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6,025 results for “Science of science”
FIGURE 5 in Two Seira Lubbock 1869 (Collembola, Arthropleona, Entomobryidae) new to science, with remarkable secondary sexual characters
FIGURE 5. Seira raptora sp. n., third foot complex, a— basal tooth bigger than medial and apical teeth; b — tenent hair capitate and ciliate; SEM 3300x, bar length 10 m.
FIGURE 6 in Two Seira Lubbock 1869 (Collembola, Arthropleona, Entomobryidae) new to science, with remarkable secondary sexual characters
FIGURE 6. Seira raptora sp. n., femur of the male left first leg; a — femur lateral view showing the femoral ventral broadening with spines in the apex, SEM 370x; b — close of the file of spines at the ventral distal apex of the femur, SEM 1150x.
FIGURE 4 in Two Seira Lubbock 1869 (Collembola, Arthropleona, Entomobryidae) new to science, with remarkable secondary sexual characters
FIGURE 4. Seira raptora sp. n., labial triangle, showing seta r normal and smooth, M12 and E feathered; SEM 1650x.
FIGURES 108–111, 113–125 in Gliwiczia gen. nov. a new monoraphid diatom genus from Lake Baikal with a description of four species new for science
FIGURES 108–111, 113–125: Gliwiczia calcar comb. nov. Figs 108–111, 113–123: Figs 108–109: Valve view of rapheless valves from type material. Fig. 110. Original drawing of Achnanthes calcar from Cleve (1891). Fig. 111. Drawing from Cleve (1891) that he noted as possible belonging to raphe valve of Achnathes calcar. Fig. 112. Raphe valves abundant in type material comparable with fig. 111 and belonging to Cavinula spp. Figs 113–120. Valve view of rapheless valves of Gliwiczia cf. calcar comb. nov. from Hoton Nur lake, Mongolia. Figs 121, 122. Valve view of rapheless valves of Gliwiczia cf. calcar comb. nov. from Lake Dalnee, Kamchatka. Fig. 123. Valve view of raphe valves of Gliwiczia cf. calcar comb. nov. from Lake Dalnee, Kamchatka. Figs 124, 125. Internal view of rapheless valves of Gliwiczia cf. calcar comb. nov. from Lake Dalnee, Kamchatka. (108, 109, 112–123: LM; 110, 111: drawings; 124,125: SEM). Scale bars: 10 µm.
FIGURES 67–88 in Gliwiczia gen. nov. a new monoraphid diatom genus from Lake Baikal with a description of four species new for science
FIGURES 67–88: Gliwiczia latarea sp. nov. Figs 67–88: Figs 67–78: Valve view of rapheless valves. Fig. 67: arrow indicates presence of cavum in sternum valve. Figs 79–83: Valve view of raphe valves. Fig. 79: arrow indicates presence of cavum in raphe valve. Fig. 84. Internal view if raphe valve note presence of cavum. Figs 85, 87, 88: Internal valve view of rapheless valves, note presence of striae in central part opposite cavum. Fig. 86. Internal view of valve part with cavum (67–78: LM; 84–88: SEM). Scale bar: 10 µm.
FIGURES 63–66 in Gliwiczia gen. nov. a new monoraphid diatom genus from Lake Baikal with a description of four species new for science
FIGURES 63–66: Gliwiczia tenuis sp. nov. Figs 63–66: Fig. 63–64: Internal view of rapheless valves note shorter striae in central part of valve opposite cavum. Fig. 65. Internal view of raphe valve note presence of cavum. Fig. 66: External view of raphe valve. It is questionable whether this valve belongs to G. tenuis sp. nov. (SEM).
FIGURES 37–42 in Gliwiczia gen. nov. a new monoraphid diatom genus from Lake Baikal with a description of four species new for science
FIGURES 37–42: Gliwiczia skvortzowii sp. nov. Figs 37–42: Figs 37–40: Internal valve view of rapheless valves. Figs 41–42: Internal view of rapheless valves, note pair of foramina lips. (SEM).
FIGURES 43–62 in Gliwiczia gen. nov. a new monoraphid diatom genus from Lake Baikal with a description of four species new for science
FIGURES 43–62: Gliwiczia tenuis sp. nov. Figs 43–66: Figs 43–55: Valve view of rapheless valves. Fig. 43: arrow indicates presence of cavum in sternum valve. Figs 56–58: Valve view of raphe valves. Fig. 58: arrow indicates presence of cavum in raphe valve. Figs 59: Internal valve view of rapheless valve. Fig. 60 Internal view of valve part with cavum. Figs 61, 62. Internal view of areolae covered by circular foramina, come off from areolae in fig. 61 (43–58: LM; 59–62: SEM). Scale bar: 10 µm.
