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17,039 results for “Natural”
IODP Expedition 356 Natural gamma radiation
Natural gamma radiation (NGR) data in the ~0.1 to 3.0 MeV range were measured using eight custom-designed sodium iodide (thallium) [NaI(Tl)] detectors arranged along the core measurement axis at 20 cm intervals. The NGR system uses layers of passive shielding (lead) and active shielding (plastic scintillators and coincidence electronics) to reduce the cosmic-ray signal for low-count analysis of sediment core sections and to obtain the maximum signal-to-noise ratio. Data are reported on a total counts per second basis and the raw spectral files are available as compressed files for later analysis.
IODP Expedition 353 Natural gamma radiation
Natural gamma radiation (NGR) data in the ~0.1 to 3.0 MeV range were measured using eight custom-designed sodium iodide (thallium) [NaI(Tl)] detectors arranged along the core measurement axis at 20 cm intervals. The NGR system uses layers of passive shielding (lead) and active shielding (plastic scintillators and coincidence electronics) to reduce the cosmic-ray signal for low-count analysis of sediment core sections and to obtain the maximum signal-to-noise ratio. Data are reported on a total counts per second basis and the raw spectral files are available as compressed files for later analysis.
IODP Expedition 359 Natural gamma radiation
Natural gamma radiation (NGR) data in the ~0.1 to 3.0 MeV range were measured using eight custom-designed sodium iodide (thallium) [NaI(Tl)] detectors arranged along the core measurement axis at 20 cm intervals. The NGR system uses layers of passive shielding (lead) and active shielding (plastic scintillators and coincidence electronics) to reduce the cosmic-ray signal for low-count analysis of sediment core sections and to obtain the maximum signal-to-noise ratio. Data are reported on a total counts per second basis and the raw spectral files are available as compressed files for later analysis.
WOW Nature - Bosco del ponte del Quarelo
<p>Air pollution Device Measurements ["pm1","pm2p5","pm4","pm10","humidity"]</p>
WOW Nature - Bosco di Prasaccon
<p>Air pollution Device Measurements ["pm1","pm2p5","pm4","pm10","humidity"]</p>
Natural Language-Guided Programming User Study
<p>In this dataset you find the user study data that was used in the <strong><em>Natural Language-Guided Programming</em></strong> paper, which is accepted for Onward! 2021. A preprint can be found here <a href="https://arxiv.org/pdf/2108.05198.pdf">https://arxiv.org/pdf/2108.05198.pdf</a>. The dataset consists of the following files:</p> <ul> <li> <p>benchmark.json contains 201 test cases. Each test case consists of context, a natural language intent and target code. The test cases are intended to evaluate a model that can predict code giving a piece of context code and a natural language intent. The test cases were derived from Jupyter notebooks that were crawled from Github projects with permissive licenses. In the project_metadata field you find information about the original project such as its git url and license.</p> </li> <li> <p>predictions-annotated.json contains predictions of the three models used in the paper for 100 test cases in benchmark.json. Each prediction is accompanied with qualitive assesments from three annotators.</p> </li> <li> <p>train-index.jsonl is the list of github projects that were used for training the models.</p> </li> <li> <p>eval-index.jsonl is a list of github projects that we kept separate for evaluation. The benchmark.json was created from a random subset of the projects in this list.</p> </li> </ul> <p>For more details we refer to the paper.</p>
Nature-Based Solutions Tools Catalogue
<p>The Adaptive Cities Through Integrated Nature-Based Solutions (ACT on NBS), EIT Climate KIC project made an inventory and assessment of NBS tools for climate resilient cities. The objectives of the catalogue are: 1) to increase organizational accessibility to data on NBS and climate resilience tools that have been developed and used in Europe and worldwide; 2) that different end-users become aware of several tools that already exist to plan, design, and implement NBS and make use of them to address specific environmental challenges and adaptation measures in their cities, neighborhoods or even regions.