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363 results for “harmonics”
SOils DAta Harmonization database (SoDaH): an open-source synthesis of soil data from research networks
This SOils DAta Harmonization (SoDaH) database is designed to bring together soil carbon data from diverse research networks into a harmonized dataset that can be used for synthesis activities and model development. The research network sources for SoDaH span different biomes and climates, encompass multiple ecosystem types, and have collected data across a range of spatial, temporal, and depth gradients. The rich data sets assembled in SoDaH consist of observations from monitoring efforts and long-term ecological experiments. The SoDaH database also incorporates related environmental covariate data pertaining to climate, vegetation, soil chemistry, and soil physical properties. The data are harmonized and aggregated using open-source code that enables a scripted, repeatable approach for soil data synthesis.
Database of Spherical Harmonic Representations of Sound Source Directivities
<p>This is a database of complete spherical harmonic representations of the directivities of sound sources. The data are provided as impulse responses that represent the directivity of the given source in a given discrete direction. The Matlab script <code>compute_spherical_harmonics_model.m</code> demonstrates how a spherical harmonic representation can be computed from the data. We do not provide spherical harmonic coefficients directly because of the multitude of definitions of spherical harmonics and also of the Discrete Fourier transform. We rather ask you to select the combination of definitions you would like to use and compute the spherical harmonic coefficients on demand. You may want to add re-sampling or zero padding and the like to make the data compatible with your intended application.</p> <p>As of now, all spherical harmonic representations are based on previously published data. Please do not forget to site this repository as well as the original repositories when using the data. References to the original sources are provided with each dataset. All data are bandlimited to the spherical harmonic order <code>N</code> that is specified in the corresponding file name. The conversion between raw data and spherical harmonic coefficients is therefore essentially lossless.</p>
Experimental and Simulation Results of "Ultrafast Response of Harmonic Modelocked THz Laser"
<p>This repository contains the experimental and simulation results of the article "Ultrafast Response of Harmonic Modelocked THz Laser" by Wang, F., Pistore, V., Riesch, M. <em>et al.</em>, published in <em>Light Sci Appl</em> <strong>9, </strong>51 (2020). https://doi.org/10.1038/s41377-020-0288-x</p> <p> </p>
Spherical harmonic model of the Moon's magnetic field derived from gridded data in Tsunakawa et al. (2015)
<p><strong>T2015_449</strong> is a 449 degree and order spherical harmonic model of the magnetic potential of the Moon. This model was used in Wieczorek (2018) and is a spherical harmonic expansion of the global magnetic field model of Tsunakawa et al. (2015). The original gridded data are from the file "globalSVM20150511/LunarSVM_000_02_v01.dat" and the spherical harmonic coefficients use the standard Schmidt semi-normalization, excluding the Condon-Shortley phase factor of (-1)<sup>m</sup>. The coefficients are in units of Teslas.</p>
WDMAM 2.0: degree 800 spherical harmonic model of the Earth's lithospheric magnetic field
<p>WDMAM 2.0 is a degree 800 spherical harmonic model of the Earth's lithospheric magnetic field. The reference radius of the model is 6371.2 km, and the file is formatted as rows of</p> <p>degree, order, glm, hlm</p> <p>This model is exactly the same as found on the WDMAM web site (http://www.wdmam.org/model/WDMAM_mod.out.gz) with the exceptions that the file has been reformatted to make it easier to read by computer software, and the final unnecessary column has been removed.</p>
Figure-maker code for 'The imaginary part of the high-harmonic cutoff'
<p><strong>Figure-maker code for 'The imaginary part of the high-harmonic cutoff'</strong></p> <p>This collection contains the code and experimental data used to produce the figures in the paper</p> <blockquote> <p>The imaginary part of the high-harmonic cutoff. Emilio Pisanty, Marcelo F. Ciappina and Maciej Lewenstein. <a href="https://doi.org/10.1088/2515-7647/ab8f1e">J. Phys. Photonics 2, 034013 (2020)</a>, <a href="https://arxiv.org/abs/2003.00277">arXiv:2003.00277</a>.</p> </blockquote> <p>The collection consists of the following:</p> <ul> <li>The Mathematica notebooks used to produce the data as well as to generate the figures, together with pdf printouts of those notebooks.</li> <li>The Mathematica package file RB-SFA.m, taken from <a href="https://github.com/episanty/RB-SFA">the RB-SFA package</a>.