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6,025 results for “Science of science”
Real operating data of a photovoltaic system installed at Area Science Park - Trieste - Italy
<p>Data collected from a monocrystalline silicon photovoltaic (PV) plant installed on building Q2 at Area Science Park in the Basovizza campus located in Trieste, Italy. The data represent almost 9 years of real operating conditions of the PV plant. Every 15 minutes the DC side electrical PV system working parameters were recorded, in addition also ambient temperature, irradiance in the plane of the modules and panel temperatures were recorded. Data are periodically downloaded using a control software.</p>
CHIST-ERA The European Open Science Policy Landscape - Status and Trends
<p>CHIST-ERA carried out an analysis among its member agencies regarding their national Open Science policies and practices. An Analysis summarises the Status Quo in 2021 and compares the evolution of the policies between 2019 and 2021.</p> <p>Data are coming from Open Science Policies Survey - in 2019 and 2021 - among funding agencies that are members of ChistEra.</p>
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.
Data for: Image-based evaluation of beers at an online Pint of Science festival using Projective Mapping, Check-All-That-Apply and Acceptability
<p>Data obtained from n=67 untrained attendants at an outreach Pint of Science festival, online because of the COVID-19 pandemic but usually held at bars. The participants used images of brand logos to evaluate eight beers among the most commonly consumed in Spain. Three sensory analysis techniques were used: Projective Mapping, Acceptability and Check-All-That-Apply (CATA).</p>
PICASO 3.0 Atmospheric Models of WASP-39 b for the JWST Transiting Exoplanet Community Early Release Science Program
<p><strong>OVERVIEW</strong></p> <p>The exoplanetary atmospheric models used in the recent <a href="https://www.nature.com/articles/s41586-022-05269-w">discovery of CO<sub>2 </sub>in WASP- 39 b's atmosphere</a> by the JWST transiting exoplanet community early release science program are presented here. These models are also being used to analyze multiple observations of WASP 39-b obtained using various JWST instruments and observational modes by the transiting exoplanet ERS team. The 1D Radiative-Convective-Thermochemical Equilibrium (RCTE) atmospheric models were computed using the open-source 1D climate model <a href="https://natashabatalha.github.io/picaso/">PICASO 3.0</a> (<a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>). These atmospheric models were then post-processed with condensation clouds using the open-source cloud model <a href="https://natashabatalha.github.io/virga/">VIRGA</a> (<a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>). The atmospheric models were also post-processed with the 1D photochemical network code <a href="https://github.com/exoclime/VULCAN">VULCAN</a> (<a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a>) to explore photochemistry in WASP-39 b's atmosphere.</p> <p><strong>1D RCTE CLOUD-FREE MODELS</strong></p> <p>The base 1D RCTE grid includes atmospheric metallicity points at 0.1, 0.3, 1.0, 3.0, 10.0, 30.0, 50.0, and 100.0x solar values. The Carbon-to-Oxygen (C/O) ratio value is varied between four values - 0.23, 0.46, 0.69, and 0.92. The intrinsic temperature of the planet has been varied across 100, 200, and 300 K, whereas two values of the heat redistribution factor - 0.4 and 0.5 are included. A heat redistribution factor of 0.5 corresponds to the case of full heat redistribution. With these grid points, the grid includes a total of 8x4x3x2= 192 different models.</p> <p>These models are in the "RCTE_cloud_free.zip" folder. The naming scheme of these files is "profile_eq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_.nc" where [T_int] represents the intrinsic temperature of the planet, [MH] is the log<sub>10 </sub>of the atmospheric metallicity relative to solar, [CtoO] is the C/O ratio relative to solar, and [rfacv] is the heat-redistribution factor. So, a metallicity value of 0.3xsolar will have a [MH] value of -0.5, and a C/O 0.46 is considered 1xsolar and will correspond to [CtoO]=1. [T_int] and [rfacv] can assume values described in the previous paragraph.