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FIG. 5 in Sibling species in the marine pollution indicator genus Pontonema (Nematoda: Oncholaimidae), with a description of mediterranea sp. nov.
FIG. 5. Pontonema mediterranea sp. nov. (a) Lateral view of male head-end; (b) lateral view of male tail-end; (c) lateral view of female tail.
FIG. 2 in Sibling species in the marine pollution indicator genus Pontonema (Nematoda: Oncholaimidae), with a description of mediterranea sp. nov.
FIG. 2. Multidimensional scaling (MDS) ordination for Pontonema males (upper) and females (lower) based on the complete character set. Dissimilarity measure for data is normalized Euclidean distance. Stress in both cases = 0.13. Sites K, C, T, S, G and B correspond to Kiel fjord, Cornelian Bay, Tyne Estuary, SeÁte, Garroch Head (Firth of Clyde) and Bay of Blanes, respectively.
FIG. 3 in Sibling species in the marine pollution indicator genus Pontonema (Nematoda: Oncholaimidae), with a description of mediterranea sp. nov.
FIG. 3. Multidimensional scaling (MDS) ordinations for Pontonema males based on character subsets. Dissimilarity measure for data is normalized Euclidean distance. Sites K, C, T, S, G and B correspond to Kiel fjord, Cornelian Bay, Tyne Estuary, SeÁte, Garroch Head (Firth of Clyde) and Bay of Blanes, respectively. (a) Ordination of character 15, stress= 0.17; (b) ordination of characters 7 and 15, stress = 0.02; (c) ordination of characters 3, 7 and 15, stress = 0.07; (d) ordination of characters 1, 7, 12 and 15, stress= 0.10.
FIG. 1 in Sibling species in the marine pollution indicator genus Pontonema (Nematoda: Oncholaimidae), with a description of mediterranea sp. nov.
FIG. 1. Diagrammatic representation of the characters quanti®ed. Based on P. alaeospicula as drawn by Bett and Moore (1988). The broken lines indicate the character measurements, and the numbers correspond to the character names as given in table 1. (a) Anterior oesophageal region, to extent of nerve ring. (b) Anterior region. Second subventral tooth is obscured by ®rst. The obvious characters of body length, maximum body diameter and length from vulva to anterior are not represented. (c) Posterior end male. (d) Posterior end female.
FIG. 4 in Sibling species in the marine pollution indicator genus Pontonema (Nematoda: Oncholaimidae), with a description of mediterranea sp. nov.
FIG. 4. Multidimensional scaling (MDS) ordinations for Pontonema females based on character subsets. Dissimilarity measure for data is normalized Euclidean distance. Sites K, C, T, S, G and B correspond to Kiel fjord, Cornelian Bay, Tyne Estuary, SeÁte, Garroch Head (Firth of Clyde) and Bay of Blanes, respectively. (a) Ordination of character 6, stress= 0.23; (b) ordination of characters 1 and 6, stress = 0.00; (c) ordination of characters 1, 8 and 9, stress = 0.09; (d) ordination of characters 1, 8, 9 and 10, stress= 0.17.
FIGURE 4. Discriminant function analysis depicting morphological differentiation within the C. vittatus-hansenae group. Close grey circle indicates C. vittatus Group II. Open circle denotes C. hansenae Group I. Closed black circle represents C. hansenae Group II in Re-evaluating the taxonomic status of Chiromantis in Thailand using multiple lines of evidence (Amphibia: Anura: Rhacophoridae)
FIGURE 4. Discriminant function analysis depicting morphological differentiation within the C. vittatus-hansenae group. Close grey circle indicates C. vittatus Group II. Open circle denotes C. hansenae Group I. Closed black circle represents C. hansenae Group II.
