Find research datasets worth reusing

opencc-by-4.0Dec 2017View details →

Fig. 10 in Haemoprotozoa: Making biological sense of molecular phylogenies

Fig. 10. Probable evolutionary origins of haemoprotozoan parasites (solid lines = strong inferential support; dotted lines = presumptive).

opencc-by-4.0Dec 2017View details →

Fig. 9 in Haemoprotozoa: Making biological sense of molecular phylogenies

Fig. 9. Phenotypic characters mapped against broad molecular phylogenies of piroplasm blood parasites. Molecular phylogenetic relationships are indicated on the left as a consensus (macro-evolutionary) tree derived from multiple studies cited within the text.

opencc-by-4.0Dec 2017View details →

Fig. 4 in Haemoprotozoa: Making biological sense of molecular phylogenies

Fig. 4. Phenotypic characters mapped against broad molecular phylogenies of trypanosomatid haemoflagellates. Molecular phylogenetic relationships are indicated on the left as a consensus (macro-evolutionary) tree derived from multiple studies cited within the text.

opencc-by-4.0Jul 2024View details →

Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"

Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"  <a href="../api/records/13133835/draft/files/tmdcm.txt/content" target="_blank" rel="noopener noreferrer">tmdcm.txt</a>: current meter data  <a href="../api/records/13133835/draft/files/tide.txt/content" target="_blank" rel="noopener noreferrer">tide.txt</a>: tidal gauge data  <a href="../api/records/13133835/draft/files/windspeed.txt/content" target="_blank" rel="noopener noreferrer">windspeed.txt</a>: windspeed data  Figure 2: Figure2.npy Figure 3: Figure 3 abc .npy Figure16:  <a href="../api/records/13133835/draft/files/spatial_Vc.npy/content" target="_blank" rel="noopener noreferrer">spatial_Vc.npy</a> & <a href="13133835" target="_blank" rel="noopener noreferrer">spatial_h.npy</a>  Figure 17: <a href="../api/records/13133835/draft/files/streching_ncf.npy/content" target="_blank" rel="noopener noreferrer">streching_ncf.npy</a>

opencc-by-4.0Jul 2017View details →

Figure 2 in A sense of scale: Foraging cetaceans' use of scale-dependent multimodal sensory systems

Figure 2. Scale-of-senses schematic of the hypothetical interchange of sensory modalities used by baleen whales to locate prey at variable scales. The line for audition of signals from prey is faded to denote a lack of evidence for this sensory system in baleen whales. X-axis on log scale, with equivalent metric distance given in gray type, and associated scale below. Y-axis ranks the relative use of each sensory modality between 0 (no contribution) and 10 (highest contribution) relative to its own information capacity, not relative to other senses.

opencc-by-4.0Jul 2017View details →

Figure 1 in A sense of scale: Foraging cetaceans' use of scale-dependent multimodal sensory systems

Figure 1. Scale-of-senses schematic of the hypothetical interchange of sensory modalities used by dolphins to locate prey at variable scales. The line for chemoreception is faded to denote a lack of support for the sensory system in dolphins. X-axis on log scale, with equivalent metric distance given in gray type, and associated scale below. Y-axis ranks the relative use of each sensory modality between 0 (no contribution) and 10 (highest contribution) relative to its own information capacity, not relative to other senses.

Fig. 2 Tomopteris pacifica. Neurogenesis. Confocal maximum projections. A in Development and structure of the anterior nervous system and sense organs in the holopelagic annelid Tomopteris spp. (Phyllodocida, Errantia)

Fig. 2 Tomopteris pacifica. Neurogenesis. Confocal maximum projections. A Early developmental stages are characterized by a large amount of yolk and a prominent prototroch (pt). B At 5 days post-fertilization (dpf), the larval stages possess four pairs of well-developed trunk appendages and a distinct prototroch (pt). Note that the anterior-most appendage (I) is uniramous while all other appendages appear biramous. C Slightly older stages show a well-developed ventral nerve cord (vnc) with outgoing parapodial neurite bundles (pn) innervating the body appendages; serotonergic somata form serial clusters along the ventral nerve cord. D A closer examination of larvae at around 6–7 dpf shows the presence of a prominent nuchal nerve (nn) innervating the nuchal organs and originating from the dorsal part of the larval brain (br). The insert shows the innervation of the nuchal organ.