</p> <p><strong>1D RCTE CLOUDY MODELS</strong></p> <p>The base 1D RCTE cloud-free models were post-processed to include condensation cloud species Na<sub>2</sub>S, MnS, and MgSiO<sub>3</sub>. The cloud structure and optical property calculations were performed using the VIRGA model where the sedimentation efficiency <em>f<sub>sed </sub></em>and the vertical eddy diffusion coefficient (<em>K<sub>zz</sub></em>) are free parameters. For the cloudy models, 5 <em>f<sub>sed </sub></em> values - 0.6, 1, 3, 6, and 10 were used along with 3 different values of log<sub>10</sub><em>K<sub>zz </sub></em>- 5, 7, 9, and 11, where <em>K<sub>zz </sub></em>is in cm<sup>2</sup>/s. These models are included in the "RCTE_cloudy.zip" folder following the naming structure "profile_eq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz]_fsed_[fsed].cld.nc" where two other variables are added in the name - [log10Kzz] and [fsed]. Both of these variables can take values listed here.</p> <p><strong>PHOTOCHEMICAL CLOUD-FREE MODELS</strong></p> <p>A much smaller subset of the base 1D RCTE models were post-processed with the 1D photochemical network code VULCAN to simulate the effects of vertical mixing and photochemistry in WASP-39 b's atmosphere. log<sub>10</sub><em>K<sub>zz </sub></em>was varied again between the 5, 7, 9, and 11 for this purpose. These files are named as "profile_diseq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz].nc" and can be found in the "photochem_cloud_free.zip" folder.<br> <br> <strong>PHOTOCHEMICAL CLOUDY MODELS</strong></p> <p>The photochemical models were post-processed with clouds to simulate a cloudy atmosphere with disequilibrium chemistry. The <em>f<sub>sed </sub></em> and log<sub>10</sub><em>K<sub>zz </sub></em> grid system for the RCTE cloudy models has been used again for these models as well. These files are in the "photochem_cloudy.zip" folder and are named according to the format "profile_diseq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz]_fsed_[fsed].cld.nc".</p> <p><strong>FILE FORMATTING AND USAGE</strong></p> <p>All the files are released in the <a href="https://docs.xarray.dev/en/stable/">xarray</a> format. Each model has one single xarray file containing all metadata of that model. This metadata includes the input parameters used to compute the model, for example, the metallicity, C/O ratio, and intrinsic temperature. The temperature-pressure (<em>T(P)</em>) profile and the volume mixing ratio profiles of all the different gases in each model is also included in the metadata. The computed transmission spectrum of the model planet from 0.3-6 microns is included in the same file as well. The spectrum is calculated with resampled opacities at a spectral resolution of 60,000, but they should be re-binned at a spectral resolution of 3000 or less for comparison with observed data. For cloudy models, the wavelength dependant optical depth, asymmetry parameter, and single scattering albedo for each atmospheric layer are included in these xarray files.</p> <p>We refer to this <a href="https://natashabatalha.github.io/picaso/notebooks/codehelp/data_uniformity_tutorial.html#Reading/interpreting-an-xarray-file">PICASO tutorial</a> for reading/writing these xarray files. The spectrum from these xarray files can be easily extracted using the following code.</p> <pre><code class="language-python">import xarray as xr path = "path/to/files" ds_sm = xr.open_dataset(path+"profile_eq_planet_300_grav_4.5_mh_+2.0_CO_2.0_sm_0.0486_v_0.5_.nc") # for spectrum wavelength = ds_sm['wavelength'].values transit_depth = ds_sm['transit_depth'].values # for T(P) profile temperature = ds_sm['temperature'].values pressure = ds_sm['pressure'].values</code></pre> <p><a href="https://github.com/natashabatalha/picaso/blob/master/docs/notebooks/fitdata/GridSearch.ipynb">This tutorial</a> shows how to use these models to analyze the NIRSpec Prism observations of WASP-39 b, which led to <a href="http://www.nature.com/articles/s41586-022-05269-w">CO<sub>2 </sub>detection</a>. Please note that the folders must be unzipped before using them with this notebook.</p> <p><strong>CREDITS</strong></p> <p>If you use these modeling products in your work, please cite this zenodo repository along with the following papers depending on the part of the grid being used:</p> <p>1) RCTE_cloud_free.zip</p> <p> <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a> </p> <p>2) RCTE_cloudy.zip</p> <p><a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a> </p> <p>3) photochem_cloud_free.zip</p> <p> <a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a> , <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a> </p> <p>4) photochem_cloudy.zip</p> <p> <a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a> , <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a></p> <p> </p>
Data from: Open access levels: a quantitative exploration using Web of Science and oaDOI data
<p>This is the raw data behind the publication (on PeerJ Preprints):</p> <p><strong>Open access levels: a quantitative exploration using Web of Science and oaDOI data</strong></p> <p>Across the world there is growing interest in open access publishing among researchers, institutions, funders and publishers alike. It is assumed that open access levels are growing, but hitherto the exact levels and patterns of open access have been hard to determine and detailed quantitative studies are scarce. Using newly available open access status data from oaDOI in Web of Science we are now able to explore year-on-year open access levels across research fields, languages, countries, institutions, funders and topics, and try to relate the resulting patterns to disciplinary, national and institutional contexts. With data from the oaDOI API we also look at the detailed breakdown of open access by types of gold open access (pure gold, hybrid and bronze), using universities in the Netherlands as an example. There is huge diversity in open access levels on all dimensions, with unexpected levels for e.g. Portuguese as language, Astronomy & Astrophysics as research field, countries like Tanzania, Peru and Latvia, and Zika as topic. We explore methodological issues and offer suggestions to improve conditions for tracking open access status of research output. Finally, we suggest potential future applications for research and policy development. We have shared all data and code openly.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 5. Differences in Validation Criteria between Classical Engineering Sciences and the Field of Brain- Like Artificial Intelligence for Automation
<p>A usual validation procedure in classical fields of engineering and computer sciences as well as in Applied AI, which is currently the dominant AI research domain, is to analyze and implement different potential methods to solve a given problem and to then compare their performance. What is thus usually desired are comparable, quantifiable results. In comparison, the starting situation is<br> different in the field of Brain-Like AI (see Figure 5).</p>
Literature Search from OPUS Deliverable 1.2: Initial State of the Art on Open Science Literature
<p>OPUS Deliverable 1.2: Initial State of the Art on Open Science Literature (<a title="OPUS Deliverable 1.2: Initial State of the Art on Open Science Literature" href="../doi/10.5281/zenodo.8410049" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.8410049</a>) conducted an analysis of initiatives and literature to reform research(er) assessment and incentivise and reward Open Science.<br>Within WP1, a State-of-the-Art on Open Science Literature was conducted. This state-of-the-art on existing literature and recommendations to reform research(er) assessment and incentivise and reward Open Science was designed to support the development of interventions in WP2, of indicators and metrics in WP3 and of pilot action plans in WP4.<br>Within this overall review, specific focus was placed on a review of:</p> <ul> <li>Research(er) assessment and Open Science and incentives and rewards and Open Science</li> <li>Precarity of research careers and Open Science</li> <li>Gender equality and Open Science</li> <li>Industry practices and Open Science</li> <li>Trust and Open Science</li> </ul> <p>This literature review data is available as:</p> <ul> <li>an online library on Zotero - <a title="OPUS Zotero Library" href="https://www.zotero.org/groups/4932671/opus_project_library/collections/H4KBRTUF" target="_blank" rel="noopener">https://www.zotero.org/groups/4932671/opus_project_library/collections/H4KBRTUF</a></li> <li>a single Microsoft Excel file (xlsx) with multiple tabs and</li> <li>CSV (comma-separated values) files.</li> </ul>
Рис. 13. A – Орест Александрович и Елена Евстафьевна. Крым. 1970-е гг. Архив С.О. Скарлато; B – Орест Александрович с внучкой Олей, сыном Сергеем и невесткой Ириной Викторовной Телеш. 1989 г. Архив С.О. Скарлато. in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Рис. 13. A – Орест Александрович и Елена Евстафьевна. Крым. 1970-е гг. Архив С.О. Скарлато; B – Орест Александрович с внучкой Олей, сыном Сергеем и невесткой Ириной Викторовной Телеш. 1989 г. Архив С.О. Скарлато.
Рис. 10. A – О.А. Скарлато с монографией Садайоши МиЯки «Anomura Залива Сагами» по сборам императора Японии, на которую им была написана реценЗиЯ [Скарлато, 1982б (118)]. Июль 1981 г. Архив С.О. Скарлато; B – Орест Александрович Скарлато. 29 ноЯбрЯ 1989 г. Архив С.О. Скарлато. in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Рис. 10. A – О.А. Скарлато с монографией Садайоши МиЯки «Anomura Залива Сагами» по сборам императора Японии, на которую им была написана реценЗиЯ [Скарлато, 1982б (118)]. Июль 1981 г. Архив С.О. Скарлато; B – Орест Александрович Скарлато. 29 ноЯбрЯ 1989 г. Архив С.О. Скарлато.
Рис. 8. A – Директор Зоологического института академик Е.Н. Павловский и Зам. директора института О.А. Скарлато в кабинете директора ЗИНа. 1963 г. Архив С.О. Скарлато; B – Директор Зоологического института член-корр. АН СССР О.А. Скарлато в кабинете директора ЗИНа. НоЯбрь 1990 г. Архив С.О. Скарлато. Fig. 8. A – Director of the Zoological Institute, Academician E.N. Pavlovsky and Deputy Director of the Institute O.A. Scarlato in the Director's Office. 1963. Archive of S.O. Scarlato; B – Director of the Zoological Institute, Corresponding Member O.A. Scarlato in the Director's Office. November 1990. Archive of S.O. Scarlato. in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Рис. 8. A – Директор Зоологического института академик Е.Н. Павловский и Зам. директора института О.А. Скарлато в кабинете директора ЗИНа. 1963 г. Архив С.О. Скарлато; B – Директор Зоологического института член-корр. АН СССР О.А. Скарлато в кабинете директора ЗИНа. НоЯбрь 1990 г. Архив С.О. Скарлато. Fig. 8. A – Director of the Zoological Institute, Academician E.N. Pavlovsky and Deputy Director of the Institute O.A. Scarlato in the Director's Office. 1963. Archive of S.O. Scarlato; B – Director of the Zoological Institute, Corresponding Member O.A. Scarlato in the Director's Office. November 1990. Archive of S.O. Scarlato.
Fig. 9. A – O.A in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Fig. 9. A – O.A. Scarlato – Director of the Zoological Institute. 1975. Archive of S.O. Scarlato; B – O.A. Scarlato at work in the mollusk collections of the Laboratory of Marine Research of the Zoological Institute. Photo By V.N. Tanasijtshuk. Archive of V.N. Tanasijtshuk.
Рис. 11. A – О.А. Скарлато выступает на 7-м малакологическом совеЩании. Слева направо: И.М. Лихарев, О.А. Скарлато, А.Н. Голиков, …. Апрель 1983 г. Архив А.В. Смирнова; B – Встреча на квартире О.А. Скарлато после 7-го малакологического совеЩаниЯ. Апрель 1983 г. Слева направо: …, ИльЯ Михайлович Лихарев, Орест Александрович Скарлато, Анита Алексеевна Нейман, …, …. Архив И.М. Лихарева. in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Рис. 11. A – О.А. Скарлато выступает на 7-м малакологическом совеЩании. Слева направо: И.М. Лихарев, О.А. Скарлато, А.Н. Голиков, …. Апрель 1983 г. Архив А.В. Смирнова; B – Встреча на квартире О.А. Скарлато после 7-го малакологического совеЩаниЯ. Апрель 1983 г. Слева направо: …, ИльЯ Михайлович Лихарев, Орест Александрович Скарлато, Анита Алексеевна Нейман, …, …. Архив И.М. Лихарева.
Рис. 7. A – Е.Н. ГруЗов, О.А. Скарлато и А.Н. Голиков на водолаЗных работах. Дальний Восток. 1962 или 1963 г. Архив А.А. Голикова; B – О.А. Скарлато готовитсЯ к погруЖению. ЮