Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
176
datasets available to search
ShareScore release 0.9.0
Dataset results
176 results for “gathering”
Fig.1. Funnel trap for gathering living RESULTS AND DISCUSSION D.latissimus a in Conceptual Application Of Dytiscus Latissimus Linnaeus, 1758 (Dytiscidae, Coleoptera) Gathering Methods In Natural Habitat
Fig.1. Funnel trap for gathering living RESULTS AND DISCUSSION D.latissimus a result of the individual (from Jan G.M. Cuppen and other): As many years application of the material gathering methods was 1 – float; 2- rubber; 3 – aeration net; a possibility to catch on regular and predictable 4 – entrance Ø 30 mm. basis a required number of animals for our laboratory research. itself. This can be identified from locating specific cuts on it. (Vahrusevs 2009) The key to a successful egg collection is a search of the plants used by beetles during their Below is an example from field notes of catching reproduction period, as well as the correct beetles during the autumn season: locating of clutches. Such plants in our reservoir "The research on the reservoir was done during under study are Carex acuta, Carex rostrata, 11.10.2009 - 28.10.2009. Caltha palustris. One has to identify suitable plants in the locations of beetles' clutches The weather conditions were favourable. It was (usually located on the sunny side along the quite warm and windless all this time in order to coast of a water reservoir). The person gathering work comfortably. Water temperature was +7-8 ŗ the material has to grasp with fingers the C. We worked as usual on the proven location. underwater part of the identified plant stem Coordinates in Google Earth (latitude 55 ° 52'33 reaching almost to its base, and slowly palpate it.95 "C; longitude 26 ° 35'18.78" H). by letting it through the fingers in the upwards direction. The upwards direction is imperative, We prepared the traps in advance (they had to as stems of many sedge plants have microscopic be repaired and mended, and modernized a little. thorns which are rooting upwards. If this method (Fig.2.). is not followed, an injury of a palm can occur. A stem of the plant which has protruding bumps We took along 21 trap to the water reservoir. The on it guarantees the presence of a clutch. A stem bait was pieces of beef heart. The traps were in its normal condition is usually smooth and placed along the coastline. The first "throw" was often flat. Such plants as Caltha palustris which kept in the water for almost a week with regular have fleshy stem are to be studied in addition check ups made every day or every second day. visually as eggs can be located inside the stem Traps were installed partly dipped under water.
РиС. 1. Карта мест сбора в БоΛьшехехцирском заповеΑнике и его окрестностях. Номера точек сбора и их описания см. в тексте Fig. 1. The map of gathering places in the Bolshekhekhtsirsky Nature Reserve and its surroundings. The numbers of collection points and their descriptions are given in the text in Heteroptera (Insecta, Heteroptera) of the Bolshekhetsirsky Nature Reserve, Khabarovsky Krai, Russia
РиС. 1. Карта мест сбора в БоΛьшехехцирском заповеΑнике и его окрестностях. Номера точек сбора и их описания см. в тексте Fig. 1. The map of gathering places in the Bolshekhekhtsirsky Nature Reserve and its surroundings. The numbers of collection points and their descriptions are given in the text
R replication code and data for: Do modern hunter-gatherers live in marginal habitats?
