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3,947 results for “Requirements”
QD-AMVA: Evaluating Systems with Queue-Dependent Service Requirements - Data
<p>The data part of this release support the results <br /> presented in the paper <br /> "QD-AMVA: Evaluating Systems with Queue-Dependent Service<br /> Requirements", by G. Casale, J. F. Perez, and W. Wang, accepted <br /> to IFIP Performance 2015. </p> <p>When referring to the dataset or scripts please cite the paper above. </p> <p>The scripts released with the above paper can be found at https://zenodo.org/record/18887</p> <p> </p>
On the alignment of academic publishers’ embargos with H2020 requirements - Dataset
<p> </p> <p>-- ATTENTION PLEASE: RIGHT NOW THE DATASET IS UNDER INDEPENDENT DOUBLE CHECK TO TEST THE PRESENCE OF POSSIBLE ERRORS : PLEASE CONTACT THE AUTHOR FOR FURTHER INFO --</p> <p> </p> <p>This dataset refers to the breifing paper "On the alignment of academic publishers’ embargos with H2020 requirements" (https://nexacenter.org/nexacenterfiles/WoS-Romeo-analysis%20APS-final.pdf) published in the ambit of the European project Pasteur4OA (http://www.pasteur4oa.eu/).</p> <p> </p> <p>We wanted to understand how the publishing behaviour of EU researchers might be affected by the H2020 policy requirement to ensure Open Access (OA) to all articles from EU-funded projects within 6 months for science and engineering projects and 12 months for humanities and social science studies. Many publishers impose an embargo on ‘Green’ Open Access, where researchers deposit their articles in repositories, and these embargoes can be longer than the maximum permitted by the H2020 policy. The issue was whether researchers may have to alter their publishing behaviour or can they continue to publish in journals of their choice and still comply with the H2020 requirements. The following research questions were posed:</p> <p>1) What is the level of compliance of current publishers’ embargo policies with the H2020 requirements?<br> 2) How many journals are compliant with the H2020 requirements?</p> <p>The overall findings were that 90% of publishers used by EU researchers to publish their work and 94% of articles published by EU researchers are compatible with the H2020 Open Access policy requirements. Our conclusion is that only in a small minority of cases – 5-10% – would EU researchers’ normal publishing behaviour run contrary to H2020 rules. </p> <p> </p> <p>The zip file containing the data used, divided by access to the post-prints: </p> <p>- ok Open Access</p> <p>- no Open Access</p> <p>- unclear</p> <p>- OA with restrictions, but compliant to H2020</p> <p>- OA with restrictions, not compliant to H200</p> <p>- OA with restrictions, not clear</p> <p>The data model is available on the paper (https://nexacenter.org/nexacenterfiles/WoS-Romeo-analysis%20APS-final.pdf)</p>
High-resolution analysis of power plant land requirements for GODEEEP
<p>This dataset contains data associated with Mongird et al. (under review). Files include output from the following three analyses found in the paper: (1) Projected power plant siting intersections with US Disadvantaged Communities (DACs), important farmland, and natural areas; (2) onshore wind and solar photovoltaic capacity factor availability under 27 different siting restriction cases, and (3) output from an analysis that determines how many DACs are projected to see both fossil fuel generation retirement and new renewable power plant development. Each of the files associated with these components are described below. </p> <p>For more detailed information please refer to Mongird et al. (under review), "High-resolution analysis of power plant land requirements for the evolving Western United States power grid indicates coordinated land use policies will be essential"</p> <p>Outputs included in this dataset are associated with two different scenarios. Summaries of each of the two scenarios included are provided below. For additional information, see <a href="https://doi.org/10.1016/j.egycc.2023.100117">Ou et al. 2023.</a></p> <h2>Scenario Descriptions</h2> <ul> <li><strong>business-as-usual</strong>: <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the US Inflation Reduction Act (IRA) incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> <li><strong>high renewables</strong>: <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include US IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> </ul> <h2>Data Descriptions</h2> <h3>1. Projected power plant siting intersections</h3> <p><strong>Description</strong></p> <p>These files identify the intersection of projected power plant locations with three types of land: federall identified disadvantaged communities (DACs), important farmland, and land in close proximity to natural areas.</p> <p><strong>Scenario Files:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>bau_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>bau_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>bau_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with important farmland by technology type and Western US state</td> </tr> <tr> <td>hr_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>hr_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>hr_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>state</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type inclusive of turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>technology_simple</td> <td>Power plant technology type excluding turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>layer_name</td> <td>Descriptive name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>layer</td> <td>Name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>total_plants</td> <td>Number of projected power plants of specified technology in specified state under given scenario</td> <td>#</td> </tr> <tr> <td>intersection</td> <td>Number of projected power plant intersections of specified technology in specified state with given layer under given scenario </td> <td>#</td> </tr> <tr> <td>fraction</td> <td>ratio of intersection and total_plants</td> <td>fraction</td> </tr> </tbody> </table> <p> </p> <p><strong>Scenario Difference Analysis Files:</strong></p> <table> <tbody> <tr> <td>difference_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_env_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table style="width: 85.255198%; height: 152px;"> <tbody> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;"><strong>Column</strong></td> <td style="width: 82.845413%; height: 19px;"><strong>Description</strong></td> <td style="width: 4.029241%; height: 19px;"><strong>Units</strong></td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">state</td> <td style="width: 82.845413%; height: 19px;">Name of US state</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">technology</td> <td style="width: 82.845413%; height: 19px;">Power plant technology type</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">layer</td> <td style="width: 82.845413%; height: 19px;">Name of geospatial raster layer used for intersection analysis</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">hr</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the high renewables scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">bau</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the business-as-usual scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 38px;"> <td style="width: 8.458634%; height: 38px;">intersection</td> <td style="width: 82.845413%; height: 38px;">Difference in projected power plant intersections between the high renewables scenario and the business-as-usual scenario</td> <td style="width: 4.029241%; height: 38px;">#</td> </tr> </tbody> </table> <h3> </h3> <h3>2. Projected onshore wind and solar photovoltaic capacity factor availability under 27 siting restriction cases</h3> <p>Description:</p> <p>This file contains results from an analysis on the capability of reaching high renewables scenario solar and wind generation in 2050 under 27 different siting restriction cases.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>capacity_factor_analysis_2050.csv</td> <td>Amount of solar PV or onshore wind generation projected to be available in a given state under a specified siting restriction case </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>region_name</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type (either solar PV or Wind)</td> <td>N/A</td> </tr> <tr> <td>capacity_density_mw</td> <td>Assumed MW per square-km</td> <td>MW</td> </tr> <tr> <td>case</td> <td>Name of siting exclusion case</td> <td>N/A</td> </tr> <tr> <td>total_generation_mwh</td> <td>Projected total generation available given remaining available land after exclusions</td> <td>MWh</td> </tr> <tr> <td>target_generation_mwh</td> <td>Projected target annual generation in 2050 for technology type under high renewables scenario</td> <td>MWh</td> </tr> <tr> <td>gcam_trading_region</td> <td>Name of zonal representation of electricity trading regions as defined in the capacity expansion model</td> <td>N/A</td> </tr> </tbody> </table> <h3> </h3> <h3>3. US DACs that see both fossil fuel generation retirement and new renewable power plant development by 2050</h3> <p><strong>Description:</strong></p> <p>This data contains US census tract GEOIDs that see both new renewable sitings and the retirement of fossil generating resources.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>dac_fossil_retire_analysis_2050.csv</td> <td>List of US census tracts that see both fossil fuel generation retirement and new renewable generation siting by 2050 </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> census_tract</td> <td> US census tract GEOID</td> </tr> <tr> <td>state_name</td> <td>Name of US state</td> </tr> <tr> <td>county_name</td> <td>Name of US county</td> </tr> <tr> <td>scenario</td> <td>scenario name</td> </tr> </tbody> </table> <p> </p> <h2>Funding statement</h2> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p> <h2>Changelog</h2> <p>v1.1</p> <p> The following updates were made following manuscript revision:</p> <ul> <li>"power_density_mw" variable name in `capacity_factor_analysis_2050.csv` file changed to "capacity_density_mw"</li> <li>More estimates are provided in `capacity_factor_analysis_2050.csv` reflecting additional capacity density and turbine hub height assumptions.</li> <li>Scenario naming adjusted to align with manuscript naming</li> </ul>
