are extremely short subsequent actions that were separately noted by SCDB, the effect of these non-matched cases would seem to be small in most cases.</p> <p>The typical use case would be that the researcher would generate some kind of results based on searching in the CAP full text, then could use the CAP ID to look up the SCDB ID in the map. With the SCDB ID, of course, the rich metadata from the SCDB can then be connected to each result as needed.</p> <p><strong>Opinion authorship</strong></p> <p>Being able to use the rich metadata of SCDB in conjunction with a case's full text is exciting, but it immediately prompts a further question -- what if the texts could be attributed directly to the Justices who authored them? SCDB produces its data in two forms; one is "case centered," where each record represents one case, and the other is "justice centered," in which each record is the vote of one Justice in one case. CAP, in turn, breaks the total text of the case into distinct opinions, and tries to attribute those opinions to their authors by scraping a string of text from the raw input. Therefore, the challenge was to connect these two sources at the opinion level.</p> <p>Connecting the opinions, like connecting the cases, involved an initial match by machines, followed by manual correction and revision. In this case, the scope of the manual effort was much larger than that posed by the case-level connection, and more errors were noted in both SCDB and CAP.</p> <p>The matching process involved a number of steps. First a list of opinions was generated from the CAP data, then matched to SCDB using the SCDB-CAP connector data described above. (Thus, a case without a CAP match in the SCDB-CAP data will not appear in the opinion author data either.) CAP opinions were numbered in the order they were encountered in each CAP case JSON object, and these numbers are used to distinguish the opinions.</p> <p>Next, a round of automatic matching was performed. If there was only one opinion, and only one author listed in the SCDB data, then the majority opinion author (as listed in SCDB) was safely assumed to be the author. If there was no author listed in SCDB, "percuriam" was recorded as the author in this data. If there were exactly two opinions and two authors, the process was also straightforward, as the SCDB-identified majority opinion author was assigned to opinion 1, and the remaining author assigned opinion 2.</p> <p>Subsequently, cases with more than two opinions were processed. A potential match (i.e. a "guess") for each opinion in a given case was created by listing each Justice identified by SCDB as having written an opinion in the case. These guesses were then parsed using a semi-automatic procedure with Levenshtein distance fuzzy name matching. With sufficiently conservative parameters, a successful fuzzy match meant that the non-successful guesses for that opinion could be deleted. These sorted guesses were then reviewed manually. Particular care was also taken for any opinion that contained authored opinions by Justices who had similar names (for example, Clark and Black differ by only a single letter). These sorts of cases, as well as instances of co-authorship, were identified and fixed manually.</p> <p>Those opinions whose authorship could not be matched then were fixed by hand. These included some where the CAP author strings were more complicated than SCDB's strict interpretation; others where the OCR in CAP which contained the Justice name was especially bad; and a number of others where "Mr. Chief Justice" couldn't be directly matched with an author name by the machine. After this light manual correction, almost 500 opinions with substantial errors remained to be individually investigated in depth, by examining the CAP record, the SCDB record, and images of the US Reports for that case. For these last tough customers, errors in the source data were commonly the cause of matching problems. Typically these were of three kinds: examples where CAP should have split the text but didn't (e.g. 2 opinions together in one opinion entry in CAP); examples where SCDB either did not identify or mis-identified an author (such as attributing it to Swayne when it was written by Miller); and examples of non-valid opinions (such as where CAP mistakenly split the opinion too early, leaving an opinion fragment).</p> <p>For these errors, a system of codes was created in the author field to signal the error type so that researchers can be suitably cautious. The error code is always at the beginning of the field and is followed by a comma and the names of each author, separated by a comma with no space to facilitate parsing. Note also that co-authors are listed as comma-separated names in this same field with no error code. Researchers will probably want to disaggregate this field to create duplicate records with each individual author for most purposes. The justice number field also contains information about all justices authoring the opinion but the error codes have been omitted here.