Fig. 9 in Development and structure of the anterior nervous system and sense organs in the holopelagic annelid Tomopteris spp. (Phyllodocida, Errantia)

Fig. 9 Tomopteris helgolandica. Nuchal organ, details of receptor cells, juvenile, TEM. A Outer part of the olfactory chamber (oc), showing that the epithelium is primarily formed by monociliary sensory dendrites (sd), some with basal bodies (arrows). The olfactory chamber has numerous sensory processes (spr), and vesicle-like structures appearing empty (asterisks). The apical region of dendrites often contains dense cored (arrowheads) and other vesicles. B Sensory dendrite (sd) with very short sensory cilium and shaft branches into microvillus-like structures (arrowhead). C Sensory dendrite sending out a microvillus

Fig.1 Tomopteris pacifica. Developmental stages. SEM images. A in Development and structure of the anterior nervous system and sense organs in the holopelagic annelid Tomopteris spp. (Phyllodocida, Errantia)

Fig.1 Tomopteris pacifica. Developmental stages. SEM images. A Spherical trochophore ca. 48–72-h post-fertilization. B Elongated embryo at ca. 5 days post-fertilization (dpf) with four segments and rudiments of parapodia, Roman numerals refer to segment numbers. C Dorsal view of larva/juvenile at 6–7 dpf showing parapodia formation; note first cirruslike appendage. Nuchal organs (no) visible as cilia semicircles in front of the prototroch (pt). D Ventral view of larva/juvenile at ca. 8–10 dpf. E

Fig. 5 in Development and structure of the anterior nervous system and sense organs in the holopelagic annelid Tomopteris spp. (Phyllodocida, Errantia)

Fig. 5 Tomopteris helgolandica. Tentacular cirrus of juvenile. TEM. A Longitudinal section of the intracellular skeletal element (se) with regular cross striation pattern. B Skeletal element, periodicity, and substructure of the striation pattern. C Process of rod-bearing cells reaching the epithelial surface of the epidermis (arrow). D Gland cell opening at the base of cirrus with a circle of microvilli (arrow). E Group of distal gland cell processes close to opening at the base of cirrus. F Central part of the cirrus formed by numerous neurites cov-

opencc-by-4.0Jan 2012View details →

Figure 4. Adding a Sensing Module and selecting its type-Designing a Growing Functional Modules "Artificial Brain"

GFM controllers learn to satisfy some predefined goals while interacting with the environment and thus should be considered as artificial brains. An example of the design process of a simple controller is provided herein to explain the inherent methodology, to exhibit the components' interconnections and to demonstrate the control process.

opencc-by-4.0Jan 2012View details →

Figure 5. Adding connections from Sensations 2-17 to the Sensing Module 1-Designing a Growing Functional Modules "Artificial Brain"

The next step consists of connecting the sixteen sensations in the input of the Sensing Module. To do this, the user must right-click on each Sensation, then on “new connection” and indicate the Sensing Module identifier. The resulting design is presented on figure 5. Finally, the Acting Module's field is set to “1” (indicating the number of the Acting Module that later will assess the correctness of the perception) and the unique extra-parameter set to “20” (related with the module's behavior).

opencc-by-4.0Jan 2012View details →

Figure 2. The editor's components from left to right: a) Sensation, b) Sensing Module, c) Global Goal, d) Acting Module.-Designing a Growing Functional Modules "Artificial Brain"

Each “Sensation” corresponds to an integer value corresponding to a specific system's sensor. Sensations are symbolized by a green rectangle on the editor's canvas (figure 2.a). Each newly created sensation is assigned an identifier previously incremented. Its unique field, initially filled with question-marks, allows it to associate a mnemonic in order to facilitate its interpretation.

opencc-by-nc-sa-4.0Oct 2018View details →

Green Roofs Footprints for New York City, Assembled from Available Data and Remote Sensing

Summary: 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. 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. Terms of Use: 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. Associated Files: As of this release, the specific files included here are: <ul> <li>GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).</li> <li>GreenRoofData2016_20180917.gpkg 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>GreenRoofData2016_20180917_Shapefile.zip 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>GreenRoofData2016_20180917.csv 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> Column Information for the datasets: 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. <ul> <li>fid - Unique identifier</li> <li>bin - 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>bbl - 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>gr_area - 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>bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li>prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).</li> <li>cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.</li> <li>doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.</li> <li>heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.</li> <li>feat_code - Code describing the type of building, pulled from the Building Footprint Data.</li> <li>groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.</li> <li>qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'</li> <li>notes - 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>classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)</li> <li>digitized - 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>newlyadded - 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>original_source - 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>address - Address based on MapPLUTO, joined to the dataset based on bbl.</li> <li>borough - Borough abbreviation pulled from MapPLUTO.</li> <li>ownertype - Owner type field pulled from MapPLUTO.</li> <li>zonedist1 - Zoning District 1 type pulled from MapPLUTO.</li> <li>spdist1 - Special District 1 pulled from MapPLUTO.</li> <li>bbl_fixed - Flag to indicate whether bbl 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> For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263): <ul> <li>xcoord - Longitude in decimal degrees.</li> <li>ycoord - Latitude in decimal degrees.</li> </ul> Acknowledgements: 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.

opencc-by-4.0Jan 2019View details →

Hybrid sequencing reveals insight into heat sensing and signaling of bread wheat

Wheat (Triticum aestivum L.), a globally important crop, is challenged by increasing temperatures (heat stress, HS); however, its polyploid nature, the incompleteness of its genome sequences and annotation, the lack of comprehensive HS-responsive transcriptomes and the unexplored heat sensing and signaling of wheat hinder our full understanding of its adaptations to HS. The recently released genome sequences of wheat, as well as the emerging single-molecular sequencing technologies, provides an opportunity to thoroughly investigate the molecular mechanisms of the wheat response to HS. We generated a high-resolution spatio-temporal transcriptome map of wheat flag leaves and filling grain under HS at 0 minute (m), 5 m, 10 m, 30 m, 1 hour (h) and 4 h by combining full-length single-molecular sequencing and Illumina short reads sequencing. This hybrid sequencing newly discovered 4,947 loci and 70,285 transcripts, generating the comprehensive and dynamic list of HS-responsive full-length transcripts and complementing the recently released wheat reference genome.

opencc-by-4.0Dec 2018View details →

Snow accumulation patterns in a high mountain Andean catchment from optical tri-stereoscopic remote sensing