Crosswalks IUCLID 6 v9 EU PPP Microorganisms - active substance application (product) to Data Requirements
<p>The <strong>Excel file</strong> provides detailed crosswalks from the current Table of Content (ToC) for Microbial Plant Protection Product (PPP) dossier in <a href="https://iuclid6.echa.europa.eu/it/home">IUCLID 6 v.9</a> to Commission Regulation (EU) 283/2013 and Commission Regulation (EU) 284/2013 as amended by Commission Regulation (EU) 2022/1439 & Commission Regulation (EU) 2022/1440.</p> <p>There are two worksheets:</p> <ul> <li><strong>ACTIVE SUBSTANCE</strong> (283-2013): mapping between the current IUCLID working context "EU PPP Microorganisms - active substance information" to the Commission Regulation (EU) No 283/2013 as amended by Commission Regulation (EU) 2022/1439.</li> <li><strong>PRODUCT</strong> (284-2013): mapping between the the current IUCLID working context "EU PPP Microorganisms - active substance application (product)" to the Commission Regulation (EU) No 284/2013 as amended by Commission Regulation (EU) 2022/1440.</li> </ul> <p>The spreadsheets contain the following columns:</p> <ul> <li><strong>Data Requirements Section (Commission Reguation (EU) 2022/1439 or 2022/1440)</strong>: the name of the ToC section (in accordance with the new data requirements).</li> <li><strong>IUCLID section</strong>: the name of the ToC section in IUCLID.</li> <li><strong>Endpoint study record</strong>: name of the document template used to report individual studies of the section. These usually correspond to <a href="https://www.oecd.org/en/topics/sub-issues/assessment-of-chemicals/harmonised-templates.html">OECD Harmonised Templates (OHT)</a>. </li> <li><strong>Endpoint summary</strong>: name of the document template used to report the summary information for the section endpoints.</li> <li><strong>Other IUCLID document</strong>: name of any other document template in IUCLID used to report information of the section. </li> <li><strong>OHT</strong>: number of the OECD Harmonised Template used in the section.</li> <li><strong>Additional context</strong>: fulI IUCLID paths indicating the section of the respective document where information needs to be provided and/or specific values to be indicated. </li> </ul> <p>Note: <span>in cases where an IUCLID document is not included in the updated ToC this will be found in a specific section 'Documents applicable to the former data requirements' which can be found at the end of the dataset.</span></p> <p><strong>Version 5 </strong>includes changes in the table of contents of <a href="https://iuclid6.echa.europa.eu/it/home">IUCLID 6 v9</a>.</p> <p> </p> <p> </p>
Text-fig. 9. Phylogenetic tree indicating the number of required character state changes (steps) under parsimony for various positions of Miranthus gen. nov. in a molecular based backbone tree (see material and methods for additional details). in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications
Text-fig. 9. Phylogenetic tree indicating the number of required character state changes (steps) under parsimony for various positions of Miranthus gen. nov. in a molecular based backbone tree (see material and methods for additional details).
Supplementary Material for "Aiding the Design of Critical Software Systems by Iterative Exploration of Distinct Requirement Violation Scenarios"
<p>This dataset provides artifacts about an industrial case study of a Steer-by-Wire system. It collects models of the system modeled in the open-source Gamma Statechart Composition Framework. You can find more information about the framework here: <a href="https://github.com/ftsrg/gamma">https://github.com/ftsrg/gamma</a>.</p>
Academic publishing requires linguistically inclusive policies
