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6,025 results for “Science of science”
XMM-Newton Survey Science Center Survey of the Galactic Plane
Many different classes of X-ray sources contribute to the Galactic landscape at high energies. Although the nature of the most luminous X-ray emitters is now fairly well understood, the population of low-to-medium X-ray luminosity (L<sub>X</sub> = 10<sup>27</sup> - 10<sup>34</sup> erg/s) sources remains much less studied, our knowledge being mostly based on the observation of local members. The advent of wide-field and high-sensitivity X-ray telescopes such as XMM-Newton now offers the opportunity to observe this low-to-medium L<sub>X</sub> population at large distances. This study reports the results of a Galactic plane survey conducted by the XMM-Newton Survey Science Centre (SSC). Beyond its astrophysical goals, this survey aims at gathering a representative sample of identified X-ray sources at low latitude that can be used later on to statistically identify the rest of the serendipitous sources discovered in the Milky Way. The survey is based on 26 XMM-Newton observations, obtained at |b| < 20 degrees, distributed over a large range in Galactic longitudes and covering a summed area of 4 deg<sup>2</sup>. The flux limit of this survey is 2 x 10<sup>-15</sup> erg/cm<sup>2</sup>/s in the soft (0.5 - 2 keV) band and 1 x 10<sup>-14</sup> erg/cm<sup>2</sup>/s in the hard (2 - 1 2keV) band. A total of 1319 individual X-ray sources have been detected. Using optical follow-up observations supplemented by cross-correlation with a large range of multi-wavelength archival catalogs, the authors identify 316 X-ray sources. This constitutes the largest group of spectroscopically identified low-latitude X-ray sources at this flux level. The majority of the identified X-ray sources are active coronae with spectral types in the range A to M at maximum distances of ~1 kpc. The number of identified active stars increases towards late spectral types, reaching a maximum at K. Using infrared colors, the authors classify 18% of the stars as giants. The observed distributions of F<sub>X</sub>/F<sub>V</sub>, X-ray and infrared colors indicates that their sample is dominated by a young (100 Myr) to intermediate (600 Myr) age population with a small contribution of close main-sequence or evolved binaries. The authors find other interesting objects such as cataclysmic variables (d ~ 0.6 - 2 kpc), low-luminosity high-mass stars (likely belonging to the class of Gamma-Cas-like systems, d ~ 1.5 - 7 kpc), T Tauri and Herbig-Ae stars. A handful of extragalactic sources located in the highest Galactic latitude fields could be optically identified. For the 20 fields observed with the EPIC pn camera, the authors have constructed log N(>S) - log S curves in the soft and hard bands. In the soft band, the majority of the sources are positively identified with active coronae and the fraction of stars increases by about one order of magnitude from b = 60 degrees to b = 0 degrees at an X-ray flux of 2 x 10<sup>-14</sup> erg/cm<sup>2</sup>/s. The hard band is dominated by extragalactic sources, but there is a small contribution from a hard Galactic population formed by CVs, HMXB candidates or Gamma-Cas-like systems and by some active coronal stars that are also detected in the soft band. At b = 0 degrees, the surface density of hard sources brighter than 1 x 10<sup>-13</sup> erg/cm<sup>2</sup>/s steeply increases by one order of magnitude from l = 20 degrees to the Galactic center region (l = 0.9 degrees). This HEASARC table contains 739 X-ray sources detected in the 26 different fields observed in this study and listed in Tables 8 - 33, inclusive, of the reference paper. These 739 sources have the best XMM quality, i.e. the summary flag sum_flag which contains information about flags set automatically and manually for a given source is zero, meaning that there are no negative flags for the source detection, have either a 2MASS, USNO, GSC, or SDSS counterpart, whatever the probability of identification is, or have some information via SIMBAD or the authors own imaging or spectroscopic observations. For each X-ray source, its X-ray parameters are summarized, listing the pn count rates, and information on optical and infrared counterparts is provided. The properties of the 26 target fields are given in Table 1 of the reference paper, along with the breakdown of source classes in each field. This table was created by the HEASARC in May 2013 based on <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/A+A/553/A12">CDS Catalog J/A+A/553/A12</a>, the 26 files table8.dat to table33.dat, inclusive. This is a service provided by NASA HEASARC .
