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which has priority over all other available names (Lanza and Broadley 2014). More recently, Broadley et al. (2018) split Gonionotophis into four genera, with brussauxi being the only Angolan species remaining in the genus. MAP 291. Distribution of Gonionotophis brussauxi in Angola. in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
which has priority over all other available names (Lanza and Broadley 2014). More recently, Broadley et al. (2018) split Gonionotophis into four genera, with brussauxi being the only Angolan species remaining in the genus. MAP 291. Distribution of Gonionotophis brussauxi in Angola.
Figure 6. Three interfaces available on the mobile device
<p>The device provides two important facilities: multimedia facilities (it allows recording, processing and playing audio samples) as well as graphic facilities (it provides a friendly and accessible interface). In Figure 6 is illustrated the main page of the application that is implemented on the mobile device (a), as well as two types of exercises; (b) the child is required to identify whether a sound is present in a word (which is indicated by an image); and (c), the child is required to choose a word from a group of paronyms.</p>
High-resolution projections of evapotranspiration and water availability for Europe under climate change
<p>Europe-wide high-resolution (1 km) gridded data of estimates of monthly and annual potential evapotranspiration (ET0), annual actual evapotranspiration (AET0) and water availability for a climate normal period largely preceding an anthropogenic warming signal (1961-1990) and for two CMIP5 multimodel future projections (2011-2040 and 2041-2070). In the ET0 calculation, the monthly and annual heat index <em>I</em> and annual <em>α</em> parameter were estimated following the Thornthwaite method, and AET0 was calculated using the Budyko approach.</p> <p>For citations and more details, please refer to "High-resolution projections of evapotranspiration and water availability for Europe under climate change" by Ştefan Dezsi, Marcel Mândrescu, Dănuţ Petrea, Praveen Kumar Rai, Andreas Hamann, Mărgărit-Mircea Nistor, published in <em>International Journal of Climatology</em> (<a href="https://doi.org/10.1002/joc.5537">https://doi.org/10.1002/joc.5537</a>)</p>
Green Roofs Footprints for New York City, Assembled from Available Data and Remote Sensing
<p><strong><em>Summary:</em></strong></p> <p>The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md</a>). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.</p> <p>These data are associated with an existing GitHub Repository, <a href="https://github.com/tnc-ny-science/NYC_GreenRoofMapping">https://github.com/tnc-ny-science/NYC_GreenRoofMapping</a>, and as needed and appropriate pending future work, versioned updates will be released here.</p> <p><strong><em>Terms of Use:</em></strong></p> <p>The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.</p> <p><strong><em>Associated Files:</em></strong></p> <p>As of this release, the specific files included here are:</p> <ul> <li><em>GreenRoofData2016_20180917.geojson</em> is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).</li> <li><em>GreenRoofData2016_20180917.gpkg</em> is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li><em>GreenRoofData2016_20180917_Shapefile.zip</em> is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li><em>GreenRoofData2016_20180917.csv</em> is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.</li> </ul> <p><strong><em>Column Information for the datasets:</em></strong></p> <p>Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.</p> <ul> <li><em>fid</em> - Unique identifier</li> <li><em>bin</em> - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: <a href="https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh">https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh</a>. Associated metadata for fields from that dataset are available at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md</a>.</li> <li><em>bbl</em> - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (<a href="http://gis.nyc.gov/taxmap/map.htm">http://gis.nyc.gov/taxmap/map.htm</a>) and PLUTO/MapPLUTO (<a href="https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page">https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page</a>). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.</li> <li><em>gr_area</em> - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li><em>bldg_area</em> - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li><em>prop_gr</em> - Proportion of the building covered by green roof according to this layer (<em>gr_area</em>/<em>bldg_area</em>).</li> <li><em>cnstrct_yr</em> - Year the building was constructed, pulled from the Building Footprint data.</li> <li><em>doitt_id</em> - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.</li> <li><em>heightroof</em> - Height of the roof of the associated building, pulled from the Building Footprint Data.</li> <li><em>feat_code</em> - Code describing the type of building, pulled from the Building Footprint Data.