<p>Curated databases of the project "Academic publishing requires linguistically inclusive policies." </p> <p>"CuratedDatabase_Guidelines.txt" and "CuratedDatabase_Survey.txt" contain information about the linguistic policies of journals in biological sciences. Their information was collected from author guidelines and surveys to editors-in-chief, respectively. Additional information from the journal was extracted from the 2020 Journal Citation Reports (JCR).</p> <p><strong>Description, data type, and scoring options of the databases' fields (columns)</strong></p> <p>"CuratedDatabase_Guidelines.txt"'</p> <ul> <li>Collaborator: Anonymised identifier of collaborator who collected the information. Categorical.</li> <li>SurveyAnswer: Whether the editor-in-chief of the editor accepted the request to complete the survey. Categorical. YES, NO.</li> <li>Journal_JCR: Anonymised identifier of the journal name. Text.</li> <li>ImpactFactor_JCR: Impact Factor of the journal as per JCR. Numeric.</li> <li>OA_JCR: Percentage of Golden Open Access articles as per JCR. Numeric.</li> <li>Discipline_JCR: Discipline of the journal as per JCR. Categorical.</li> <li>CountryPublication_JCR: Country or region of publication of the journal as per JCR. Categorical.</li> <li>LanguageCountry: Language of the country where the journal is published. Categorical. English, Non-English.</li> <li>IssuesPerYear_JCR: Regularity as number of issues published by year as per JCR. Numeric.</li> <li>PublisherType: Business model of the journal's publisher. Categorical. Profit, Non-profit.</li> <li>SocietyJournal: Whether the journal is published on behalf of a scientific society. Categorical. 0 for NO, 1 for YES.</li> <li>Language: Languages in which the journal publish content. Text.</li> <li>RegionalGlobal: Whether the journal explicitly target to a global or regional audience. Categorical. Global, Regional.</li> <li>LinguisticDiversityEditors: Proportion of editors that are based in countries where English is not predominantly spoken. Numeric.</li> <li>EnglishEditingService: Type of English editing services that the journal provides to authors. Categorical. 0 for NONE, 1 for COMMERCIAL, 2 for FREE.</li> <li>AdditionalSupport: Additional support that journals offer to authors. Text.</li> <li>AdditionalLanguageGuidelines: Whether the author guidelines are available in languages other than English. Categorical. 0 for ONLY ENGLISH, 1 for ADDITIONAL LANGUAGES IN SOME SECTIONS, 2 for ADDITIONAL LANGUAGES.</li> <li>LanguageGuidelines: If apply, languages in which the author guidelines are available.</li> <li>LinguisticInclusivityStatement: Whether the journal clearly states that a paper will not be rejected solely by the perceived quality of English. Categorical. NO, YES.</li> <li>AdditionalLanguageManuscripts: Whether the journal publish entire manuscripts in languages other than English. Categorical. 0 for NO, 1 for YES.</li> <li>AdditionalLanguageAbstract: Whether the journal publish abstracts in languages other than English. Categorical. 0 for NO, 1 for YES.</li> <li>WhereSecondAbstract: If apply, the format in which the journal publish abstracts in languages other than English. Categorical.</li> <li>ProofReadingAL: If apply, whether the non-English content is proofread. Categorical. </li> <li>ReferencesAdditionalLanguage: Whether the journal publish references in languages other than English. Categorical. 0 for PROHIBITED/NO-MENTION, 1 for ALLOWED, 2 for ENCOURAGED.</li> <li>AutomaticTranslationTools: Whether the journal's website implement machine translation tools to read the manuscripts in languages other than English. 0 for NO, 1 for YES.</li> </ul> <p>"CuratedDatabase_Survey.txt"</p> <ul> <li>Journal_JCR: Anonymised identifier of the journal name. Text.</li> <li>ImpactFactor_JCR: Impact Factor of the journal as per JCR. Numeric.</li> <li>OA_JCR: Percentage of Golden Open Access articles as per JCR. Numeric.</li> <li>SocietyJournal: Whether the journal is published on behalf of a scientific society. Categorical. 0 for NO, 1 for YES.</li> <li>LinguisticDiversityEditors: Proportion of editors that are based in countries where English is not predominantly spoken. Numeric.</li> <li>AdditionalLanguageManuscripts: Whether the journal publish entire manuscripts in languages other than English. Categorical. 0 for NO, 1 for YES.</li> <li>EnglishEditingService: Type of English editing services that the journal provides to authors. Categorical. 0 for NONE, 1 for COMMERCIAL, 2 for FREE.</li> <li>ReferencesAdditionalLanguage: Whether the journal publish references in languages other than English. Categorical. 0 for PROHIBITED/NO-MENTION, 1 for ALLOWED, 2 for ENCOURAGED.</li> <li>InstructionsReviewers: Whether reviewers are instructed to avoid recommending the rejection manuscripts solely based on the perceived standard of English. 0 for NO, 1 for YES.</li> <li>InstructionsEditors: Whether editors are instructed to avoid rejecting manuscripts solely based on the perceived standard of English. 0 for NO, 1 for YES.</li> <li>YearStartLinguisticPolicies: For journals that publish content in languages other than English, the year in which those policies were implemented. Numeric. Values "1920" correspond to "Before 1920."</li> <li>AdditionalLanguageAbstract: Whether the journal publish abstracts in languages other than English. Categorical. 0 for NO, 1 for YES.</li> </ul>
Data augmentation for Multi-Classification of Non-Functional Requirements - Dataset