PSP Integrated Science Investigation of the Sun, Energetic Particle Instrument-Hi (ISOIS EPI-Hi) High Energy Telescope (HET) Rates, Level 2 (L2), 5 min Data
Parker Solar Probe, PSP, Integrated Science Investigation of the Sun, IS☉IS, Energetic Particle Instrument, EPI-Hi, High Energy Telescope, HET, Rates: The Epoch time tags indicate midpoint of integration. For information concerning use of the IS☉IS data please refer to the Energetic Particle Data User Guide available from the PSP IS☉IS Science Operations Center, SOC, web site hosted by the University of New Hampshire. The energetic particle pitch angle calculations are made possible via use of magnetic field data provided by the PSP FIELDS team.The public data reflect current instrument calibration as determined by the IS☉IS science team. Calibration efforts are ongoing and the public data will be updated over the course of the mission based on improved understanding of instrument response to the near-Sun energetic particle environment. IS☉IS visualization tools are provided as a quicklook utility for the community to better access the IS☉IS data and are also under continual development. While we make every effort to ensure their accuracy, the IS☉IS team cannot guarantee that they are error-free. For questions regarding the use of IS☉IS data, please contact Colin Joyce (cjjoyce@princeton.edu).Refer to the following web site concerning the Rules of use for the IS☉IS energetic particle data: https://spp-isois.sr.unh.edu/ISOIS_Terms_of_Use.html.The PSP IS☉IS effort is funded as part of the NASA Parker Solar Probe mission under contract NNN06AA01C. Use of any PSP IS☉IS data in publications or presentations should include the following text for acknowledgement and also cite the PSP IS☉IS instrument suite publication:Acknowledgement: Thanks to the Integrated Science Investigation of the Sun (IS☉IS) Science Team (PI: D. J. McComas, Princeton University).Citation: McComas, D. J. (2020). PSP Integrated Science Investigation of the Sun, Energetic Particle Instrument-Hi (ISOIS EPI-Hi) High Energy Telescope (HET) Rates, Level 2 (L2), 5 min Data [Data set]. NASA Space Physics Data Facility.
DEEP IMPACT 9P/TEMPEL 1 ENCOUNTER - RADIO SCIENCE DATA V1.0
This data set contains raw radio science data from the Deep Impact flyby spacecraft, collected during the encounter with comet 9P/Tempel 1.
MO MARS RADIO SCIENCE 1 ORIGINAL/INTERMEDIATE DATA REC V1.0
The data set consists of several CD-WO volumes which contain radiometric and open loop data acquired from the Mars Observer spacecraft during its Cruise between Earth and Mars. The basic data are supplemented by ancillary data including DSN weather files and media calibrations, spacecraft maneuver information, and operations schedules. There are fourteen data types.
CLEM1 LUNAR RADIO SCIENCE RAW BISTATIC RADAR V1.0
The Clementine Bistatic Radar Raw Data Archive (BSR-RDA) is a time-ordered collection of raw and partially processed data from bistatic radar scattering experiments conducted using the Clementine spacecraft while it orbited the Moon.
Figure 1 in The five deadly sins of science publishing
Figure 1. Cartoon of demonstrations that would seem appropriate outside journal offices.