Sleep alterations as a function of 88 health indicators
<p>Supplementary data and code for the paper "Sleep alterations as a function of 88 health indicators".</p><p>Data files</p><p>- comorbidity.xlsx: the proportion of participants reporting all possible combinations of two binary health indicators. Proportions are shown relative to participants with the condition. For example, 18.97% of participants with "Allergy" also have "Angina" and 18.73% also have "Asthma", while 34.35% of participants with "Angina" and 64.73% of those with "Asthma" also have "Allergy". Values for continuous health indicators are missing as comorbidity is not defined for these.</p><p>- descriptive statistics.xlsx: the distribution of "No", "Yes", and "Missing" responses for each of the medical conditions and medications in the dataset.</p><p>- Rotatable scatterplot.fig: an interactive, manually rotatable MATLAB .fig file depicting the contents of Figure 6.</p><p>- Rotating scatterplot.mp4: a video of an automatically rotating version of the former</p><p> </p><p> </p>
Open and Reproducible Dataset of Bibliometric Indicators of Russia
<p>Version 1. Data collected on 03-04 November 2023.</p><p><i>by Ivan Sterligov, HSE University, </i><a href="mailto:ivan.sterligov@gmail.com"><i>ivan.sterligov@gmail.com</i></a></p><p>See readme.md for details</p><p>The dataset comes with python code that lets anyone collect and update all the data automatically, provided they set up a free access to Scopus API and conform to the rules of API usage set by Elsevier.</p><p>The dataset is a part of a broader academic project aimed at tracking dramatic changes in Russian publication output after 2022 and generally focuses on the period starting at 2018. This is an academic non-commercial research project and includes no Scopus metadata "as is", and no matadata on any particular paper, author or journal, only publication counts obtained using proprietary queries and journal lists constructed by its author. No citation metrics are used or published.</p><p>Dataset will be hopefully updated and new versions posted at Zotero. You can cite specific version DOI to be sure that you reference particular values. Also, the dataset as a whole has a common DOI which resolves to the newest version.</p>
Cyber security indicators
<p>Cyber security indicators: National Cyber security indicators (2020) and Secure Internet servers (per 1 million people) (2010-2020)</p>
Dataset containing all action and intervention indicators (KPI) in Valencia Demo Site for the MAtchUP project.
<p>KPIs added for interventions I1A, I2A and I2B, from June 2023 till September 2023.</p>
Dataset containing all action and intervention indicators (KPI) in Valencia Demo Site for the MAtchUP project.
Open the record for dataset details and reuse information.
Selection and application of agri-environmental indicators to assess potential technologies for nutrient recovery in agriculture (Data Sets)
Open the record for dataset details and reuse information.
Canadian Environmental Sustainability Indicators
Open the record for dataset details and reuse information.
Real-world vehicular source indicators for exhaust and non-exhaust contribution to PM2.5 during peak and off-peak hours using tunnel measurement
<p>The data are the species concentrations, traffic and meteorological information during the sampling periods in WJL tunnel, and the estimated function of E<sub>N</sub> curve under vehicle electrification.</p>
IntelComp Climate Change Indicators