<p>There are four datasets:</p> <p>1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value.</p> <p>2. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language.</p> <p>3. Balanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements.</p> <p>The labeling schema, similar to PROMISE NFR, includes the following categories: A: Availability, PO: Portability, L: Legal, FT: Fault tolerance, SC: Scalability, MN: Maintainability, LF: Look and feel, PE: Performance, O: Operational. US: Usability, and SE: Security.</p>
Effective seed sterilization methods require optimization across maize genotypes
<p>Studies of plant-microbe interactions using synthetic microbial communities (SynComs) often require the removal of seed-associated microbes by seed sterilization before inoculation to provide gnotobiotic growth conditions. A diversity of seed sterilization protocols have been developed in the past and have been used on different plant species with various amounts of validation. From these studies, it has become clear that each plant species requires its own optimized sterilization protocol. It has, however, so far not been tested if the same protocol works equally well for different varieties and seed sources of one plant species. We evaluated six seed sterilization protocols on two different varieties (Sugar Bun & B73) of maize. All unsterilized maize seeds showed fungal growth upon germination on filter paper, highlighting the need for a sterilization protocol. A short sterilization protocol with hypochlorite and ethanol was sufficient to prevent fungal growth on Sugar Bun germinants, however, a longer protocol with heat treatment and germination in fungicide was needed to obtain clean B73 germinants. This difference may have arisen from the effect of either genotype or seed source. We then tested the protocol that performed best for B73 on three additional maize genotypes from four sources. Seed germination rates and fungal contamination levels varied widely by genotype and geographic source of seeds. Our study shows that consideration of both variety and seed source is important when optimizing sterilization protocols and highlights the importance of including seed source information in plant-microbe interaction studies that use sterilized seeds.</p>
Fig. 1 in Area Requirements Of Passerine Birds In The Reed Archipelago Of Lake Velence, Hungary
Fig. 1. Incidence functions of the 8 passerine bird species observed on the 109 reed islands at Lake Velence, Hungary. Abbreviations: Acraru = Acrocephalus arundinaceus, Acrsci = A. scirpaceus, Acrsch = A. schoenobaenus, Acrmel = A. melanopogon, Loclus = Locustella luscinioides, Panbia = Panurus biarmicus, Embsch = Emberiza schoeniclus, Lussve = Luscinia svecica
Fig. 1 in Geographic Variation In Habitat Requirements Of Two Coexisting Newt Species In Europe
Fig. 1. (A) Distribution of the northern crested newt (Triturus cristatus) and (B) the smooth newt (T. vulgaris) in Europe (ARNOLD 2002), and their habitat studies performed by country. The symbols indicate (i) landscape types where study was conducted (1 = woodland mosaic with bogs; 2 = woodland mosaic with semi-natural areas; 3 = woodland mosaic with agricultural areas; 4 = inland dunes; 5 = bogs; 6 = agricultural areas; 7 = urban areas) and (ii) the inclusion of terrestrial and aquatic habitat features (filled symbols – both examined; half filled – aquatic habitat only; hollow symbol – terrestrial habitat only). Vegetation zones (according to AASMÄE 2005): A, tundra; B, alpine tundra; C, taiga; D, tem-
FOXO transcription factors are required for normal somatotrope function and growth
<p><strong>Supplemental Fig 1. <em>Prop1 </em>and <em>Sst</em> expression is unchanged in dKO mice. </strong>Whole pituitary glands were collected from WT and dKO mice at 6 weeks of age. RNA was isolated and cDNA generated in order to evaluate mRNA abundance for <em>Prop1 </em>in females and males. Hypothalamus was collected to evaluate expression of <em>Sst</em>.<em> </em>Expression was normalized to <em>Tfrc</em>. The data represent 7-8 animals for each genotype and sex and were analyzed using Student’s t test.</p> <p><strong>Supplemental Fig 2. Gonadotrope, thyrotrope and corticotrope cells appear normally distributed in dKO mice. </strong>Immunohistochemistry for LHB, TSHB, and ACTH was performed on pituitary gland tissue from female and male mice to determine the distribution of gonadotropes, thyrotropes, and corticotropes, respectively. No obvious difference was observed between dKO mice and WT controls. Scale bars represent 100 mm. Representative images of three animals per genotype and sex are shown.</p> <p><strong>Supplemental Fig 3. Lactotrope cells appear normally distributed in dKO mice.</strong> Immunohistochemistry for PRL was performed on pituitary gland tissue from female and male mice to determine the distribution of lactotropes. No apparent difference in lactotrope distribution was observed between dKO mice and WT controls. Scale bars represent 100 mm. Representative images of three animals per genotype and sex are shown.