FIGURES 89–107 in Gliwiczia gen. nov. a new monoraphid diatom genus from Lake Baikal with a description of four species new for science
FIGURES 89–107: Gliwiczia vixcalcar sp. nov. Figs 89-107: Figs 89–101: Valve view of rapheless valves. Fig. 89: arrow indicates presence of cavum in sternum valve. Figs 102, 103: Valve view of raphe valves. Fig. 103: arrow indicates presence of cavum in raphe valve. Figs 104–106: Internal valve view of rapheless valves. Fig. 106. Internal view of valve part, note the pore occlusion. Fig. 107: Internal view of raphe valve note presence of cavum (89–103: LM; 104–107: SEM). Scale bar: 10 µm.
FIGURES 33–36 in Gliwiczia gen. nov. a new monoraphid diatom genus from Lake Baikal with a description of four species new for science
FIGURES 33–36: Gliwiczia skvortzowii sp. nov. Figs 33–36: Figs 33–36: External valve view of raphe valves. (SEM).
FIGURES 1–32 in Gliwiczia gen. nov. a new monoraphid diatom genus from Lake Baikal with a description of four species new for science
FIGURES 1–32: Gliwiczia skvortzowii sp. nov. Figs 1–34: Figs 1–19: Valve view of rapheless valves. Fig. 1: arrow indicates presence of cavum in sternum valve. Figs 20–32: Valve view of raphe valves. Fig. 20: arrow indicates presence of cavum in raphe valve. (LM). Scale bar: 10 µm.
Google Scholar as a Data Source for Research Assessment in the Social Sciences
<p>Column 1</p> <p>Source</p> <p>Data sources that the publications retrieved. Values for this column are “Google Scholar”, “Scopus”, and “Web of Science”.</p> <p>Column 2</p> <p>Authors</p> <p>The authors of the publications. This column is kept as additional information for verification of data. Not used in the analysis, it has not been standardized.</p> <p>Column 3</p> <p>Title</p> <p>Titles of the publications. For non-English publications, English titles, if available, are kept in this column. Otherwise, the original titles have been entered. The headings were checked and errors and omissions were corrected. Corrected titles are marked in red.</p> <p>Column 4</p> <p>Title translated with Google Translate</p> <p>In this Column, the English translated titles of the publications that do not have English titles are kept. Google Translate is used for detecting the language and translation. For publications with an English title, the expression [Title in English] has been entered. The translations of the original titles kept in this field were used in the analysis made through VOSviewer. It is marked in red as it is newly added data.</p> <p>Column 5</p> <p>Language</p> <p>Language of the publications. The languages of all publications were checked, missing data were completed and errors were corrected. If the language of the publication could not be determined, the value is [Not found]. The cells with addition or correction are marked in red.</p> <p>Column 6</p> <p>Document type</p> <p>Types of the documents. For all publications, publication type information was checked, missing ones were completed and corrections were made. All intervened cells are marked in red. Article and Review types are referred to as “Article” in the text.</p> <p>Column 7</p> <p>Full-text available</p> <p>Values for this column are “Yes” and “No”. The values for this column are Yes and No. If there is access to the full text of the publication via the web, "Yes", otherwise the "No" value has been entered.</p> <p>Column 8</p> <p>On research evaluation</p> <p>Values for this column are “Yes” and “No”. Using the title and/or abstract information, it was tried to determine whether the publications were related to the research evaluation. “Yes”, if found relevant, and “No” if not. It is marked in red as it is newly added data.</p> <p>Column 9</p> <p>Publication year</p> <p>The publication years of the documents. If the publication years are missing, they have been completed. The current publication years have been checked and corrected if necessary. If the year of publication could not be found, it is indicated as [Not found].</p> <p>Column 10</p> <p>English abstract</p> <p>Abstracts of the publications. If there is an accessible/available English abstract for the publication, it is kept in this column. [Not found/Not available] for missing values. Abstracts that were added, changed, corrected, or completed are marked in red.</p>
Bird strikes at commercial airports explained by citizen science and weather radar data