1) DBSM_Data_RioYeso' = Automatic weather station (AWS) data from Yeso Embalse and Termas del Plomo meteorological stations (available from Chilean Water Directorate, 'Dirección General de Aguas' or 'DGA' http://www.arcgis.com/apps/OnePane/basicviewer/index.html?appid=d508beb3a88f43d28c17a8ec9fac5ef0), used to force a distributed blowing snow model of Essery et al. (1999) to derive spatial snow depth of the Rio del Yeso catchment, Chile. The format is as follows: {'Year','Month','Day','Hour','Incoming shortwave radiation (Wm2)','Incoming longwave radiation (Wm2)','SnowfallRate(mm/hr)','RainfallRate(mm/hr)','Air temperature (celsius)','Relative humidity (%)','Wind speed (m s-1)','Compass wind direction','Air pressure (hPa)'}; 2) 'snowHeightPleiadesREG' = A snow depth map (horizontal resolution 4m) derived from triplets of high resoution stereo optical satellite images (Pléiades) following the methodology of Marti et al. (2016). The snow depth map is derived for a high mountain catchment (Rio del Yeso) of the central Chilean Andes (see Burger et al., 2018). 3) 'L2_LiDAR_4m' = A LiDAR (Light detection and Ranging) spatial snow depth map at a horizontal resolution of 4 m. The data were captured by a Reigl VZ-6000 LiDAR scanner and generated from the difference of two constructed digital elevation models (DEMs) between the dates 13th September, 2017 (with snow) and 12th December, 2017 (without snow).  4) 'L2_Pleiades_SDLidar_NEW' = The Pléiades snow depth map as described in 2), extracted by the areas of LiDAR scan described in 3).  5) 'SnowDepthResults' = A folder containing a corrected and gap-filled Pléiades snow depth map ('SD_PleiadesCORR') and for comparison: 'SD_TOPO', a statistical estimation of snow depth using topographic parameters and the regression equation of Grünewald et al. (2013) and; The physically based estimates of snow depth using the DBSM model as in 1) without snow transport for the 4th September, 2017 ('SD_EXTP_Sep04') and 13th September, 2017 ('SD_EXTP_Sep13') and with snow transport for those dates ('SD_Wind_Sep04','SD_Wind_Sep13'). 6) 'rdyDEM' = An independent ASTER GDEM (https://asterweb.jpl.nasa.gov/gdem.asp) cut to the area of the study catchment (horizontal resolution = 30 m).  7) 'PlanetScope_20170907_TPK' = An stitched optical PlanetScope image of the catchment (horizontal resolution of 3.25 m) derived from access under the research and teaching iniative (planet.com).  Cited work: Burger, F. et al. (2018) ‘Interannual variability in glacier contribution to runoff from a high ‐ elevation Andean catchment : understanding the role of debris cover in glacier hydrology’, Hydrological Processes, pp. 1–16. doi: 10.1002/hyp.13354. Essery, R., Li, L. and Pomeroy, J. (1999) ‘A distributed model of blowing snow over complex terrain’, Hydrological Processes, 13(14–15), pp. 2423–2438. doi: 10.1002/(SICI)1099-1085(199910)13:14/15<2423::AID-HYP853>3.0.CO;2-U. Grünewald, T. et al. (2013) ‘Statistical modelling of the snow depth distribution in open alpine terrain’, Hydrology and Earth System Sciences, 17(8), pp. 3005–3021. doi: 10.5194/hess-17-3005-2013. Marti, R. et al. (2016) ‘Mapping snow depth in open alpine terrain from stereo satellite imagery’, The Cryosphere, pp. 1361–1380. doi: 10.5194/tc-10-1361-2016.

Fig. 17.4 in Chapter 17: Gigantism, Dwarfism, and Cope's Rule: "Nothing in Evolution Makes Sense without a Phylogeny"

Fig. 17.4. Left, phylogeny of the Equidae, with emphasis on the North American record. Right, temporal distribution of the Equidae, with relative size indicated by skull length derived from toothlength dimensions (see appendix 17.2 and methodology discussion in text). Branches indicated by A, B, and C represent bodysize increase (giantism); D, E, F, and G represent bodysize decrease (nanism).

Fig. 17.2 in Chapter 17: Gigantism, Dwarfism, and Cope's Rule: "Nothing in Evolution Makes Sense without a Phylogeny"

Fig. 17.2. (a) The most recent phylogenetic hypothesis of varanid relationships based on mtDNA (Ast, 2001) compared to (b) a compilation of the hypotheses of bodysize evolution of varanids (taken from Pianka, 1995). The maximum total lengths for the species were retrieved from King and Green, 1999, and Mertens, 1942; these are listed in appendix 17.1. Note the following terminal clades were collapsed for the sake of brevity: Varanus salvator togianus, V. salvator bivittatus, V. indicus, and V. panoptes (horni).

Fig. 17.3 in Chapter 17: Gigantism, Dwarfism, and Cope's Rule: "Nothing in Evolution Makes Sense without a Phylogeny"

Fig. 17.3. Patterns of bodysize evolution in fossil horses from North America, based on MacFadden (1987; modified figure reproduced in MacFadden, 1992). Reproduced with permission of Cambridge University Press.

Fig. 17.1 in Chapter 17: Gigantism, Dwarfism, and Cope's Rule: "Nothing in Evolution Makes Sense without a Phylogeny"

Fig. 17.1. Threetaxon statements illustrating the four kinds of bodysize change discussed in the text.