</p> <p><strong>Supplemental Fig 4. <em>Foxo1 </em>and <em>Foxo3</em> expression levels in liver and hypothalamus of dKO mice. </strong>Liver and hypothalamus were collected from WT and dKO mice at 6 weeks of age. RNA was isolated and cDNA generated in order to evaluate mRNA abundance for <em>Foxo1 </em>and <em>Foxo3 </em>in females and males. Expression was normalized to <em>Tfrc</em>. The data represent 5-8 animals for each genotype and sex and were analyzed using Student’s t test (*p<0.05, **p<0.01, ***p<0.001).</p> <p> </p> <p><strong>Materials and Methods</strong></p> <p><em>Animals and genotyping</em></p> <p> To obtain <em>Foxo1<sup>Δpit</sup></em> mice <em>Foxo1<sup>+/-</sup></em> mice (15) were mated to <em>Foxg1<sup>+/cre</sup></em> mice (10) to produce <em>Foxo1<sup>+/-</sup>;Foxg1<sup>+/cre</sup></em> mice. These were then mated to <em>Foxo1<sup>fl/fl</sup></em> mice (13) to obtain <em>Foxo1<sup>fl/-</sup>;Foxg1<sup>+/cre</sup></em> (<em>Foxo1<sup>Δpit</sup></em>) mice. Experimental <em>Foxo1<sup>fl/fl</sup>;Foxo3<sup>fl/fl</sup>;Foxg1<sup>+/cre</sup></em> (dKO) animals were generated by crossing <em>Foxo1<sup>fl/fl</sup>;Foxo3<sup>fl/fl</sup></em> females with <em>Foxo1<sup>+/fl</sup>;Foxo3<sup>fl/fl</sup>;Foxg1<sup>+/cre</sup></em> males. <em>Foxg1<sup>+/cre</sup></em> mice were purchased from Jackson Laboratories, Bar Harbor, ME, USA (stock no. 004337) and were maintained on a 129SvJ (stock no. 000691) background (10,11). <em>Foxo1<sup>fl/fl</sup></em> mice (Jackson Laboratories, stock no. 024756) which have <em>loxP</em> sites flanking exon two of the <em>Foxo1</em> gene (15) were a generous gift from Drs. Accili and Pajvani, with permission from Dr. DePinho. <em>Foxo1<sup>+/-</sup></em> mice (15) were provided by Drs. Accili and Pajvani.<em> Foxo3<sup>fl/fl</sup></em> mice were purchased from Jackson Laboratories (stock no. 024668) (16). Genotyping was performed using specific primers for <em>Foxo1-null </em>(<em>LacZ</em> fwd and rev),<em> Foxo1-flox </em>(FK1ckA-C),<em> Foxg1-cre </em>(<em>cre </em>fwd and rev), and <em>Foxo3-flox </em>(ofk2ck1-3). A list of primers used can be found in Table S1.</p> <p> All mice were housed in a 12-hour light/dark cycle with feed (Formulab Diet 5008; Purina Mills, Gray Summit, MO, USA) and water <em>ad libitum</em>. Animals were weighed once per week starting at postnatal day seven. Mice were euthanized using CO<sub>2</sub> inhalation. Mouse length was measured post-euthanization by measuring from nose to rump. All procedures were conducted in accordance with the principles and procedures outlined in the National Institutes of Health Guidelines for the Care and Use of Experimental Animals and in accordance with Southern Illinois University Carbondale policies.</p> <p><em>Immunohistochemistry</em></p> <p> Pituitary gland tissue was collected post-euthanization and fixed in 10% formalin in PBS then dehydrated in graded ethanol solutions (50% then 80%). Tissue was then embedded in paraffin blocks and cut into 5 μm sections and mounted on positively charged slides. All immunohistochemistry (IHC) was begun by deparaffinization and rehydration of tissue sections using xylene (twice for 5 minutes each), 100% ethanol (twice for 3 minutes each), 95% ethanol (twice for 3 minutes each), then PBS. For immunofluorescent detection where antibody signal was amplified (Supplemental Table S2), slides were then incubated in 1.5% H<sub>2</sub>O<sub>2</sub> for 20 minutes. All tissue sections were blocked for 60 minutes using the Tyramide Signal Amplification (TSA) Kit Blocking Solution (TSB; Perkin Elmer, Waltham, MA, USA), which was also used as the diluent for all antibody solutions. Primary antibodies were incubated overnight at 4°C but all other steps were performed at room temperature (RT). After primary antibody incubation, tissue sections were washed three times for 3 minutes each in PBS-TWEEN 20 (PBS-T, 0.05%). Fluorophore-conjugated secondary antibody was then incubated on tissue sections for 60 minutes. Nuclei were stained using 4’,6’-diamidino-2-phenylindole (DAPI). Sections were then mounted using immunofluorescent mount (0.5 mM polyvinyl alcohol, 0.12 M Tris pH 8.0, 0.3% w/v glycerol, 2.5% w/v 1,4-diazabicyclo[2.2.2]octane) and glass coverslips.</p> <p> Imaging was performed using a Retiga 2000R digital camera attached to a Leica DM 5000B fluorescent microscope (Leica Biosystems, St. Louis, MO, USA). Individual captures of FITC and DAPI channels were merged using Adobe Photoshop CS3. Some images were brightened for illustrative purposes; however, the exact alterations were duplicated in both control and experimental images to maintain the ability to compare results. Three animals per genotype were analyzed for these studies.</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>ID</strong></p> </td> <td> <p><strong>Antigen</strong></p> </td> <td> <p><strong>Citation</strong></p> </td> <td> <p><strong>Host</strong></p> </td> <td> <p><strong>Company</strong></p> </td> <td> <p><strong>Cat. No.