</li> <li>The intermediate data where the calculation is impractical to run quickly and flexibly.</li> <li>Copies of all the figures in the paper.</li> </ul> <p>The code here is also used to generate the figures for the Supplementary Material for the paper, which is available at <a href="https://imaginary-harmonic-cutoff.github.io">imaginary-harmonic-cutoff.github.io</a> and has been archived separately as <a href="http://doi.org/10.5281/zenodo.3758483">doi:10.5281/zenodo.3758483</a>.</p> <p>This collection is presented in an as-is manner in the hope that it will be useful.</p> <p>The copyright of this collection rests with the authors (2020). It is made available under the Creative Commons Attribution-ShareAlike 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>) license; reuse and redistribution is allowed (and encouraged) under the terms of that license. However, in addition to that legal constraint, if your use of this material results in a publication, you have the academic obligation to cite the paper as quoted above.</p>
spherical harmonics analysis of the wavefronts of a young supernova remnant
<p>This repository complements the upcoming ApJ paper "From supernova to supernova remnant: comparison of thermonuclear explosion models". The plots represent the surface of the three wavefronts of a young SNR: CD = contact discontinuity (edge of the ejecta), RS = reverse shock, FS = forward shock (outer blast wave). Radial fluctuations from the explosion center are mapped in spherical projection, and expanded in spherical harmonics to obtain the power spectrum = distribution of angular scales. Four supernova models are compared: N100ddt, N5ddt, N100def, N5def. The repository contains 3 folders: </p> <ul> <li><strong>maps_spectra</strong> (64 files, 16 MB) contains all the plots for the 4 models, for 4 fields: CD, FS, RS, FS-RS, as a function of time: as movies from 1 yr to 500 yr, and as snapshots at 3 selected times 1 yr, 100 yr, 500 yr.</li> <li><strong>SH_expansion</strong> (1059 files, 310 MB) contains a pedagogical example of the full expansion in spherical harmonics, for one map: the CD of N100ddt at 1 yr. It includes the basis functions Y𝓁𝓂 up to 𝓁=16 for each 𝓂, the individual components of the expansion up to 𝓁max=383, and the progressively reconstructed signal at each 𝓁.</li> <li><strong>SH_residuals</strong> (192 files, 90 MB) contains all the residuals plots to assess the quality of the reconstruction, for the 4 models, for the 4 fields, as a function of time.</li> </ul>
Data set accompanying: Topology of the Warm plasma dispersion relation at the second Harmonic Electron Cyclotron Resonance Layer
<p>The Warm Plasma Dispersion Relation, for waves in the electron cyclotron resonance range of frequencies, can be cast into the form of a bi-quadratic equation for $N_\perp$, where the coefficients are a function of $N_\perp^2$ and an iterative procedure is required to obtain a solution. However, this iterative procedure is not well understood and fails to converge towards a solution at the second harmonic resonance layer. In particular at higher densities where the wave can couple to an electron Bernstein wave.<br> This paper focuses on a solution to the poor convergence of the iterative method, enabling determination of the topology of the dispersion relation around the second harmonic using a fully relativistic code for oblique waves.<br> A feed-forward controller is proposed with the ability to adjust the rotation of a step of $N_\perp^2$ within the complex plane, while also limiting the step-size.<br> It is shown that implementation of the controller stabilizes unstable solutions, while improving overall robustness of the iteration. This allows the evaluation of the coupling between the fast extraordinary mode and electron Bernstein waves at the second harmonic electron cyclotron resonance layer, for non-perpendicularly propagating waves.</p>
Data from: Integration and harmonization of trait data from plant individuals across heterogeneous sources
<p>Trait data represent the basis for ecological and evolutionary research and have relevance for biodiversity conservation, ecosystem management and earth system modelling. The collection and mobilization of trait data has strongly increased over the last decade, but many trait databases still provide only species-level, aggregated trait values (e.g. ranges, means) and lack the direct observations on which those data are based. Thus, the vast majority of trait data measured directly from individuals remains hidden and highly heterogeneous, impeding their discoverability, semantic interoperability, digital accessibility and (re-)use. Here, we integrate quantitative measurements of verbatim trait information from plant individuals (e.g. lengths, widths, counts and angles of