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1,298 results for “Archive”
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from CARAIB maize simulations
<p>This data set contains output data from simulations with the model CARAIB for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from CARAIB soybean simulations
<p>This data set contains output data from simulations with the model CARAIB for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from CARAIB spring wheat simulations
<p>This data set contains output data from simulations with the model CARAIB for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE winter wheat simulations
This data set contains output data from simulations with the model APSIM-UGOE for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE spring wheat simulations
This data set contains output data from simulations with the model APSIM-UGOE for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from JULES soybean simulations
This data set contains output data from simulations with the model JULES for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the WFDEI (Weedon et al. 2014) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from JULES rice simulations
This data set contains output data from simulations with the model JULES for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the WFDEI (Weedon et al. 2014) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from APSIM-UGOE rice simulations
This data set contains output data from simulations with the model APSIM-UGOE for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from JULES spring wheat simulations
<p>This data set contains output data from simulations with the model JULES for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the WFDEI (Weedon et al. 2014) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').</p>
Reti Medievali Open Archive: Metadata in RDF-XML
<p>This RDF-XML dump contains metadata about the items deposited in <em>RM Open Archive</em> converted in Linked Open Data.</p> <p>RM <em>Open Archive</em> is an Open Access scholarly repository, which covers the whole range of medieval studies: social, economic, political and institutional history, as well as cultural, religious and gender representations and practices.</p> <p>RM <em>Open Archive</em> was realised in the frame of the PRIN 2010-2011 project <a href="http://www.medievistica.unina.it/"><em>Concepts, Practices and Institutions of a Discipline: Italian Medieval Studies in 19th and 20th Centuries</em></a>, coordinated by Prof. Roberto Delle Donne at "Federico II" University of Naples. <br> It is under the aegis of the following learned societies, which invite their members to deposit publications. </p> <p><em><a href="http://www.sismed.eu/it/">Società italiana degli storici medievisti</a></em> (Italian Society of Medievalists)</p> <p><em>Consulta per il Medioevo e l'Umanesimo latini</em> (Council for Latin Middle Ages and Humanism)</p> <p><em><a href="http://www.sifr.it/">Società Italiana di Filologia Romanza</a></em> (Italian Society of Romance Philology)</p> <p><em><a href="http://www.paleografi-diplomatisti.org/">Associazione Italiana dei Paleografi e Diplomatisti</a></em> (Italian Association of Paleography and Diplomatics)</p> <p><em><a href="http://www.mediaevistenverband.de/">Mediävistenverband e.V.</a></em> (German Association of Medievalists)</p>
Empairex 1: Optical properties data archive
<p>This spreadsheet contains all the numerical data used to make the figures in the manuscript "Chemical composition, optical properties and radiative forcing efficiency of nascent particulate matter emitted by an aircraft turbofan burning conventional and alternative fuels" and in the corresponding supplementary information. </p>
German Gas Feed-in/-out Data Archive
<p>Provided data includes the following data files:</p> <p>- 2015 German gas feed-out [TWh/a Hs] per NUTS3 region.</p> <p>- 2015 German gas feed-in timeseries [GWh/h Hs] per sector.</p> <p>- 2015 German gas feed-out timeseries [GWh/h Hs] per sector.</p> <p>- 2050 German gas feed-in timeseries [GWh/h Hs] per sector for the Energiewende scenario.</p> <p>- 2050 German gas feed-out timeseries [GWh/h Hs] per sector for the Energiewende scenario.</p> <p>- 2050 German gas feed-in timeseries [GWh/h Hs] per sector for the Reference scenario.</p> <p>- 2050 German gas feed-out timeseries [GWh/h Hs] per sector for the Reference scenario.</p>
Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA Data Archive
<p>[This repository contains the source data for the workflow presented in the manuscript "<strong>Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA</strong>". The workflow can be found here: http://gitlabscottgroup.med.usherbrooke.ca/gaspard/snakemake_blockbuster ]</p> <p>The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 115 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new tRNA and snoRNA genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distort the perceived architecture of the human transcriptome.</p>
Database of abstracts in publications on exoplanets from the NASA archive
<p>Collection of pre-processed data of the abstracts in publications on exoplanets, as collected by NASA. The dataframes are stored as JSON prepared for importing as Pandas dataframes.</p>
Tropical sand cays as natural paleo-cyclone archives