</p> <p>The NBS tools catalogue contains 70 tools collected. For this research, a “tool” is understood to be either a methodology, software, catalogue, repository, e-platform, guideline or handbook. The tools were identified through interviews with EU municipalities and workshops organized by ACT on NBS, and by additional desk research.</p> <p>The desk study entailed a combination of reviewing the websites from international organizations and EU granted projects related to cities dealing with NBSs, ecosystem services (ESs), green infrastructure, urban resilience and climate change, and reviewing peer-reviewed scientific journals, reports and grey literature.</p> <p>The latter search was implemented through Google search, Google scholar and Scopus in August 2020. This implies that tools that were developed and published before that date were collected. The search strategy was implemented using combinations of search terms such as: nature-based solutions, NBS, ecosystem-based adaptation, green infrastructure, climate adaptation, climate resilience, ecosystem services, climate hazards, urban biodiversity, urban nature, water and land management “AND” urban areas, cities “AND” tools, software, methodology, catalogue, repository, platform, handbook and guideline.</p> <p>For the assessment, tools were labeled on their descriptive characteristics and potential fields of application. The use of pre-defined indicators was chosen to support the characterization of the tools and to allow for their comparison. The categories and indicators were formulated based on the current literature and refined through expert judgements. The indicators were also, in some cases, further adapted through an iterative process of tools’ analysis.</p> <p>For more information on the NBS tools analysis please read our academic publication on “Nature-Based Solutions Tools for Planning Urban Climate Adaptation: State of the Art”.</p> <p><strong><a href="https://doi.org/10.3390/su13116381">https://doi.org/10.3390/su13116381</a></strong> </p>
The Natural History Museum's collection of Dalbergia, Pterocarpus and the Phaseolinae subtribe
<p>In 2018 the Natural History Museum (NHMUK, herbarium code: BM) undertook a pilot digitisation project together with the Royal Botanic Gardens Kew (project Lead) and the Royal Botanic Garden Edinburgh to collectively digitise non-type herbarium material of the subtribe <em>Phaseolinae</em> and the genera <em>Dalbergia </em>L.f. and <em>Pterocarpus</em> Jacq. (rosewoods and padauk), all from the economically important family of legumes (<em>Leguminosae</em> or <em>Fabaceae</em>). </p> <p>These taxonomic groups were chosen for two case studies using the herbarium collections to support the aims of the UK’s Department for Environment Food & Rural Affairs (DEFRA)-allocated, Official Development Assistance (ODA) funding: study 1 - to support the development of dry beans as a sustainable and resilient crop; study 2 - to aid conservation and sustainable use of rosewoods and padauk.</p> <p>We present the images and metadata for 11,222 NHMUK specimens. This includes label transcription and georeferencing, along with summary data on geographic, taxonomic, collector and temporal coverage. We also provide timings and the methodology for our transcription and georeferencing protocols. Approximately 35% of specimens digitised were collected in ODA-listed countries, in tropical Africa, but also in south east Asia and South America.</p>
The dataset of the Global Collections survey of natural history collections
<p>From 2016 to 2018, we surveyed the world’s largest natural history museum collections to begin mapping this globally distributed scientific infrastructure. The resulting dataset includes 73 institutions across the globe. It has:</p> <ul> <li> <p>Basic institution data for the 73 contributing institutions, including estimated total collection sizes, geographic locations (to the city) and latitude/longitude, and Research Organization Registry (ROR) identifiers where available.</p> </li> <li> <p>Resourcing information, covering the numbers of research, collections and volunteer staff in each institution.</p> </li> <li> <p>Indicators of the presence and size of collections within each institution broken down into a grid of 19 collection disciplines and 16 geographic regions.</p> </li> <li> <p>Measures of the depth and breadth of individual researcher experience across the same disciplines and geographic regions.</p> </li> </ul> <p>This dataset contains the data (raw and processed) collected for the survey, and specifications for the schema used to store the data. It includes:</p> <ol> <li>A diagram of the MySQL database schema.