</li> <li><em>groundelev</em> - Lowest elevation at the building level, pulled from the Building Footprint Data.</li> <li><em>qa</em> - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'</li> <li><em>notes</em> - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.</li> <li><em>classified</em> - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)</li> <li><em>digitized</em> - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)</li> <li><em>newlyadded</em> - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)</li> <li><em>original_source</em> - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.</li> <li><em>address</em> - Address based on MapPLUTO, joined to the dataset based on <em>bbl</em>.</li> <li><em>borough</em> - Borough abbreviation pulled from MapPLUTO.</li> <li><em>ownertype</em> - Owner type field pulled from MapPLUTO.</li> <li><em>zonedist1</em> - Zoning District 1 type pulled from MapPLUTO.</li> <li><em>spdist1</em> - Special District 1 pulled from MapPLUTO.</li> <li><em>bbl_fixed</em> - Flag to indicate whether <em>bbl</em> was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)</li> </ul> <p>For <em>GreenRoofData2016_20180917.csv</em> there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):</p> <ul> <li><em>xcoord</em> - Longitude in decimal degrees.</li> <li><em>ycoord</em> - Latitude in decimal degrees.</li> </ul> <p><strong><em>Acknowledgements: </em></strong></p> <p>This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.</p>
Overview of available toxicity data for calystegines - results of the in silico genotoxicity predictions
<p>Results of the<em> in silico</em> genotoxicity predictions complementing the EFSA scientific report on calystegines: https://doi.org/10.2903/j.efsa.2019.5574</p>
Text-fig. 3. Rhinoppioides quadrituberculatus MIKO gen. et sp. nov. Above: assumed fragments of legs as seen in body cavity of holotype (Aa – in dorsal view, Ab – in ventral view) and paratype (B, only dorsal view available). Below: speculative reconstruction of legs, assumed segments leg IV in above rows (numbers 1, 3, 5, 7, 12, 13, 14), assumed segments of leg I below (numbers 6, 9, 10). Rest of the segments assumed to belong to legs II and III. Only trochanters III (nr. 8) and IV (nr. 7, 12) undoubtedly belonging to the new species. Bars indicating 50 µm, numbers indicate identity of segments. in Oribatid Mite Fossils From Quaternary And Pre-Quaternary Sediments In Slovenian Caves I.Two New Genera And Two New Species Of The Family Oppiidae From The Early Pleistocene
Text-fig. 3. Rhinoppioides quadrituberculatus MIKO gen. et sp. nov. Above: assumed fragments of legs as seen in body cavity of holotype (Aa – in dorsal view, Ab – in ventral view) and paratype (B, only dorsal view available). Below: speculative reconstruction of legs, assumed segments leg IV in above rows (numbers 1, 3, 5, 7, 12, 13, 14), assumed segments of leg I below (numbers 6, 9, 10). Rest of the segments assumed to belong to legs II and III. Only trochanters III (nr. 8) and IV (nr. 7, 12) undoubtedly belonging to the new species. Bars indicating 50 µm, numbers indicate identity of segments.
Figure. Interferon alpha-A based phylogenetic tree (neighbor joining method) constructed by MEGA 6.1 for Punjab urial in comparison with other mammalian species sequences available from GenBank (NCBI). in Characterization of interferon alpha of major histocompatibility complex class I in Punjab urial (Ovis vignei punjabiensis)
Figure. Interferon alpha-A based phylogenetic tree (neighbor joining method) constructed by MEGA 6.1 for Punjab urial in comparison with other mammalian species sequences available from GenBank (NCBI).
Fig. 2 in Trophic relationships in fish assemblages of Neotropical floodplain lakes: selectivity and feeding overlap mediated by food availability
Fig. 2. Ordination by principal coordinate analysis (PCoA) of the food resource availability for six floodplain lakes along the Upper Paraná River, Paraná-Mato Grosso do Sul. AQI = aquatic insects; OAI = other aquatic invertebrates; OTI = other terrestrial invertebrates; PLA = plants; TRI = terrestrial insects.
Fig.5 in Trophic relationships in fish assemblages of Neotropical floodplain lakes: selectivity and feeding overlap mediated by food availability
Fig.5. Relationship between the mean of the proportional overlap Index (IS) and the scores of the first PCoA axis of resource availability in isolated floodplain lakes along the upper Paraná River.Values of IS closer to 1 indicates greater diet overlap. The mean IS was calculated based on individuals of 3 (ZÉ = ZÉ Marinho), 7 (Carioca = Car), 4 (TiÃo = Tia), 5 (Genipapo = Gen), 2 (CidÃo = Cid) and 5 species (Canal = Can).AQI = aquatic insects; PLA = plants.
Fig. 1 in Trophic relationships in fish assemblages of Neotropical floodplain lakes: selectivity and feeding overlap mediated by food availability
Fig. 1. Locations of the lakes on the upper Paraná River floodplain, Brazil: 1, Canal do Meio; 2, Carioca; 3, ZÉ Marinho; 4, CidÃo; 5, Genipapo; 6, TiÃo.