stems, leaves, fruits and inflorescence parts) from multiple sources such as field observations and herbarium collections. We develop a workflow to harmonize heterogeneous trait measurements (e.g. trait names and their values and units) as well as additional information related to taxonomy, measurement or fact and occurrence. This data integration and harmonization builds on vocabularies and terminology from existing metadata standards and ontologies such as the Ecological Trait-data Standard (ETS), the Darwin Core (DwC), the Thesaurus Of Plant characteristics (TOP) and the Plant Trait Ontology (TO). A metadata form filled out by data providers enables the automated integration of trait information from heterogeneous datasets. We illustrate our tools with data from palms (family Arecaceae), a globally distributed (pantropical), diverse plant family that is considered a good model system for understanding the ecology and evolution of tropical rainforests. We mobilize nearly 140,000 individual palm trait measurements in an interoperable format, identify semantic gaps in existing plant trait terminology and provide suggestions for the future development of a thesaurus of plant characteristics. Our work thereby promotes the semantic integration of plant trait data in a machine-readable way and shows how large amounts of small trait data sets and their metadata can be integrated into standardized data products.</p>
High Average Power Second-harmonic Generation of a CW Erbium Fiber MOPA
<p>Open access data set for the manuscript "High Average Power Second-harmonic Generation of a CW Erbium Fiber MOPA" to be published in Photonics Technology Letters.</p>
Harmonized LUCAS dataset (ST_LUCAS)
<p>ST_LUCAS is a harmonized dataset derived from the LUCAS (Land Use and Coverage Area frame Survey) dataset. LUCAS is an Eurostat activity that has performed repeated in situ surveys over Europe every three years since 2006. Original LUCAS data (<a href="https://ec.europa.eu/eurostat/web/lucas/data">https://ec.europa.eu/eurostat/web/lucas/data</a>) starting with the 2006 survey were harmonized into common nomenclature based on the 2018 survey. ST_LUCAS dataset is provided in two versions:</p> <ul> <li> <p><em>lucas_points</em>: each LUCAS survey is represented by single record</p> </li> <li> <p><em>lucas_st_points</em>: each LUCAS point is represented by a single location calculated from multiple surveys and by a set of harmonized attributes for each survey year</p> </li> </ul> <p>Harmonization and space-aggregation of LUCAS data were performed by ST_LUCAS system available from <a href="https://geoforall.fsv.cvut.cz/st_lucas">https://geoforall.fsv.cvut.cz/st_lucas</a>. The methodology is described in <em>Landa, M.; Brodský, L.; Halounová, L.; Bouček, T.; Pešek, O. Open Geospatial System for LUCAS In Situ Data Harmonization and Distribution. ISPRS Int. J. Geo-Inf. 2022, 11, 361. <a href="https://doi.org/10.3390/ijgi11070361">https://doi.org/10.3390/ijgi11070361</a>.</em></p> <p>List of harmonized LUCAS attributes: <a href="https://geoforall.fsv.cvut.cz/st_lucas/tables/list_of_attributes.html">https://geoforall.fsv.cvut.cz/st_lucas/tables/list_of_attributes.html</a></p> <p>ST_LUCAS dataset is provided under the same conditions (“free of charge”) as the original LUCAS data (<a href="https://ec.europa.eu/eurostat/web/lucas/data">https://ec.europa.eu/eurostat/web/lucas/data</a>).</p>
Comprehensive Collection of EU-27 crops statistics: a harmonized regional dataset of area and production.
<p><strong>Description</strong>: This dataset provides a detailed overview of data related to agricultural production and cultivated area in the European Union with 27 member states (EU27) at the regional level. The objective of this work is to provide a homogeneous dataset to analyze crop trends across European regions.</p> <p>The data covers a wide range of crops, including cereals, vegetables, fruits, and other categories relevant to European agriculture. The information is disaggregated by NUTS regions (Nomenclature of Territorial Units for Statistics).</p> <p>The data used in this dataset comes from Eurostat and national statistics from the member states, and has been harmonized to ensure consistency and reliability. The accompanying documentation provides more details on the harmonization methodology and a complete list of the national statistics websites visited.</p> <p><strong>Keywords</strong>: Agriculture, EU27, Agricultural production, Cultivated area, Regional data, Dataset, Crops, NUTS, Eurostat, National statistics.</p> <p><strong>License</strong>: [CC BY]</p>
Supporting Data for: A Multimer Embedding Approach for Molecular Crystals up to Harmonic Vibrational Properties