<p>The IntelComp Climate Change Indicators dataset provides essential indicators for STI policymakers in the energy and agrifood sectors of Europe and Greece. It covers a broad spectrum of societal sectors including Science, Technology, Industry, ESG (Environmental, Social, and Governance), Human Resources, and Policy. Each indicator set is derived from specific analytics: Science indicators are created from publication analytics, Technology from patent analytics, Industry from company/industry analytics, ESGs from ESG analytics, Human Resources from Green Skills analytics, and Policy from regulation analytics.</p> <p>This dataset, a product of multiple AI-driven pipelines, is derived by processes on raw datasets (Tier 1) and analytics datasets (Tier 2, as described in this DMP) to produce a comprehensive collection of Tier 3 indicators. These indicators provide a detailed view of the current state and trends within the energy and agrifood sectors, assisting policymakers in agenda setting and policy formulation. The data bridges various aspects of societal development and offers a framework for understanding the intersection of science, technology, industry, and human resource development in these critical domains."</p>
Solar wind parameters and geomagnetic indices for severe (SYM-H < -100nT) geomagnetic storms over two solar cycles
<p><em><strong>Severe storms data set (1996-2022)</strong></em></p> <p><em>Lotz, Grant, Davel (2024).</em></p> <p><strong>Changes from previous version:</strong></p> <ul> <li>Previous version had errors in calculation of `HER_eh` and `ABK_eh`. The mistake is corrected in this version.</li> <li>This version includes storms from 1996-2022. Previous version had 1996-2019.</li> </ul> <p>This data set is curated from 1-minute time resolution OMNI [4] and INTERMAGNET [7] data, spanning the period 1996 - 2022.</p> <p>It consists of all geomagnetic storms that reached minimum SYM-H (Symmetric-H index) of < -100 nT. We classified these as "severe" storms. There are 115 such events in the period 1996 - 2018. No severe storms detected between 2007-2010 or in 2019. Each file in the set contains the events in that year (YYYY) in the format "severe_YYYY.pickle". The geomagnetic storms are identified by the procedure described in [1], with thresholds -100 nT and -20 nT (see [1] for further detail).</p> <p>The data sets are Pandas "dataframes" [2], saved in the Python "pickle" format [3]. Each dataframe contains 23 variables, listed in the table below, with their description and unit.</p> <p>Fourteen of the parameters are solar wind plasma and magnetic field parameters from the High Resolution OMNI data set [4].</p> <p>There are 7 geomagnetic variables:</p> <ul> <li>one is the global Sym-H index,</li> <li>four are H-component geomagnetic observations from Hermanus, South Africa (HER: 34.4 S, 19.22 E) and Abisko, Sweden (ABK: 68.35 N, 18.82 E) and their time derivatives</li> <li>two are the e_h index [5] calculated at HER and ABK</li> </ul> <p>Finally, we list the time shift applied to each data point to shift to the bow shock nose (described for OMNI at [6]) and a string identifier for each geomagnetic storm.</p> <p> </p> <table> <tbody> <tr> <td><strong>#</strong></td> <td><strong>Name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>1</td> <td>time_shift</td> <td> <p>Time shift applied to shift solar wind parameters to bow shock</p> </td> <td>s</td> </tr> <tr> <td>2</td> <td>Bt</td> <td>Magnitude of interplanetary magnetic field (IMF)</td> <td>nT</td> </tr> <tr> <td>3</td> <td>Bx</td> <td>X-component of IMF</td> <td>nT</td> </tr> <tr> <td>4</td> <td>By_GSE</td> <td>Y-component of IMF (GSE coordinates)</td> <td>nT</td> </tr> <tr> <td>5</td> <td>Bz_GSE</td> <td>Z-component of IMF (GSE coordinates)</td> <td>nT</td> </tr> <tr> <td>6</td> <td>By_GSM</td> <td>Y-component of IMF (GSM coordinates)</td> <td>nT</td> </tr> <tr> <td>7</td> <td>Bz_GSM</td> <td>Z-component of IMF (GSM coordinates)</td> <td>nT</td> </tr> <tr> <td>8</td> <td>Vsw</td> <td>Bulk solar wind speed</td> <td>km/s</td> </tr> <tr> <td>9</td> <td>Vx</td> <td>X-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>10</td> <td>Vy</td> <td>Y-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>11</td> <td>Vz</td> <td>Z-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>12</td> <td>Np</td> <td>Proton number density in solar wind plasma</td> <td>#/cc</td> </tr> <tr> <td>13</td> <td>Temp</td> <td>Temperature