<p>Sand cays are valuable paleo-archives that can significantly increase our understanding of Holocene tropical cyclone variability. Here we conducted detailed sedimentological and chronological analyses from a 195-cm-depth pit excavated on Guangjin Island (northern South China Sea), a cay influenced by frequent tropical cyclones. Radiometric dating of multiple deposits revealed that foraminifera, soft coral spicules, and gastropod shells yielded variable age distributions, while U/Th ages of pristine <em>Acropora</em>branches provided a clear record of deposition and cay formation. Based on this robust chronostratigraphy, the proportions of > 2mm grain-size fraction within the deposits corresponded with the frequency of paleo-typhoons recorded by historical records in recent centuries. U/Th ages of <em>Acropora </em>branches from the deposits matched with three known historical typhoon events. Our results highlight the potential of cyclone-deposited sand cays as new archives for recording paleo-cyclones.</p>
Data archive for Towards understanding potential atmospheric contributions to abrupt climate changes: characterizing changes to the North Atlantic eddy-driven jet over the last deglaciation
<p>This archive contains simulated North Atlantic eddy-driven jet latitude and tilt data generated using the PlaSim model to accompany Andres and Tarasov (Climate of the Past, accepted), DOI: https://doi.org/10.5194/cp-15-1-2019.</p> <p>Paper abstract is as follows:</p> <p>"Abrupt climate shifts of large amplitudes were common features of the Earth’s climate as it transitioned into and out of the last full glacial state approximately 20 000<br> years ago, but their causes are not yet established. Midlatitude atmospheric dynamics may have played an important role in these climate variations through their effects on heat and precipitation distributions, sea ice extent, and wind-driven ocean circulation patterns. This study characterizes deglacial winter wind changes over the North Atlantic (NAtl) in a suite of transient deglacial simulations using the PlaSim Earth system model (run at T42 resolution) and the TraCE-<br> 21ka (T31) simulation. Though driven with yearly updates in surface elevation, we detect multiple instances of NAtl jet transitions in the PlaSim simulations that occur within 10<br> simulation years and a sensitivity of the jet to background climate conditions. Thus, we suggest that changes to the NAtl jet may play an important role in abrupt glacial climate changes.</p> <p>We identify two types of simulated wind changes over the last deglaciation. Firstly, the latitude of the NAtl eddy-driven jet shifts northward over the deglaciation in a sequence of distinct steps. Secondly, the variability in the NAtl jet gradually shifts from a Last Glacial Maximum (LGM) state with a strongly preferred jet latitude and a restricted latitudinal range to one with no single preferred latitude and a range that is at least 11 ◦ broader. These changes can significantly affect ocean circulation. Changes to the position of the NAtl jet alter the location of the wind forcing driving oceanic surface gyres and the limits of sea ice extent, whereas a shift to a more variable jet reduces the effectiveness of the wind forcing at driving surface ocean transports.</p> <p>The processes controlling these two types of changes differ on the upstream and downstream ends of the NAtl eddy-driven jet. On the upstream side over eastern North America, the elevated ice sheet margin acts as a barrier to the winds in both the PlaSim simulations and the TraCE-21ka experiment. This constrains both the position and the latitudinal variability in the jet at LGM, so the jet shifts in sync with ice sheet margin changes. In contrast, the downstream side over the eastern NAtl is more sensitive to the thermal state of the background climate. Our results suggest that the presence of an elevated ice sheet margin in the south-eastern sector of the North American ice complex strongly constrains the deglacial position of the jet over eastern North America and the western North Atlantic as well as its variability."</p> <p> </p>
Mining the UK Web Archive for Semantic Change Detection (Dataset)
<p>The dataset that was used and released with the RANLP 2019 paper, titled "Mining the UK Web Archive for Semantic Change Detection" (see <a href="https://github.com/adtsakal/Semantic_Change">https://github.com/adtsakal/Semantic_Change</a>). It contains annual word2vec representations of more than 47K words over the period 2000-2013, along with a list of 65 words with known semantic change over the same time period. </p>
Text-fig. 1. Geographical position of the locality in Komořany Lake - A Lost Archive For Palaeobotanical, Archaeological And Historical Information
Text-fig. 1. Geographical position of the locality
CephBase: cephbase.tar.gz full archive
CephBase is a repository of data and factual information for the CLASS CEPHALOPODA - squids, octopuses, cuttlefish and nautilus. These remarkable and unique animals are best known to the public for their ability to instantly change color pattern, and for their jet propulsed escape and inking. Cephalopods are keystone species in marine ecosystems, they are important biomedical models for research, and they are the target of substantial fisheries worldwide. data migration documentation: <p></p>https://github.com/EOL/ContentImport/issues/1<p></p>
The European Vegetation Archive (EVA)
The European Vegetation Archive (EVA) is an initiative of European Vegetation Survey aimed at establishing and maintenance of a single data repository of vegetation-plot observations (i.e. records of plant taxon co-occurrence at particular sites, also called phytosociological relevés) from Europe and adjacent areas and to facilitate the use of these data for non-commercial purposes, mainly academic research and applications in nature conservation and ecological restoration. The initiative follows the EVA Data Property and Governance Rules. It closely cooperates with the Global Index of Vegetation-Plot Databases (GIVD), the Global Vegetation Database (sPlot) and the Plant Trait Database (TRY). <p></p>http://euroveg.org/eva-database<p></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.