<p>Accurate calculations of molecular crystals are crucial for drug design and crystal engineering. However, periodic high-level density functional calculations using hybrid functionals are often prohibitively expensive for relevant systems. These expensive periodic calculations can be circumvented by the usage of embedding methods in which for instance the periodic calculation is only performed at a lower-cost level and then monomer energies and dimer interactions are replaced by those of the higher-level method. Herein, we extend upon such a multimer embedding approach to enable energy corrections for trimer interactions and the calculation of harmonic vibrational properties up to the dimer level. We evaluate this approach for the X23 benchmark set of molecular crystals by approximating a periodic hybrid density functional (PBE0+MBD) by embedding multimers into less expensive calculations using a generalized-gradient approximation (GGA) functional (PBE+MBD). We show that trimer interactions are crucial for accurately approximating lattice energies within 1 kJ/mol and might also be needed for further improvement of lattice constants and hence cell volumes. Finally, vibrational properties are already very well captured at the monomer and dimer level, making it possible to approximate vibrational free energies at room temperature within 1 kJ/mol.</p><p>This supporting dataset includes results of PBE0+MBD, PBE+MBD, and multimer embedding calculations for the X23 set of molecular crystals. See the included README.md file for more details. The related preprint can be found at <a href="https://doi.org/10.48550/arXiv.2209.02687">https://doi.org/10.48550/arXiv.2209.02687</a>.</p>
Korsnes museum, Old harmonic
https://www.museumnord.no/en/our-venues/korsnes-museum/ My 3D reconstruction generated with photogrammetry software 3DF Zephyr v6.507 processing 142 images Source: Objaverse 1.0 / Sketchfab
Harmonized data and R code for "Coherent response of zoo- and phytoplankton assemblages to global warming since the Last Glacial Maximum"
<p>Harmonized data and R code for "<em>Coherent response of zoo- and phytoplankton assemblages to global warming since the Last Glacial Maximum</em>"<br>by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Karl-Heinz Baumann, Helmut Hillebrand and Michal Kucera (submitted to <em>Global Ecology and Biogeography</em>, 2024).</p> <p><strong>STRUCTURED ABSTRACT</strong><br><em>Aim</em>: We use the fossil record of different marine plankton groups to determine how their biodiversity changed during past climate warming comparable to projected future warming.<br><em>Location</em>: North Atlantic Ocean and adjacent seas. Time series cover a latitudinal range of 75°N to 6°S.<br>Time period: Past 24,000 years, i.e., from the Last Glacial Maximum (LGM) to the current warm period covering the last deglaciation.<br><em>Major taxa studied</em>: Planktonic foraminifera, dinoflagellates and coccolithophores.<br><em>Methods</em>: We analyse time series of fossil plankton communities using principal component analysis and generalised additive models to estimate the overall trend of temporal compositional change in each plankton group and identify periods of significant change. We further analyse local biodiversity change by analysing species richness, species gains and losses, and the effective number of species in each sample and compare alpha diversity to the LGM mean.<br><em>Results</em>: All plankton groups show remarkably similar trends in the rates and spatio-temporal dynamics of local biodiversity change and a pronounced non-linearity with climate change in the current warm period. Assemblages of planktonic foraminifera and dinoflagellates started to significantly change with the onset of global warming around 15,500 to 17,000 years ago and continued to change at the same pace during the current warm period until at least 5,000 years ago, while coccolithophores assemblages changed at a constant rate throughout the past 24,000 years seemingly irrespective of the prevailing temperature change.<br><em>Main conclusions</em>: The climate change during the transition from the LGM to the current warm period led to a long-lasting reshuffling of the zoo- and phytoplankton assemblages likely associated with the emergence of new ecological interactions and possibly a shift in the dominant drivers of plankton assemblage change from more abiotic-dominated causes during the last deglaciation to more biotic-dominated causes with the onset of the Holocene.</p> <p><strong>CONTENT</strong><br>This dataset includes the harmonized assemblage data of the three investigated plankton groups (planktonic foraminifera, dinoflagellates and coccolithophores) as well as all the R code needed to re-produce the results of this study and it's main figures.</p> <p>Scripts written by Tonke Strack</p> <p><br><strong>DATA SOURCES</strong><br>1) GMST: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. <br> <em>Nature</em> 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021).<br>2) WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, <em>Technical Editor. </em><br><em> NOAA Atlas NESDIS</em> 81, 52 (2019).<br>3) plankton assemblage data: individual data references provided in CoreList.csv</p> <p><br><strong>DATA</strong><br>1. Harmonized assemblage data<strong>*</strong>: <em>FullDataTable_PF_harmonized.txt</em><br>2. Core list of additional information on time series: <em>CoreList.csv</em><br>3. Reference lists for species names names: <em>ReferenceList_PlanktonicForaminifera.csv, ReferenceList_Dino.csv, ReferenceList_Cocco.csv</em></p> <p><br><strong>CODE</strong><br>1. <em>01_LoadData.R</em>: loads harmonized assemblage data from planktonic foraminifera, dinocyst and coccolithophores<br>2. <em>02_GMST_import.R</em>: loads loads the globally resolved surface temperature since the LGM from Osman et al. (2011)<br>3. <em>03_DataAnalysis_PCA_GAM.R</em>: PCA/GAM analysis on the plankton assemblage data (results shown in Figure 2 and 3), sensititvity analysis (results shown in Figure 4), and some summary statistics<br>4. <em>04_DataAnalysis_MH_GAM_AlternativeApproach.R</em>: alternative GAM approach using Morisita-Horn index (results shown in Figure S2, S3 and S4)<br>5. <em>05_DataAnalysis_BiodiversityChange.R</em>: local biodiversity change analysis of individual time series (results shown in Figure 5, 6 and S9)</p> <p><br>*Assemblage data of individual time series were manually downloaded, quality checked, taxonomically harmonized, and combined into one data file.<br>Planktonic foraminifera data were harmonized following Siccha and Kuchera (2017). We merged <em>Globigerinoides ruber ruber</em> and <em>Globigerinoides ruber </em><br><em>albus</em>, because some studies only reported them together as<em> Globigerinoides ruber</em>. Also, P/D intergrades (an informal category of morphological<br>intermediates between <em>Neogloboquadrina incompta</em> and <em>Neogloboquadrina dutertrei</em>) were merged with <em>Neogloboquadrina incompta</em>.<br>Dinocyst taxonomy was harmonized following de Vernal et al. (2020) with slight additions following Zonneveld et al. (2013). Names that could not be<br>resolved using synonym lists and assigned a harmonized name following de Vernal et al. (2020) and Zonneveld et al. (2013) were treated as unidentified<br>specimens and were excluded from the assemblage analyses. These specimens were present in 4 time series and were rare taxa (relative abundances < 3%).<br>The protoperidinoids were also excluded from further assemblage analyses as this category includes all unidentified brownish cysts (de Vernal et al., 2020).<br>Coccolithophore taxonomy follows Young et al. (2003) and coccolith countings were conducted on a scanning-electron microscope (SEM) to ensure that all<br>specimens are resolved to the species level. We merged <em>Coccolithus pelagicus</em> subspecies, because they were not distinguished in all studies. <br>Species not reported in the time series data were assumed to be absent (that is, zero abundance) which is in accordance with the completeness of the counts<br>reported in the original studies. The original data were either given in absolute or relative abundances, and after excluding unnecessary columns<br>(unidentified or rare taxa that could not be harmonised) the abundances were recalculated to 100 %. In total, 41 species of planktonic foraminifera,<br>30 species of coccolithophores and 53 species of organic-walled dinocysts were observed in our study.</p> <p><strong>REFERENCES</strong><br>de Vernal, A., Radi, T., Zaragosi, S., Van Nieuwenhove, N., Rochon, A., Allan, E., . . . Richerol, T. (2020). Distribution of common modern dinoflagellate cyst taxa in surface sediments of the Northern Hemisphere in relation to environmental parameters: The new n=1968 database. <em>Mar. Micropaleontol.</em>, 159. doi:10.1016/j.marmicro.2019.101796<br>Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. S<em>ci. Data</em> 4, 170109, doi:10.1038/sdata.2017.109 (2017).<br>Young, J. R., Geisen, M., Cros, L., Kleijne, A., Sprengel, C., Probert, I., & Østergaard, J. B. (2003). A guide to extant coccolithophore taxonomy. <em>Journal of Nannoplankton Research Special Issue</em>, 1, 1-125. doi:10.58998/jnr2297<br>Zonneveld, K. A. F., Marret, F., Versteegh, G. J. M., Bogus, K., Bonnet, S., Bouimetarhan, I., . . . Young, M. (2013). Atlas of modern dinoflagellate cyst distribution based on 2405 data points. <em>Rev. Palaeobot. Palynol.</em>, 191, 1-197. doi:10.1016/j.revpalbo.2012.08.003</p>
Data referring to the Article "Harmonized Skies: A Survey on Drone Acceptance across Europe" published in Drones Journal