Fig. 4 in Trophic relationships in fish assemblages of Neotropical floodplain lakes: selectivity and feeding overlap mediated by food availability
Fig. 4. Relationship of the mean the Schoener's Index (O) between pairs of species and the scores of the first PCoA axis of resource availability in isolated floodplain lakes along the upper Paraná River. The mean O was calculated based on 10 (ZÉ = ZÉ Marinho), 28 (Carioca = Car), 6 (TiÃo = Tia), 21 (Genipapo = Gen), 3 (CidÃo = Cid) and 10 (Canal = Can) pairs of species. AQI = aquatic insects; PLA = plants.
Fig. 3 in Trophic relationships in fish assemblages of Neotropical floodplain lakes: selectivity and feeding overlap mediated by food availability
Fig. 3. Relationships between feeding selectivity by fish and the availability of food resources for six floodplain lakes along the Upper Paraná River, ParanáMato Grosso do Sul. Shape of data distribution (envelope effect) was significant.
Fig. 2. Vertebral centra available for the ornithomimid ZPAL MgD−I in New material of a derived ornithomimosaur from the Upper Cretaceous Nemegt Formation of Mongolia
Fig. 2. Vertebral centra available for the ornithomimid ZPAL MgD−I/65 from the Upper Cretaceous Nemegt Formation, Mongolia in left lateral view. Abbreviations: C, cervical; D, dorsal; S, sacral; Ca, caudal.
Dataset and code: Decarbonizing lithium-ion battery primary raw materials supply chain: Available strategies, mitigation potential and challenges
<p>Repository to share the data and code associated with the scientific article <strong>Istrate et al. Decarbonizing lithium-ion battery primary raw materials supply chain: Available strategies, mitigation potential and challenges. Joule (2024)</strong>. The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.</p>
Data and analyses for Perkowski et al. (2024) manuscript accepted to AoB Plants: "Symbiotic nitrogen fixation reduces belowground biomass carbon costs of nitrogen acquisition under low, but not high, nitrogen availability"
<p>This repository contains data and scripts for analyses and plots in Perkowski et al. (2024), titled "Symbiotic nitrogen fixation reduces belowground biomass carbon costs of nitrogen acquisition under low, but not high, nitrogen availability".</p> <p>v2.0 updates code and scripts per reviewer comments and is the final release prior to manuscript proofing.</p>
Database of available existing wire spaced rod bundle experiments
<p><span>This report provides a literature review with respect to thermal-hydraulic experiments on rod bundles with wire spacers, representative of fuel assemblies in liquid-metal cooled fast reactors.<span> </span>Both isothermal and heated tests are evaluated, for nominal and non-nominal geometries. </span></p> <p><span>General acceptance criteria are defined for incorporating experiments into this review. The most relevant one is the related to the geometry: only the classical case with one wire per pin is considered. Further additional acceptance criteria are defined for each scenario. All rejected cases are listed in the appendix.</span></p> <p><span>Only publicly available data are considered, with a cut-off date of December 31<sup>st</sup>,<sup> </sup>2023. Ongoing activities are also mentioned, mostly from European collaborative projects, since results are expected to be published soon. A discussion on the comparison with simulation results is presented.</span></p>
Consumer biodiversity increases organic nutrient availability across aquatic and terrestrial ecosystems
<p>Human land-use intensification threatens arthropod (e.g., insect and spider) biodiversity across aquatic and terrestrial ecosystems. Insects and spiders play critical roles in ecosystems by accumulating and synthesizing organic nutrients like polyunsaturated fatty acids (PUFA). However, links between biodiversity and nutrient content of insect and spider communities have yet to be quantified. We relate insect and spider richness to biomass and PUFA-mass from stream and terrestrial communities encompassing nine land-uses. PUFA-mass and biomass relate positively to biodiversity cross ecosystems. In terrestrial systems, human-dominated areas have lower biomass and PUFA-mass than more natural areas, even at equivalent levels of richness. Aquatic ecosystems have consistently higher PUFA mass than terrestrial ecosystems. Our findings reinforce the importance of conserving biodiversity and highlight unique benefits of aquatic biodiversity.<br><br>This is all of the data and code required to reproduce the analyses. The repository doesn't allow specific folder structures, but the data should ideally be organized into folders organized and named as follows -</p> <p> 1_raw_data/<br>├─ bdm_data/<br>│ ├─ bdm_families_list.csv<br>├─ pufa_concentration_data/<br>│ ├─ Literature_estimates/<br>│ │ ├─ Literature_estimates.xlsx<br>│ ├─ Margaux_PUFA_Conc/<br>│ │ ├─ 2a.Lunz 2019_FA_InsectTransfer - Content.xlsx<br>│ │ ├─ 3a.Lunz 2019_FA_InsectConsu - Content.xlsx<br>│ ├─ Martin_Creuzberg_PUFA_Conc/<br>│ │ ├─ all_STOTEN.xlsx<br>│ ├─ Tarn_PUFA/<br>│ │ ├─ Mindelsee_FA.xlsx<br>├─ wsl_data/<br>2_modified_data/<br>├─ terr_insects_nutrients_null.rds<br>├─ aq_insect_nutrients_null.rds<br>├─ aquatic.data.rds<br>├─ terrestrial_data.rds<br>├─ insect_data/<br>│ ├─ aq_insect_regressions.rds<br>│ ├─ te_insect_regressions.rds<br>├─ null_models/<br>│ ├─ aquatic_habitat_all.rds<br>│ ├─ terrestrial_habitat_all.rds<br>3_r_scripts/<br>├─ Diversity_Analaysis.rmd<br>├─ Null_models.rmd<br>├─ Nutrient_content.rmd<br>├─ Scaling_relationships.rmd<br> </p> <p> </p>
Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research
<p><strong>This is the dataset of the report: Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research</strong></p> <p>It contains the following information of all the papers from ASE, FSE, and ICSE in 2023:</p> <ul> <li>Paper title</li> <li>Keyword</li> <li>Is the source data available and accessible in the paper?</li> <li>If the source data is not available, do the authors explain why?</li> <li>Hosting platforms</li> <li>Access mode</li> <li>License</li> <li>Is their experiment data reused from previous work, or newly generated specifically for this study, or combination of both? </li> <li>Do the authors change/modify their experiment data before experiment?</li> <li>What modifications do they perform?</li> <li>Does the link provide detailed instructions about how to replicate their paper?</li> <li>Does the link contains their complete experiment data, their source code or other materials that are necessary to replicate their experiments?</li> <li>What's the data format inside the link?</li> <li>What's the content of the link?</li> </ul> <p> </p> <p>We collect the data in a rush.</p> <p>If you want to use this dataset and find any errors, please contact us ;-)</p> <p> </p> <p>Our emails:</p> <ul> <li>echo.xiangchen@gmail.com</li> <li>zhifengyao731@gmail.com</li> </ul>
Data from: Intraspecific correlations between growth and defense vary with resource availability and differ within- and among-populations
<p>A paradigm in the plant defense literature is that defending against herbivores comes at a cost to growth, resulting in a growth-defense tradeoff. However, while there is strong evidence for growth-defense tradeoffs across species, evidence is mixed within species. Several mechanisms can account for this equivocal support within species, but teasing them apart requires examining growth-defense relationships both within and among populations, an approach seldom employed. We examined correlations between plant biomass (growth) and terpene production (defense) within and among populations of Monarda fistulosa, a perennial herb. We sampled populations from Montana and Wisconsin, regions that differ in resource availability characterized by different summer precipitation and associated abiotic conditions that influence plant productivity. We found negative, neutral, and positive growth-defense correlations, depending on the scale examined. Negative correlations occurred across populations originating from divergent regions, positive correlations occurred across populations originating from within the high-resource region, and neutral correlations were found within single populations. Collectively, these results challenge the general expectation of ubiquitous tradeoffs and support emerging views that resource availability (as it affects productivity) shapes the evolution of defense at different scales.</p>
Data from: Recovery of silver fir (Abies alba Mill.) seedlings from ungulate browsing mirrors soil nitrogen availability
<p><em>Abies alba</em> (Mill.) has a high potential for mitigating climate change in European mountain forests, yet, its natural regeneration is severely limited by ungulate browsing. Here, we simulated browsing in a common garden experiment to study growth and physiological traits, measured from bulk needles, using a randomized block design with two levels of browsing severity and seedlings originating from 19 populations across Switzerland. Genetic factors explained most variation in growth (on average, 51.5%) and physiological traits (10.2%) under control conditions, while heavy browsing considerably reduced the genetic effects on growth (to 30%), but doubled those on physiological traits related to C storage. While browsing reduced seedling height, it also lowered seedling water use efficiency (decreased δ<sup>13</sup>C) and increased their δ<sup>15</sup>N. Different populations reacted differently to browsing stress, and for seedling height, starch concentration and δ<sup>15</sup>N population differences appeared to be the result of natural selection. First, we found that populations originating from the warmest regions recovered the fastest from browsing stress, and they did so by mobilizing starch from their needles, which suggests a genetic underpinning for a growth-storage trade-off across populations. Second, we found that seedlings originating from mountain populations growing on steep slopes had a higher δ<sup>15</sup>N in the common garden than those originating from flat areas, indicating that they have been selected to grow on N poor, potentially drained, soils. This finding was corroborated by the fact that N concentration in adult needles was lower on steep slopes than on flat ground, strongly indicating that steep slopes are the most N poor environments. These results suggest that populations adapted to these N poor environments have a genetically based high N use efficiency, which could be necessary for their recover from ungulate browsing.</p>
ScienceDex guides
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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.