</li> <li>A SQL dump of the MySQL database schema, excluding the data.</li> <li>A SQL dump of the MySQL database schema with all data. This may be imported into an instance of MySQL Server to create a complete reconstruction of the database.</li> <li>Raw data from each database table in CSV format.</li> <li>A set of more human-readable views of the data in CSV format. These correspond to the database tables, but foreign keys are substituted for values from the linked tables to make the data easier to read and analyse.</li> <li>A text file containing the definitions of the size categories used in the collection_unit table.</li> </ol> <p>The global collections data may also be accessed at<a href="https://rebrand.ly/global-collections"> https://rebrand.ly/global-collections</a>. This is a preliminary dashboard, constructed and published using Microsoft Power BI, that enables the exploration of the data through a set of visualisations and filters. The dashboard consists of three pages:</p> <p><strong>Institutional profile:</strong> Enables the selection of a specific institution and provides summary information on the institution and its location, staffing, total collection size, collection breakdown and researcher expertise.</p> <p><strong>Overall heatmap:</strong> Supports an interactive exploration of the global picture, including a heatmap of collection distribution across the discipline and geographic categories, and visualisations that demonstrate the relative breadth of collections across institutions and correlations between collection size and breadth. Various filters allow the focus to be refined to specific regions and collection sizes.</p> <p><strong>Browse:</strong> Provides some alternative methods of filtering and visualising the global dataset to look at patterns in the distribution and size of different types of collections across the global view.</p>
Supplemental data from: Nature or nurture: A genetic basis for the behavioral selection of depth in siscowet and lean lake charr (Salvelinus namaycush) ecomorphs
<p>These files contain the raw depth and temperature sensor data from siscowet and lean lake charr (<em>Salvelinus namaycush</em>) ecomorphs tagged with pop-up satellite archival tags (PSATs). These fish were produced from wild gametes taken from Lake Superior and reared in a common garden study for nine years and then tagged with PSATs and released in southern Lake Superior. The dataset is supplemental to:</p> <p>Goetz, F., Sitar, S., Seider, M., and Jasonowicz, A. 2022. Nature or nurture: A genetic basis for the behavioral selection of depth in siscowet and lean lake charr (<em>Salvelinus namaycush</em>) ecomorphs. Canadian Journal of Fisheries and Aquatic Sciences. (in press).</p> <p><strong>Data description for metadata.csv:</strong></p> <p>This file contains the metadata associated with each tag deployment. This includes biological data as well as key mission paramters.</p> <table> <thead> <tr> <td>Column</td> <td>Type</td> <td>Description</td> </tr> </thead> <tbody> <tr> <td>mission_id</td> <td>integer</td> <td>mission identifier</td> </tr> <tr> <td>tag_sn</td> <td>integer</td> <td>tag serial number</td> </tr> <tr> <td>ecotype</td> <td>string</td> <td>lake trout ecotype</td> </tr> <tr> <td>release_date</td> <td>string</td> <td>date of tag release</td> </tr> <tr> <td>length_mm</td> <td>float</td> <td>total length in mm</td> </tr> <tr> <td>weight_g</td> <td>float</td> <td>weight in g</td> </tr> <tr> <td>lipid</td> <td>float</td> <td>lipid level meadured by Distell fatmeter set in research mode</td> </tr> <tr> <td>release_site</td> <td>string</td> <td>release site (deep or shallow site)</td> </tr> <tr> <td>sampling_rate</td> <td>string</td> <td>sampling interval of tag (format=HH:MM:SS)</td> </tr> <tr> <td>mission_end_utc</td> <td>datetime</td> <td>programmed tag pop off date and time in UTC time (format=YYYY-MM-DD HH:MM:SS)</td> </tr> <tr> <td>notes</td> <td>string</td> <td>notes and comments</td> </tr> </tbody> </table> <p> </p> <p><strong>Data description for the raw sensor data files:</strong></p> <p>The raw sensor data is found in the files that are prefixed with "raw-sensor-data". The data for each tag is in contained in a seperate file and the files are named as follows "raw-sensor-data-{<em><strong>mission_identifier</strong></em>}-{<em><strong>tag_serial_number</strong></em>}.csv".</p> <table> <thead> <tr> <td>Column</td> <td>Type</td> <td>Description</td> </tr> </thead> <tbody> <tr> <td>mission_id</td> <td>integer</td> <td>mission identifier</td> </tr> <tr> <td>tag_sn</td> <td>integer</td> <td>tag serial number</td> </tr> <tr> <td>timestamp_utc</td> <td>datetime</td> <td>timestamp of sensor reading (format=YYYY-MM-DD HH:MM:SS)</td> </tr> <tr> <td>depth_m</td> <td>string</td> <td>depth in meters</td> </tr> <tr> <td>temperature_c</td> <td>string</td> <td>temperature in degrees celcius</td> </tr> </tbody> </table>
TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision
<p>This repository contains the silver standard dataset and code for the paper "TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision".</p> <p>The file "heuristic_uniq_terms_nd.txt" contains the list of terms used as the heuristic and the file "natural_disasters_ssd_tweetids.tsv" contains the tweet ids in the silver standard dataset. </p> <p>To hydrate the tweets, you can use tools like twarc or Social Media Mining toolkit - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362951/</p>
The LOTUS Initiative for Open Natural Products Research: frozen dataset union wikidata (with metadata)
<p>Dataset present on Wikidata used in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a></p>
The LOTUS Initiative for Open Natural Products Research: waste to recycle
<p>Dataset not uploaded to Wikidata. </p> <p>Generated in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a></p> <p>Shared for further curation.</p>
The LOTUS Initiative for Open Natural Products Research: metadata
<p>Metadata of each of the three objects (structures, organisms, references) used in the frame of the LOTUS Initiative: <a href="https://doi.org/10.7554/eLife.70780">https://doi.org/10.7554/eLife.70780</a></p>
Sustaining Cities, Naturally Webinar: Ecology Session - Ecological management – let's bring biodiversity to cities
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This film was shown as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Sustaining Cities, Naturally Webinar: Education Session - Breathe Respirar Programme - CONEXUS schools
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This pitch was made as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Sustaining Cities, Naturally Webinar: Education Session - Vigie-Nature école
<p>Poorly planned urbanisation can lead to societal challenges as social deprivation, climate change, deteriorating health and increasing pressure on urban nature. Urban ecosystem restoration can contribute to lessen these challenges, e.g. through implementing nature-based solutions (NBS). </p> <p>This pitch was made as part of the online webinar ‘Sustaining cities, Naturally: Urban ecosystem restoration in Europe, China and Latin America’, which took place as an official side-event of the European Week of Regions and Cities 2022 on 13th and 14th October 2023. The webinar was jointly organised by the projects: INTERLACE, CONEXUS, Regreen and CLEARING HOUSE. </p> <p>The webinar illustrated how Horizon 2020 projects support international cooperation in knowledge creation and knowledge exchange between local authorities and researchers to promote urban ecosystem restoration in Europe, China and Latin America and brought together cities, regions and local authorities, city network representatives, policy makers, researchers, civil society and experts on nature-based solutions and urban ecosystem restoration from Europe, China and Latin America. </p>
Supplementary material for "Increased sensitivity of marine invertebrates to metal toxicity in the past two decades linked to Climate Change and Ocean Acidification: revelations from a natural population of sea urchins in the Mediterranean Sea." by "Davide Sartori, Guido Scatena, Cristina Vrinceanu, Andrea Gaion".
<p>Satellite observations of environmental factors and effect concentration 50 for copper to sea urchin, from 2003 to 2022.</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - CellRanger outputs for scRNAseq experiments in Figures 2,5, and 6
<p>Attached are the outputs of the cell ranger pipeline for 10x 3' scRNAseq of HSPCs. For any questions regarding this dataset please contact Leila Perie (leila.perie@curie.fr)</p>
Urbanus and Cosgrove et al. Nature Communications (2023) - 12 months scRNAseq fastq files (mouse 1) for Figures 5 and 6
<p>This dataset contains .fastq files for the 12 month timepoint (mouse 1) scRNAseq dataset used in Figures 5 and 6. For any questions about this dataset please contact Leila Perie (leile.perie@curie.fr)</p>
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.