<p>Data and R replication code for testing the Marginal Habitat Hypothesis. The R code files contain all models and are organized by the figures they generate for the associated paper.</p> <p>The data are sourced from:</p> <p>1) the Standard Cross Cultural Sample (SCCS).</p> <p>2) NASA Moderate Resolution Imaging Spectroradiometer (MODIS) NPP data (MOD17A3 algorithm) from Numerical Terra Dynamic Simulation Group at the University of Montana.</p> <p>3) Marine Ecoregions Of the World (MEOW): <a href="http://maps.tnc.org/files/metadata/MEOW.xml">http://maps.tnc.org/files/metadata/MEOW.xml</a></p> <p>4) Terrestrial Ecoregions Of the World (TEOW): <a href="http://maps.tnc.org/files/metadata/TerrEcos.xml">http://maps.tnc.org/files/metadata/TerrEcos.xml</a></p>
Hyperspectral Unmixing Dataset of UAV Gathered Blueberry Field Data
<p>Hyperspectral Unmixing dataset created from hyperspectral data gathered usign SPECIM push-broom hyperspectral camera mounted on a UAV flying over blueberry fields in Lithuania. Created dataset contains six data classes and linear mixtures from raw data. All data is given in Python Numpy array .npy files. </p> <p>To keep the annonimity of data owners only the non georectified data cubes are published.</p> <p>Dataset includes three hyperpectral data cubes of blueberry fields and Dark reference cube to show camera noise.</p> <p><strong>Data structure:</strong></p> <p>cube_1, cube_2, cube_2 and Dark - folder with hyperspectral data.</p> <p>calibration_data.npy - Data of calibration plates (with 40%, 10% and 5% reflectance values) from hyperspectral flight that were used to conver DN to reflectance.</p> <p>endmembers.npy - Spectra of siz endmembers (classes) used in the dataset.</p> <p><strong>cube_x folders include:</strong></p> <p>class_matrix.npy - Numpy matrix file of hyperspectral image classes (classification results)</p> <p>raw_data.npy - Hyperspectral cube created from raw camera data (with DN values)</p> <p>data_cube_3_0.npy and abundances_3_0.npy - Classified and mixed (using slidin window of 3x3 pixels with no overlap) hyperspectral data cube and class abundance matrix. </p> <p>endmember_errors.npy - matrix of variation for each of endmembers in the hyperspectral cube (used for evaluation mostly.)</p> <p><strong>Dark folder:</strong></p> <p>includes data folder with raw-dark_fl1_20230830_140006_radiance.dat and .hdr ENVI raster data files (library like <em>rasterio</em> for Python can used to read these files). This is the dark (0% reflectance) data cube and header file used in calibration.</p>
Figure 9 in Functional morphology and light-gathering ability of podocopid ostracod eyes and the palaeontological implications
Figure 9. Character state distribution of podocopids on a phylogenetic tree proposed by Yamaguchi (2003). The families not shown in bold face were not examined in this study, and estimations of their eye types were mainly based on the figures and descriptions of the cuticular lens by Benson et al. (1961) and Van Morkhoven (1963).
Figure 7. A in Functional morphology and light-gathering ability of podocopid ostracod eyes and the palaeontological implications
Figure 7. A, measurements of the four types of podocopid species (LG1, LG2, MG, and HG) plotted on Figure 6. B, theoretical models of possible podocopid ostracod eyes accommodated in the morphospace.
Figure 4. The setting for the phototactic experiments. A in Functional morphology and light-gathering ability of podocopid ostracod eyes and the palaeontological implications
Figure 4. The setting for the phototactic experiments. A white fibre-optic illumination (150 W) from a halogen lamp was exposed to the podocopid ostracod at the distance of D and an angle of 45°.
Figure 3. A in Functional morphology and light-gathering ability of podocopid ostracod eyes and the palaeontological implications
Figure 3. A, cuticular lens–tapetum model: bold dots in the lens represent the centre of the arc described by the outer and inner surface of the lens; r1, the radius of curvature of the outer lens surface; r2, the radius of curvature of the inner surface; e1, the thickness of the lens; e2, the distance between the lens and the tapetum; d, the diameter of a spherical mirror. B, an example of the computer simulation of ray tracing: Lt, the sum of the total lengths of segments of rays passing through the rhabdom (shown by the grey segments); n, the total number of rays of incident light.
Figure 2 in Functional morphology and light-gathering ability of podocopid ostracod eyes and the palaeontological implications
Figure 2. Idealized cross-sections showing the two possible ultrastructures of the podocopid lateral ocellus viewed from the anterior side. A, the pigment cup connects with the cuticular lens by connective tissue. B, the pigment cup is buried in the calcified valve, and the lens cell is degenerate.
Figure 1. The podocopid ostracod Aurila kiritsubo Yajima, 1982 in Functional morphology and light-gathering ability of podocopid ostracod eyes and the palaeontological implications
Figure 1. The podocopid ostracod Aurila kiritsubo Yajima, 1982; the right valve with a close-up of the cuticular lens. Scale bar = 100 µm.
Figure 8 in Functional morphology and light-gathering ability of podocopid ostracod eyes and the palaeontological implications
Figure 8. The relationship between the total irradiance of the microhabitat (%) of each species (where the total irradiance at the sea surface is 100%) and the light-gathering ability of eyes (G).
Figure 11. A in Functional morphology and light-gathering ability of podocopid ostracod eyes and the palaeontological implications
Figure 11. A, the relationship between the relative valve thickness (Tv) and the relative thickness of the lens (E1): r, correlation coefficient; P, significance level of the correlation. B, plots of the standardized curvature of the outer lens surface (R1) against the relative height of the ridge (Hr).
Figure 5 in Functional morphology and light-gathering ability of podocopid ostracod eyes and the palaeontological implications
Figure 5. Nine selected three-dimensional contour diagrams showing the theoretical morphospace consisting of parameters R1, R2, and G. The z-axis represents the G value corresponding to each combination of R1 and R2, for E1 = 2.0, 1.0, and 0.05, and E2 = 0.3, 0.6, and 0.9. Selected theoretical models are given for the corresponding regions in the morphospace.
GNSS training - dataset - Rybnik- 1st GATHERS Summer School
<p>The files in the collection contain a observation data, results and visualization algorithms. Data were collected during 1st GATHERS Summer School in Rybnik (<a href="https://www.kopalniaignacy.pl/en/">Ignacy Historic Mine</a>). The observations were taken at the Ignacy historical mine: <a href="https://goo.gl/maps/DEGFyDW5c3BfXDDR8">Location in Google Maps</a></p> <p>Following files are stored:</p> <p>GATHERS-T.5.1.1-Summer-School-GNSS-Observations-Results-PU.zip file contains following folders:</p> <p>- GNSS_data_Rybnik_2022-09-22 - RINEX files (GNSS receivers and smartphone) from field measurements (shaking table, bicycle wheel, base station) as well as GNSS products, for the day of measurements, required for GNSS data processing and used during the 1st Summer School GNSS training session,</p> <p>- Processing_Results_PPP_RTKLib - RTKLib PPP estimated 3D kinematic time-series of coordinates in IGb14 reference frame, shaking table results are only provided (GNSS receiver),<br> <br> - Processing_Results_variometric_VADASE - VADASE variometricaly estimated 3D kinematic time-series of coordinates in IGb14 reference frame, shaking table results are only provided (GNSS receiver),</p> <p>- Processing_Resuklts_Baseline_RTKLib - GNSS kinematic, baseline estimated 3D kinematic time-series of coordinates in IGb14 reference frame, shaking table results are only provided (GNSS receiver),</p> <p>- MINISEED_shaking_table_Rybnik_2022-09-22 - accelerometer data from shaking table experiment in MINISEED format, Warning due to accelerometer short power outages accelerometer time series are not time-aligned with GNSS results</p> <p>GNSSseismoRybnik_2022-09-22.ipynb - Google Colaboratory file containing the Python code used during the 1st Summer School GNSS training. The code loads GNSS processing results as well as accelerometer files into Python ObsPy seismological environment for data visualization and analysis. </p> <p>READ_ME_FIRST.txt - explanation of Google Colaboratory paths handling<br> <br> 20220922_143919.mp4, 20220922_145315.mp4, 20220922_145616.mp4 - the video files demonstrating of shaking table experiment </p> <p>If some questions arise, please send a message to:</p> <p><a href="mailto:jan.kaplon@upwr.edu.pl">jan.kaplon@upwr.edu.pl</a>, </p>
FAIR-IMPACT Synchronisation Force: preliminary data gathered from projects and intiatives