of solar wind plasma</td> <td>K</td> </tr> <tr> <td>14</td> <td>Pd</td> <td>Dynamic or flow pressure of solar wind plasma</td> <td>nPa</td> </tr> <tr> <td>15</td> <td>E_field</td> <td>Solar wind electric field</td> <td>mV/km</td> </tr> <tr> <td>16</td> <td>SymH</td> <td>Symmetric-H index of the geomagnetic field</td> <td>nT</td> </tr> <tr> <td>17</td> <td>HER_H</td> <td>H-component of the geomagnetic field at Hermanus</td> <td>nT</td> </tr> <tr> <td>18</td> <td>HER_dHdt</td> <td>Time derivative of HER_H</td> <td>nT/min</td> </tr> <tr> <td>19</td> <td>HER_eh</td> <td>e_h index at HER</td> <td>nT/min</td> </tr> <tr> <td>20</td> <td>ABK_H</td> <td>H-component of the geomagnetic field at Abisko</td> <td>nT</td> </tr> <tr> <td>21</td> <td>ABK_dHdt</td> <td>Time derivative of ABK_H</td> <td>nT/min</td> </tr> <tr> <td>22</td> <td>ABK_eh</td> <td>e_h index at ABK</td> <td>nT/min</td> </tr> <tr> <td>23</td> <td>Storm_ID</td> <td>String identifier of the geomagnetic storm</td> <td>-</td> </tr> </tbody> </table> <p> </p> <p><em><strong>References</strong></em></p> <ol> <li>Lotz, S. I., & Danskin, D. W. (2017). <em>Space Weather</em>, 15, 1347– 1356. <a href="https://doi.org/10.1002/2017SW001662">https://doi.org/10.1002/2017SW001662</a></li> <li><a href="https://omniweb.gsfc.nasa.gov/ow_min.html">https://omniweb.gsfc.nasa.gov/ow_min.html</a></li> <li><a href="https://pandas.pydata.org/">https://pandas.pydata.org/</a></li> <li><a href="https://docs.python.org/3/library/pickle.html">https://docs.python.org/3/library/pickle.html</a></li> <li>Wintoft, P., Wik, M., & Viljanen, A. J. Space Weather Space Clim., 5, A7 (2015). <a href="http://dx.doi.org/10.1051/swsc/2015008">http://dx.doi.org/10.1051/swsc/2015008</a></li> <li><a href="https://omniweb.gsfc.nasa.gov/html/ow_data.html#time_shift">https://omniweb.gsfc.nasa.gov/html/ow_data.html#time_shift</a></li> <li><a href="https://www.intermagnet.org/index-eng.php">http://www.intermagnet.org</a></li> </ol>
Supplementary material 2 from: Grabowska J, Płóciennik M, Grabowski M (2024) Detailed analysis of prey taxonomic composition indicates feeding habitat partitioning amongst co-occurring invasive gobies and native European perch. NeoBiota 92: 1-23. https://doi.org/10.3897/neobiota.92.116033
Relative abundance of prey categories (%N) (number of given prey category in relation to total number of prey) identified in fish guts at sites Z, R, B in the Western Bug River in August 2007
Supplementary material 1 from: Grabowska J, Płóciennik M, Grabowski M (2024) Detailed analysis of prey taxonomic composition indicates feeding habitat partitioning amongst co-occurring invasive gobies and native European perch. NeoBiota 92: 1-23. https://doi.org/10.3897/neobiota.92.116033
Relative abundance of species (%N) in fish assemblages found at sites Z, R, B in the Western Bug River in August 2007 (Penczak et al. 2010)
Fig. 3 in Fluctuating asymmetry and oxidative stress indicate environmental stress of Cane toads Rhinella marina
Fig. 3. Antioxidant enzyme activity and oxidative stress of R. marina. a) SOD activity, b) Total antioxidant capacity units are Trolox nmol, c) Lipid peroxidation. Different letters show different means.
Fig. 2 in Fluctuating asymmetry and oxidative stress indicate environmental stress of Cane toads Rhinella marina
Fig. 2. а) Spatial conformation of landmarks to testing FA of the body of toads that consisted of 22 landmarks, where landmarks 1 and 12 were located on axis of the symmetry. b) The Procrustes consensus for all original and reflected configurations, being perfectly symmetric. c) The fluctuating asymmetry is shown as the difference between the averages of all original and reflected configurations (black dots). For comparison, the plot shows the overall symmetric (grey dots) and asymmetric Procrustes consensus and reflects variation of landmarks in left and right sides.
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.