</p> <ul> <li>!C -- error: multiple opinion texts combined (i.e. CAP splitting error)</li> <li>!X -- error: unattributed or misattributed opinion (not listed in SCDB as writer)</li> <li>!D -- error: extra opinion that should be deleted, i.e. not a valid opinion</li> <li>!W -- error: listed as Writers by SCDB, but should be co-authors</li> </ul> <p> </p> <p><strong>Data file structure</strong></p> <p>"scdb_cap-051820.tsv" is a Tab-separated data file containing 5 columns: SCDB ID, CAP ID, US Reports citation, case date, and case name (the latter three from the SCDB data).</p> <p>"scdb-cap-opinion-authorship_051920.tsv" is a Tab-separated data file containing seven columns: SCDB ID, CAP ID, US Reports citation, case name, opinion number in the case, opinion author, and SCDB justice ID. See above for caveats about disaggregating and error codes in fields six and seven.</p> <p><strong>Errors</strong></p> <p>It is likely that errors remain in this data, and it is also hoped that some of the errors beyond the author's immediate control might be fixed in the upstream data so that they can be corrected here. Authors would be grateful for error reports, and also reports of errors fixed, if any.</p> <p> </p>
Supplementary Data – Indian Covid19 Cases at Different Temperature
<p>Supplimentary Datasets of A STATEWISE STATISTICAL ANALYSIS ON COVID19 CASES IN INDIA AT DIFFERENT TEMPERATURE derived from Curve Expert 1.4 and Excell Spreadsheet . </p>
Excavator-generated information from Linux drivers (Decoder Use-Case A)
<p>This dataset is released as part of DECODER's D6.2 deliverable. It contains the information generated by the Excavator tool for easing the verification with Frama-C of the watchdog and ethernet Linux drivers that have been selected as Use-Case A of the project.</p>
Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects
<p>The dataset contains analyzed projects and modules for the paper "Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects". The contents are the following:</p> <ul> <li><a href="/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/manual-studied-modules.csv?versionId=8258b59c-3f88-487b-a4a3-007cb362a44a">manual-studied-modules.csv</a>: Manually analyzed Maven modules mentioned in Section 5.2</li> <li><a href="https://zenodo.org/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/projects.zip">projects.zip</a>: Instrumented and Mutated Github Projects. Projects list applied mutation changes, and their dynamic and static call graph.</li> </ul>
Artificial COVID-19 Cases in Paris and Geographic Data Useful for Geomasking
<p>Artificial dataset of addresses of COVID-19 cases in Paris. The dataset was created to test geomasking techniques to be used on the real data collected by the French health administration. The dataset was used in the paper "Geographically Masking Addresses to Study COVID-19 Clusters" by Walid Houfaf-Khoufaf and Guillaume Touya. The dataset contains the following files:</p> <ul> <li>roads_paris_IGN.shp contains the road lines from IGN France in Paris;</li> <li>buildings_paris_IGN.shp contains the building polygons from IGN France in Paris (useful to aggregate points to building groups);</li> <li>faces.shp contains the blocks built from the roads (useful to aggregate points to blocks);</li> <li>ban_75.shp contains all the address points in Paris from the open BAN database.</li> <li>artificial_COVID_cases.csv contains the artificial COVID cases generated from 3 months in 2020 in the Paris area.</li> </ul> <p> </p>
A dataset of human and Plasmodium falciparum genotypes in severe malaria cases from The Gambia and Kenya
<p>This data release contains human and <em>Plasmodium falciparum</em> malaria genotypes from the article:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The release contains genotypes from human and <em>Plasmodium falciparum</em> genetic variants, genotyped using blood samples from 4,171 children ascertained with severe symptoms of malaria at the Royal Victoria Teaching Hospital (now the Edward Francis Small Teaching Hospital), The Gambia, and from the Kilifi District Hospital (now Kilifi County Hospital), Kenya in the period 1995-2009.</p> <p>An accompanying set of association test summary statistics has also been released on Zenodo (doi: <a href="https://doi.org/10.5281/zenodo.5722497">10.5281/zenodo.5722497</a>). Please see <a href="http://www.malariagen.net/resource/32">www.malariagen.net/resource/32</a> for full details of other resources associated with the above manuscript.</p> <p> </p>
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
On the airburst of large meteoroids in the Earth's atmosphere. The Lugo bolide: reanalysis of a case study
<p>Seismic data of the Lugo bolide of January 19, 1993.<br /> </p>
Identification at local and global scale: a case for using the Compact URI (CURIE) for life science data
<p>Panel A) A Local Resource Identifier (LRI) is not suited to global scale identification because of inevitable collisions: “9606” corresponds to a Pubmed article, a CGNC gene, a PubChem chemical, as well as an NCBI taxon (<em>Homo sapiens</em>), a BOLD taxon (<em>Bombycilla</em> <em>cedrorum</em>), and a GRIN taxon (<em>Catha</em> <em>edulis</em>)</p> <p>Panel B) Prefixing is often used to indicate the source of an LRI, but prefixes themselves are often undocumented and collide.</p> <p>Panel C) Prefixes may exist in alternate forms. When all of the alternates are not known, collapsing equivalent identifiers is tedious and incomplete.</p> <p>Panel D) CURIE syntax addresses these issues by having a prefix whose relationship with a resolving namespace is clearly documented.</p>