Databases of "Economic criteria of Policosanol extraction" article: standardization, merge and removing duplication of results found in the Scopus Elsevier and Web of Science bases
<p>These eight databases derives from the processes of standardization, merge and removing duplication of results found in the Scopus Elsevier and Web of Science bases, in CSV format to be used with software suitable for replicating the results, new statistical, bibliometric, semantic and other treatments.</p> <p>DatabaseE1 - Extraction - BUSINESS ATTRACTIVENESS. </p> <p>DatabaseE2 - Extraction - COMMERCIAL AND EXPORT FEASIBILITY </p> <p>DatabaseE3 - Extraction - TECHNICAL AND INDUSTRIAL VIABILITY: </p> <p>DatabaseE4 - Extraction - ECOEFICIENCY</p> <p>DatabaseEG1 - General - BUSINESS ATTRACTIVENESS </p> <p>DatabaseEG2 - General - COMMERCIAL AND EXPORT FEASIBILITY </p> <p>DatabaseEG3 - General - TECHNICAL AND INDUSTRIAL VIABILITY: </p> <p>DatabaseEG4 - General - ECOEFICIENCY</p> <p>It is constitutive of the article entitled " Economic criteria of Policosanol extraction: a systematic literature review and patent prospection", by professors Rilton Gonçalo Bonfim Primo, Jesus Martín-Gil, Fernando Cardoso Pedrão, Carlos Narciso Bouza and Ricardo de Araújo Kalid.</p>
Artikel- und Buchpublikationen aus der Soziologie: Open Science, Open Access zu Texten, Open Access zu Forschungsdaten, Open Access zu Forschungssoftware, Altmetrics [full record]
<p>Die Daten umfassen Stichproben an je 100 Journalartikeln und Buchpublikationen aus der Soziologie. Ausgwählt wurden Veröffentlichungen aus deutschsprachigen und nicht-deutschsprachigen Ländern.</p> <p>Für alle Artikel der Stichprobe wurde geprüft,</p> <p>1) ob sie im Gold Open Access verfügbar waren<br> 2) ob sie im Green Open Access verfügbar waren<br> 3) auf welchem Repository-Typ sie (im Falle einer Green Open Access Publikation) verfügbar waren<br> 4) seit wann sie im Green Open Access verfügbar waren<br> 5) ob Forschungsdaten zum Artikel verfügbar waren<br> 6) ob Forschungssoftware zum Artikel verfügbar war<br> 7) wie häufig der Artikel in Google Scholar zitiert wurde<br> 8) wie häufig der Artikel im Web of Science zitiert wurde<br> 9) wie häufig der Artikel in Scopus zitiert wurde<br> 10) wie häufig der Artikel in den Sociological Abstracts zitiert wurde<br> 11) wie viele Mendeley User Counts der Artikel aufwies<br> 12) wie häufig der Artikel getwittert wurde (Datenquelle: Topsy)</p> <p> </p> <p>Für alle Bücher der Stichprobe wurde geprüft,</p> <p>1) ob sie im Gold Open Access verfügbar waren<br> 2) ob sie im Green Open Access verfügbar waren<br> 3) auf welchem Repository-Typ sie (im Falle einer Green Open Access Publikation) verfügbar waren<br> 4) seit wann sie im Green Open Access verfügbar waren<br> 5) wie häufig das Buch in Google Scholar zitiert wurde<br> 6) wie häufig das Buch im Web of Science zitiert wurde (als Cited-Reference-Analyse)<br> 7) wie häufig das Buch in Scopus zitiert wurde<br> 8) wie häufig das Buch in der International Bibliography of the Social Sciences zitiert wurde<br> 9) wie viele Mendeley User Counts das Buch aufwies<br> 10) wie häufig das Buch getwittert wurde (Datenquelle: Topsy)</p> <p><br> Da die Impact-Informationen unter Copyright-Schutz der Datenbank-Anbieter stehen, können diese nicht frei zugänglich gemacht werden. Es existiert jedoch eine offen verfügbare Version dieser Datensammlung, die allerdings keine Impact-Informationen enthält.</p>
Global data on caudal condition in geckos based on iNaturalist images with supplementary material for the manuscript: Human footprint is changing how geckos respond to enemies: evidence from global citizen science data
<h3>This repository contains the supplementary material and dataset linked to the paper "Human footprint is changing how geckos respond to enemies: evidence from global citizen science data" </h3> <p>we provide two supplementary tables in .csv and .xlsx files (Table S1 and S2), a figure S1, and the two datasets in .csv (data_28_03.csv is the raw dataset, and the table S3 is the dataset compile from the literature review). We also include a crosstable (table2.html) comparing metrics based on the variables raised during the project among gecko species.</p> <h3> </h3>
Data from: Deep Sea Spy: an online citizen science annotation platform for science and ocean literacy