<p>1. Aircraft collisions with birds span the entire history of human aviation, including fatal collisions during some of the first powered human flights. Much effort has been expended to reduce such collisions, but increased knowledge about bird movements and species occurrence could dramatically improve decision support and proactive measures to reduce them. Migratory movements of birds pose a unique, often overlooked, threat to aviation that is particularly difficult for individual airports to monitor and predict: the occurrence of birds vary extensively in space and time at the local scales of airport responses.</p> <p>2. We use two publicly available datasets, radar data from the US NEXRAD network characterizing migration movements and eBird data collected by citizen scientists to map bird movements and species composition with low human effort expenditures but high temporal and spatial resolution relative to other large scale bird survey methods. As a test case we compare these results from weather radar distributions and eBird species composition with detailed bird strike records from three major New York airports.</p> <p>3. We show that weather radar based estimates of migration intensity can accurately predict probability of bird strikes, with 80% of the variation in bird strikes across the year explained by the average amount of migratory movements captured on weather radar. We also show that eBird based estimates of species occurrence can, using species' body mass and flocking propensity, accurately predict when most damaging strikes occur.</p> <p>4. Synthesis and applications: Our results highlight the power of federating datasets with movement and distribution data for developing better and more taxonomically and ecologically tuned models of likelihood of strikes occurring and severity of strikes. By better understanding when, and where, different species occur, airports across the world can predict seasonal periods of collision risks with greater temporal and spatial resolution; such predictions include potential to predict when the most severe and damaging strikes may occur.</p>
Community science butterfly data Northwest Arkansas
<p>This data set contains the butterfly behavior observations of community members who visited the Botanical Gardens of the Ozarks, as well as students in the Principles of Zoology and Animal Behavior courses at the University of Arkansas from spring 2017 to fall 2020. This data was used to assess the relationship between butterfly color and butterfly flower color choice, as well as the relationship between butterfly color, behavior, abundance, and cloud cover in Northwest Arkansas. It corresponds to "Engaging the community in pollinator research: the effect of wing pattern and weather on butterfly behavior" in Integrative and Comparative Biology. This data set includes the original data reported by community observers as well as the cleaned values used in the manuscript.</p>
Data from: A general-purpose spatial survey design for collaborative science and monitoring of global environmental change: the global grid
Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85° because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution ~600 m × 600 m) and require a 64-bit integer representation.
Do extraordinary science and technology scientists balance their publishing and patenting activities?
<p>Data of figures in the article titled "<strong>Do extraordinary science and technology scientists balance their publishing and patenting activities?."</strong></p>
Submission to Special Issue of Quantitative Science Studies "Scientific Knowledge Graphs and Research Impact Assessment"
<p>To be added</p>
Measuring Disagreement in Science
<p>This data set contains data used in the project <em>Measuring Disagreement in Science</em> documented in the following preprint: <a href="http://arxiv.org/abs/2107.14641">arXiv:2107.14641</a>. The data set contains the following data:</p> <ul> <li>The 65 disagreement queries (signal phrase, filter phrase, valid or not, number of citing sentences returned by the query, SQL query). Of the 65 queries, 23 passed our 80% validity threshold.</li> <li>The 455,625 citing sentences identified by the 23 validated disagreement queries (DOI, sentence sequence number, queries by which the sentence was identified, sentence text).</li> <li>The publications in which the citing sentences occur (DOI, publication year, number of sentences in the main text, main field to which the publication was assigned, meso-level field to which the publication was assigned, inferred gender of the first and last author).</li> </ul> <p>The data was retrieved through the Elsevier ScienceDirect API subject to <a href="https://www.elsevier.com/about/policies/text-and-data-mining">Elsevier’s policy for text and data mining</a>.</p>
Panel: Science Fiction & SETI
<p>Tessa Fisher moderated this panel about Science Fiction and SETI, featuring Kemi Ashing-Giwa, Elly Bangs, Sue Burke, Dr. Ruthanna Emrys, Arula Ratnakar, Dr. Peter Watts, and Mason Williams.</p>
Replication package for: Forbidden Fruits: The Political Economy of Science, Religion, and Growth
<p>The zipped folder contains all the files need to replicate the results reported in the paper “Forbidden Fruits: The Political Economy of Science, Religion, and Growth” by Roland Bénabou, Davide Ticchi, and Andrea Vindigni, published in the Review of Economic Studies.</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.