Jisc Research Data Shared Service metadata focus group use cases
<p>Dataset of use cases collected between July and October 2016 during a series of metadata focus groups conducted with a number of the <strong>Research Data Shared Service</strong> pilots who volunteered for the process.</p> <p>This dataset is available in two formats (including an open format) with the same content: 180 use cases in the following user story structure: </p> <ul> <li><strong>As a</strong></li> <li><strong>Theme</strong></li> <li><strong>I want </strong></li> <li><strong>So that</strong></li> <li><strong>Comments</strong></li> </ul> <p>The .xlsx file contains additional formatting grouping the use cases by theme, role, data and community.</p>
Dataset supporting the paper: "A Case Study on the Parametric Occurrence of Multiple Steady States"
<p>Dataset supporting the paper:</p> <p>Russell Bradford, James H. Davenport, Matthew England, Hassan Errami, Vladimir Gerdt, Dima Grigoriev, Charles Hoyt, Marek Košta, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber. A Case Study on the Parametric Occurrence of Multiple Steady States. In Proceedings of ISSAC ’17, Kaiserslautern, Germany, July 25-28, 2017, 8 pages. ACM, 2017.<br> https://doi.org/http://dx.doi.org/10.1145/3087604.3087622<br> Preprint: https://arxiv.org/abs/1704.08997</p> <p>We provide all the accompanying material for the main symbolic computations in Section 2.1</p> <p>###############<br> Section 2.1.1<br> ###############</p> <p>The computations were carried out within the following setting:</p> <p>1. Compiled and functional Reduce system r3606 is available and the<br> environment variable $trunk points to the trunk of Reduce source tree<br> (e.g. ~/reduce-algebra/trunk). To compile and install Reduce see:<br> http://redlog.eu/reduce-wiki/index.php/Installation</p> <p>2. Compiled and functional QEPCAD B v1.69 is installed and it is<br> possible to call QEPCAD from Reduce (via rlqepcad). To compile and<br> install QEPCAD see: https://www.usna.edu/CS/qepcadweb/B/QEPCAD.html</p> <p>To reproduce the experiments reported in the paper go:</p> <p>./generate-problems-onevar.py</p> <p>./compute-all.sh --csl ~/reduce-algebra/trunk ./problems-onevar 4</p> <p>./einsetzen.sh</p> <p>A few notes BEFORE YOU RUN:</p> <p>1. To try a different value of k19, edit the file solbiomod26.red.<br> Currently we set k19 = 500.</p> <p>2. It is advised to run these test on a multicore machine, e.g., we<br> used 32 cores (the last argument of ./compute-all.sh script) for<br> trying the 3^11 candidates (einsetzen.sh script).</p> <p>3. The script generate-problems-assignmnets.py (called from<br> einsetzen.sh) creates 3^11 test files!</p> <p>4. The potential values to be tried using interval refinement are<br> described as real algebraic numbers, i.e., pairs of polynomial and<br> isolating interval. These numbers are stored in assignment files<br> (assignment-<var>-<possibility#>) produced by ./gml-to-red.py (called<br> from einsetzen.sh)</p> <p>###############<br> Section 2.1.2<br> ###############</p> <p>These computation were conducted in commercial Computer Algebra System, Maple 2016. We have included the Maple Worksheet file which is annotated to describe the calulations in detail. We also include a pdf printout of the worksheet which those without access to Maple can read.</p>
MultiCaRe: An open-source clinical case dataset for medical image classification and multimodal AI applications
<p>The dataset contains multi-modal data from over 70,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.</p> <p>More than 90,000 patients and 280,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.</p> <p>Refer to the examples showcased in this <a href="https://github.com/mauro-nievoff/MultiCaRe_Dataset">GitHub repository</a> to understand how to optimize the use of this dataset.<br><br>The license of the dataset as a whole is CC BY-NC-SA. However, its individual contents may have less restrictive license types (CC BY, CC BY-NC, CC0). For instance, regarding image filess, 66K of them are CC BY, 32K are CC BY-NC-SA, 32K are CC BY-NC, and 20 of them are CC0.</p>
MINIATURA 6 Housing decisions, behavioral aspects of choices, price expectations and anchoring effect - Polsh case study
<p>The data was created as a result of a survey conducted in accordance with the guidelines: - the survey questionnaire consisted of approximately 30 questions and a form, - the surveyed population was defined as 1,000 households living in a large Polish city (over 450,000 inhabitants), quota selection based on the number of city inhabitants, - CAWI method (online), - completion date: 1 week. The survey was parameterized. Part of the sample is a control trial, part is an experimental trial.</p><p>Dane powstały w wyniku przeprowadzonej ankiety zgodnie z wytycznymi: - kwestionariusz badania składał się z ok. 30 pytań oraz metryczki, - badana zbiorowość określono na 1000 gospodarstw domowych zamieszkałych w dużym mieście Polski (powyżej 450 tys. ludności), dobór kwotowy na podstawie liczby mieszkańców miast, - badanie metodą CAWI (on-line), - termin realizacji 1 tydzień. Ankieta byłą sparametryzowana. Część próby stanowi próba kontrolna, część próba eksperymentalna. </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.