<p>Data sets of buccinid <em>Buccinum thermophilum </em>and crab <em>Segonzacia mesatlantica </em>after identifying unique groups (i.e. individuals) — using the updated version (v0.0.3) of the <a title="Deep Sea Spy - deeptools" href="https://github.com/DeepSeaSpy/deeptools">deeptools</a> package — among Deep Sea Spy citizen participants and expert.</p> <p>Data cleaning : annotated buccinids in background removed in <span><a href="https://zenodo.org/api/records/14203506/draft/files/CS_groups.csv/content" target="_blank" rel="noopener noreferrer">CS_groups.csv</a></span></p> <p>R script used to generate and analyse the dataset.</p> <p><strong>Please, use this version (v5).<br></strong></p>
Dataset for Towards Understanding Performance Bugs in Popular Data Science Libraries
<div> <div>This dataset contains 138 performance bugs in data science popular libraries, and their impacts, root causes, locating and fixing challenge, and fixing strategy.</div> <div>Our replication package consists of three main folders:RQ1&2_Impacts_and_Root_Causes, RQ3_Root_Causes_Locating_Fixing_Effort_Challenge and RQ4_Fixing_Strategy.</div> <br> <div>RQ1&2_Impacts_and_Root_Causes</div> <br> <div>In this folder we first placed the identified impact (Explicit and Implicit). Then we gave the identified symptoms and root cause taxonomy. In each file (corresponding to each iteration), we provided the repo name, issue number, and the label (symptom and root cause).</div> <br> <div>RQ3_Root_Causes_Locating_Fixing_Effort_Challenge</div> <br> <div>We provided the number of comments, lines of changed code and issue duration involved in handling performance bugs. Furthermore, the challenge in resolving these bugs in data science libraries are identified here.</div> <br> <div>RQ4_Fixing_Strategy</div> <br> <div>We provided the identified fixing strategy with small LOC. In the file, we provided the repo name, issue number, and the label (fixing strategy).</div> </div>
Citizen science projects and protocols for coastal and marine benthos
<p>Dataset of a systematic review of citizen science projects and protocols for coastal and marine benthos.</p>
FIG. 1 in A Common Love of Science: The One-Hundredth Meeting of the American Society of Ichthyologists and Herpetologists
FIG. 1. Announcement of the first meeting of the ASIH (1916).
Shaping the Future The Transformative Potential of AI in Computer Science VET Programs
Open the record for dataset details and reuse information.
FIGURE 6 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
FIGURE 6. Halyomorpha halys (Stål, 1855): A. Adult from Giresun, B. Nymph from the same locality.
FIGURE 7 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
FIGURE 7. Stictocephala bisonia Kopp et Yonke, 1977, specimen on fern leaf from Bulancak, Giresun.
FIGURE 3 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
FIGURE 3. Corythucha ciliata (Say, 1832): A. Specimen from Samsun, B. Specimen from Bursa.
MAP 2 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
MAP 2. Distribution of Corythucha arcuata (Say, 1832) in Turkey.
MAP 6 in Insecta non gratae: New Distribution Records of Eight Alien Bug (Hemiptera) Species in Turkey with Contributions of Citizen Science
MAP 6. Distribution of Halyomorpha halys (Stål, 1855) in Turkey.
Mapping Sustainable Development Goals to Citizen Science projects - Datasets
<p> This work presents opportunities, achievements, and future challenges in using computational analytics to better understand the connection between CS and the SDGs. The work in its status does not fully cover SDGs in CS, but it evaluates and shows the potential of the text-classification techniques for identifying SDGs in CS project descriptions and for assessing trends in connection of CS and SDGs based on available data. </p> <p>A total of 56 websites have been used to extract randomly 208 project descriptions from the CS Track database collected during 2019-2022.</p> <p><strong>Content and grouping: </strong></p> <ul> <li> <p>Compiled list of keywords for SDG mapping.</p> </li> <li> <p>Spreadsheet containing training data from SciStarter (set of projects associated with SDGs)</p> </li> <li> <p>Spreadsheet containing the list of projects associated with SDGs in relation to the different automatic classifiers (nCoder, ESA and OSDG) and manual coding.</p> </li> <li> <p>Presentation in CS Track ECSA event (8th October 2022, Berlin): CS-Track database: a central database of CS projects in Europe that can be key to understand the connection of CS and SDGs “Understanding the nature of Citizen Science in a rapidly changing world” </p> </li> <li> <p>The associated full report resulting from this study is in progress. Please contact the corresponding authors for further information.</p> </li> </ul>
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.