</strong></p> </td> <td> <p><strong>Dilution</strong></p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Rabbit anti-mouse PRL antibody</p> </td> <td> <p><a href="http://antibodyregistry.org/AB_2721133">AB_2721133</a></p> </td> <td> <p>mouse PRL</p> </td> <td> <p>(A.F. Parlow National Hormone and Peptide Program Cat# AFP107120402, RRID:AB_2721133)</p> </td> <td> <p>rabbit</p> </td> <td> <p>A.F. Parlow National Hormone and Peptide Program</p> </td> <td> <p>AFP10712402</p> </td> <td> <p>IF 1:10000</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Rabbit anti-Rat TSHβ antibody</p> </td> <td> <p><a href="http://antibodyregistry.org/AB_2665563">AB_2665563</a></p> </td> <td> <p>rat TSHB</p> </td> <td> <p>(A.F. Parlow National Hormone and Peptide Program Cat# rTSHb, RRID:AB_2665563)</p> </td> <td> <p>rabbit</p> </td> <td> <p>A.F. Parlow National Hormone and Peptide Program</p> </td> <td> <p>rTSHb also AFP-1274789</p> </td> <td> <p>IF 1:2000</p> </td> </tr> <tr> <td> <p>ACTH (adrenocorticotropic hormone) antibody</p> </td> <td> <p><a href="http://antibodyregistry.org/AB_2313902">AB_2313902</a></p> </td> <td> <p>ACTH</p> </td> <td> <p>(National Hormone & Peptide Program, Torrance, CA Cat# AFP-156102789, RRID:AB_2313902)</p> </td> <td> <p>rabbit</p> </td> <td> <p>A.F. Parlow National Hormone and Peptide Program</p> </td> <td> <p>AFP-156102789</p> </td> <td> <p>IF 1:500</p> </td> </tr> </tbody> </table> <p><em>RTqPCR</em></p> <p> Tissue collected for mRNA analysis was stored in RNA Later (AM7021, Invitrogen, Carlsbad, CA, USA) until use. Whole pituitary glands were lysed, and RNA was extracted and purified using the RNAqueous Micro Kit (AM1931) according to the kit protocol. The resultant mRNA was reversed transcribed to cDNA using the Promega M-MLV kit according to included instructions (M5313, Promega, Madison, WI, USA). Ten ng of cDNA were used for mRNA analysis. All samples were run in duplicate and a sample processed with no reverse transcriptase enzyme was included as a negative control. Results were calculated using the ΔΔCt method by first normalizing to RNA-polymerase subunit II b (<em>Polr2b</em>) as an internal control then calculated relative to transferrin receptor (<em>Tfrc</em>) to compare between groups. Both genes are expressed at consistent levels between genotypes. At least five mice per genotype were used in these studies.</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Sequence (5’ to 3’)</strong></p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>LacZ fwd</p> </td> <td> <p>TTCACTGGCCGTCGTTTTACAAGCTCGTGA</p> </td> </tr> <tr> <td> <p>LacZ rev</p> </td> <td> <p>ATGTGAGCGAGTAACAACCCGTCGGATTCT</p> </td> </tr> <tr> <td> <p>FK1ckA</p> </td> <td> <p>GCTTAGAGCAGAGATGTTCTCACATT</p> </td> </tr> <tr> <td> <p>FK1ckB</p> </td> <td> <p>CCAGAGTCTTTGTATCAGGCAAATAA</p> </td> </tr> <tr> <td> <p>FK1ckC</p> </td> <td> <p>CAAGTCCATTAATTCAGCACATTGA</p> </td> </tr> <tr> <td> <p><em>cre </em>fwd</p> </td> <td> <p>GCGGTCTGGCAGTAAAAACTATC</p> </td> </tr> <tr> <td> <p><em>cre </em>rev</p> </td> <td> <p>GTGAAACAGCATTGCTGTCACTT</p> </td> </tr> <tr> <td> <p>ofk2ck3</p> </td> <td> <p>CATGCAGTCCGAGAGATTTG</p> </td> </tr> <tr> <td> <p>ofk2ck2</p> </td> <td> <p>AGTGTCTGATACCGAAGAGC</p> </td> </tr> <tr> <td> <p>ofk2ck1</p> </td> <td> <p>AACAACCTCACACATGTGCC</p> </td> </tr> <tr> <td> <p><em>mPolr2b </em>RTqPCR<em> </em>fwd</p> </td> <td> <p>AGATGTATGACGCCGACGAG</p> </td> </tr> <tr> <td> <p><em>mPolr2b </em>RTqPCR<em> </em>rev</p> </td> <td> <p>GTAAGAACTGATCACGATCCAGCA</p> </td> </tr> <tr> <td> <p><em>mTfrc </em>RTqPCR<em> </em>fwd</p> </td> <td> <p>GCAAGATGTAAAGCATCCAGTTGATGG</p> </td> </tr> <tr> <td> <p><em>mTfrc </em>RTqPCR<em> </em>rev</p> </td> <td> <p>GCATATTCTGGAATCCCAGCAG</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><em>mFoxo1 </em>RTqPCR<em> </em>fwd</p> </td> <td> <p>AGGATAAGGGCGACAGCAAC</p> </td> </tr> <tr> <td> <p><em>mFoxo1 </em>RTqPCR<em> </em>rev</p> </td> <td> <p>CCGCTCTTGCCTCCCTC</p> </td> </tr> <tr> <td> <p><em>mFoxo3 </em>RTqPCR<em> </em>fwd</p> </td> <td> <p>GGGCGACAGCAACAGCT</p> </td> </tr> <tr> <td> <p><em>mFoxo3 </em>RTqPCR<em> </em>rev</p> </td> <td> <p>CCCGCTCTTTCCCCCATC</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><em>mProp1 </em>RTqPCR fwd</p> </td> <td> <p>GCCTCTGGGACTCTGATCTCC</p> </td> </tr> <tr> <td> <p><em>mProp1</em> RTqPCR rev</p> </td> <td> <p>CAGGATACTGGTTCCTCCCAA</p> </td> </tr> <tr> <td> <p><em>mSst </em>RTqPCR fwd</p> </td> <td> <p>TCTGCATCGTCCTGGCTTTG</p> </td> </tr> <tr> <td> <p><em>mSst </em>RTqPCR rev</p> </td> <td> <p>GACAGCAGCTCTGCCAAGAA</p> </td> </tr> </tbody> </table> <p> </p> <p><em>Statistical analysis</em></p> <p> All data were analyzed using Student’s t test unless otherwise noted where (*) indicates p < 0.05, (**) indicates p < 0.01, (***) indicates p < 0.001, and (****) indicates p < 0.0001. Error bars indicate standard error of the mean (SEM).</p>