<p>Between the 21st and 24th of November 2022 more than 120 representatives of projects and initiatives funded and/or supported by the European Commission H2020 and Horizon Europe programmes and operating in the EOSC framework joined the FAIR-IMPACT Synchronisation Force collaborative sessions to assess the implementation of the FAIR principles across research projects and initiatives in the EOSC framework.</p> <p>The team focussed on work related to FAIR-IMPACT focus areas, namely Metrics and assessing FAIRness (session 1), Persistent Identifiers (Session 2), Trustworthy and FAIR-enabling repositories (Session 3) and Metadata, semantics and interoperability (Session 4).</p> <p>In order to ensure an interactive meeting, participants were asked to complete a preliminary survey according to the sessions that they would join and complete 4 questions. The inputs received constituted the starting point for the discussions during the Synchronisation Force Workshop and are collected here as pdf files.</p> <p> </p>
Soft governance and soft law literature, gathered from Scopus and Web of Science
<p>This is a list of literature related to soft governance and soft law.</p> <p>The soft governance literature was collected on the 12th of July, 2022. 110 articles from Web of Science and 169 articles from Scopus that had any variance of “soft govern*” in either their title, abstract or keywords were consolidated into one collection of 179 articles. These were randomly labelled with a SoftGov_000 identifier.</p> <p>The soft law literature was collected on the 12th of December, 2022. 1026 articles from Web of Science and 1803 articles from Scopus that had any variance of “soft law” in either their title, abstract or keywords were consolidated into one collection of 1960 articles. These were randomly labelled with a SoftLaw_0000 identifier.</p> <p>For the Web of Science searches in both phases, the specific WoS indices searched are “Science Citation Index Expanded” (SCI-EXPANDED)--1955-present, “Social Sciences Citation Index” (SSCI)--1956-present, “Arts & Humanities Citation Index” (AHCI)--1975-present, “Conference Proceedings Citation Index” – Science (CPCI-S)--1990-present, “Conference Proceedings Citation Index” – Social Science & Humanities (CPCI-SSH)--1990-present and “Emerging Sources Citation Index” (ESCI)--2017-present</p>
Replication code and data for "Comparing measured dietary variation within and between tropical hunter-gatherer groups to the Paleo Diet".
<p>Replication code and data for the publication "Comparing measured dietary variation within and between tropical hunter-gatherer groups to the Paleo Diet" appearing in the American Journal of Clinical Nutritian. Files include:</p> <p>1) R replication code (5 files that should be run sequentially in numerical order).</p> <p>2) "raw_diet_data.csv": 1 file that is ingested by the R code and used for the main analyses.</p> <p>3) "lipid_classes.csv" and "nutriants_by_source_raw.csv": 2 files that are ingested by the R code and used for the supplementary analyses.</p> <p>4) "wc2.1_30s_bio_1.tif": 1 file of GeoTiff data on annual mean temperature from the WorldClim v. 2.1 dataset (http://www.worldclim.com/version2) that is ingested by the R code.</p> <p>5) "table_1.csv": a cleaned version of the data used in analyses that is output by the R code.</p> <p>6) "table_hg_diet_data.csv" and "table_seasonal_diet_data.csv": 2 files containing seasonal and HG diet data that were not used in analyses, which are output by the R code.</p>
Synthesis of National projects on Aviation noise gathered by ANIMA National Focal Points
<p>This bundle gathers synthetic informations on more than hundred projects led at national level in EU countries and their neighbourhood (Switzeland, Ukraine, Serbia...) on aviation noise. This projects are partaking to the EU efforts to update the European Research Roadmap on such aviation noise issues.</p> <p>The bundle has been used for elaboration deliverable ANIMA D6.10 - National Focal Points projects</p>
Ancient genomes from eastern Kazakhstan reveal dynamic genetic legacy of Inner Eurasian hunter-gatherers
Open the record for dataset details and reuse information.
Hunter-gatherer child and adolescent height and tricep skinfold measures
Open the record for dataset details and reuse information.
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.