<p><span>The material provided is part of the article "Harmonized Skies: A Survey on Drone Acceptance across Europe," published in the Drones Journal (https://doi.org/10.3390/drones8030107). This article describes a study investigating civil drone acceptance in six different EU countries. This study was part of the USpace4UAM project (Grant Agreement No 101017643). The material includes the data set for the study described and the Python codes for the random forest analysis carried out to investigate the influence of demographic and personnel factors on drone acceptance.</span></p>
A synthetic moving-envelope metasurface antenna for arbitrary harmonic orders independent control
<p>Raw measured data for the paper "A synthetic moving-envelope metasurface antenna for arbitrary harmonic orders independent control"</p>
Harmonized directory of beneficiaries of the PDAC project - Congo-Brazzaville
<p><strong>Description:</strong> This datasets contains a list of all groups of agricultural, livestock, fisheries and agrifoodstuff initiatives that benefitted from the "Projet d'appui au Dévéloppement de l'Agriculture Commerciale" (PDAC) project from the Ministère de l'Agriculture de l'Elevage et de la Pêche (MAEP) of the Republic of Congo, along with their locations at departmental and district levels, and initiative names. For more details see the project details at <a href="https://pdacmaep.cg">https://pdacmaep.cg</a></p> <p><strong>Scope of data :</strong> This dataset lists 850+ beneficiary initiatives country-wide, covering 33 activities along the 12 departments and 99 districts. List of activities is as follows :</p> <ul> <li>aliment pour bétail</li> <li>ananas</li> <li>apiculture</li> <li>arachide</li> <li>arboriculture</li> <li>banane</li> <li>cacao</li> <li>chambre froide</li> <li>commercialisation</li> <li>élevage bovin</li> <li>élevage de cailles</li> <li>élevage ovins</li> <li>élevage pondeuse</li> <li>élevage porcin</li> <li>fumure</li> <li>gingembre</li> <li>grenadille</li> <li>haricot</li> <li>huile de palme</li> <li>igname</li> <li>maïs</li> <li>manioc</li> <li>maraîchage</li> <li>pêche</li> <li>pisciculture</li> <li>pois d'angol</li> <li>pomme de terre</li> <li>prestation</li> <li>production</li> <li>service vétérinaire</li> <li>soja</li> <li>taro</li> <li>transformation</li> </ul> <p><strong>Data Collection and Processing</strong>: This dataset was assembled from the different departmental directories of beneficiaries openly available online on April 18th, 2024 from the project website at <a href="https://pdacmaep.cg/index.php?page=documents">https://pdacmaep.cg/index.php?page=documents</a> and contains harmonized activity type, initiative name, department and district. Contact names and phone coordinates were removed for privacy.</p> <p><strong>Project Context</strong>: This work is part of the <a href="https://www.cirad.fr/dans-le-monde/cirad-dans-le-monde/projets/projet-pudt-congo">PUDT Congo</a> project.</p>
Harmonized INSEE socio-demographic IRIS-level data and IRIS conversion file (2010-2020)
<p>The smallest level of aggregation for sociodemographic data made publicly available by the French INSEE is the IRIS. Data is downloadable on INSEE websites but decomposed by type of variable and by year. I thus combined all datasets into a single homogenous dataset for practicality. It includes on the period 2010-2020 :</p> <ul> <li>Population structure (age, gender...) data</li> <li>Economic occupation CSP data</li> <li>Available income data</li> <li>Family data</li> </ul> <p>Since a sizeable amount of IRIS change each year, timewise comparisons are limited. I therefore created a conversion file. Each IRIS is expressed as a % combination of previous IRIS. This allows to track IRIS merge, IRIS split and border changes. Measurement errors linked to border overlap were detected when the overlap of 2 IRIS was less than 1 percent. The IRIS overlap without the measurement error is the variable _ajuste (pardon my French). This allows to express the IRIS of a year as the wieghted sum of the IRIS of any other given year.</p> <p>You will also find the codes I used to create each of the files included in this project on my github. The annotation may be lacking, it is currently being improved. </p>
Harmonized terminology for scientific research
<p>The Data Collection Framework (DCF) application is constituted of an interactive web-based application that aims at facilitating data exchange, data extraction, and data reusability. A harmonized terminology is used to collect and analyse data in a coherent way with the aim to support scientific research.</p> <p>DCF_catalogues file contains all the valid catalogues published in the DCF. Catalogue format is compatible with Catalogue Browser (java application available here: https://github.com/openefsa/catalogue-browser/wiki)</p> <p>Catalogue_list file contains the list of all catalogues, each with its scopenote, explaining the content of the catalogue itself.</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.