Material requirements for future low-carbon electricity projections in Africa - dataset
<p>This dataset is provided to supplement the paper "Material requirements for future low-carbon electricity projections in Africa".</p> <p>The paper calculated the material requirements of proposed electricity systems based on the <a href="https://zenodo.org/record/3521841#.YTHYP44zaUk">JRC-TEMBA </a>projections of African electricity systems, including the embodied emissions resulting from such systems from 2015 to 2065.</p> <p>The data included in this repository covers the total mass of materials, embodied emissions, and jobs created given by country, year and/or region.</p>
Experiment package for Elicitation of Adaptive Requirements Using Creativity Triggers: A Controlled Experiment
<p>Full experimental materials, scripts, and results for "Elicitation of Adaptive Requirements Using Creativity Triggers: A Controlled Experiment"</p>
Quantitative raw data for D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science"
<p>This dataset presents the quantitative raw data that was collected under the H2020 INCENTIVE project for the D1.3 - “Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”. The dataset includes the answers that were provided by almost 2,000 participants from 4 pilot European countries (Greece, Lithuania, Spain, and the Netherlands) regarding the general public's perceptions, attitudes, concerns, motivational factors and obstacles with regard to participation in Citizen Science activities. The original survey questionnaire was created and disseminated through the EUSurvey platform, and data collection took place from April to June 2021. For the statistical analysis of the data and the conclusions drawn from the analysis, you can access the D1.3 - "Requirements and motivations of quadruple helix stakeholders for active engagement in the Citizen Science Hubs”.</p> <p>Under INCENTIVE, four Citizen Science Hubs will be established and tested during the life-span of the project in the facilities of four Research Performing and Funding Organisations (RPFOs): University of Twente (the Netherlands), Autonomous University of Barcelona (Spain), Aristotle University of Thessaloniki (Greece) and Vilnius Gediminas Technical University (Lithuania). Essentially, the Hubs will aim to bring different stakeholders together and bridge society with science under the emerging paradigm of Citizen Science, in an institutionalised way.</p>
Requirements (enhancements) of 64 Mozilla projects mined from Bugzilla
<p>The dataset consists of 4200 enhancements that are in some form of dependency with the others (such as blocks, depends_on etc.) This data spans from 08/05/2001 to 09/08/2019.</p> <p>This dataset is gathered using Bugzilla's REST API for 64 projects. We have id, summary, priority, severity, type, version, target_milesotne, product, depends_on, blocks fields information for each one of these enhancements.</p>
Artifact: "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices
<p>Artifact for "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices</p>
Input files required by the bioconvert benchmark
<p>These files required to launch the bioconvert benchmarking snakemake framework. For details see https://bioconvert.readthedocs.io .</p>
CROSSBOW HLU2-UC7-TC4 Real curtailment required to reduce cross-border congestion
<p>This dataset summarizes the amount of energy that should be curtailed in order to obtain the desired reduction at the monitored cross-border line.</p> <p>This amount is not linear and depending on the congestion size, the curtailment cost might be really expensive. This data summarizes the KPI results of analysing the real curtailment requirements when facing different congestions. Two scenarios are considered:</p> <ul> <li>In the line between substations GKORIN and G1ARGOS</li> <li>In the line between substations G2PATR31 and GSIMOP31</li> </ul> <p>For each unit of power reduction needed (x-axis) the corresponding curtailment required is presented (y-axis)</p>
ReSpa - Towards an automatic requirements classification in a new Spanish dataset
<p>ReSpa (Spanish Dataset for requirements classification) dataset is conformed by requirements collected from final degree projects from one University. It was presented in the paper 'Towards an automatic requirements classification in a new Spanish dataset' and used also in the paper 'Requirements Classification Using FastText and BETO in Spanish Documents'.</p> <p>Cited as:</p> <p>Limaylla-Lunarejo, M. I., Condori-Fernandez, N., & Luaces, M. R. (2022, August). Towards an automatic requirements classification in a new Spanish dataset. In <em>2022 IEEE 30th International Requirements Engineering Conference (RE)</em> (pp. 270-271). IEEE. https://doi.org/10.1109/RE54965.2022.00039</p> <p> </p> <p> </p> <p> </p> <p> </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.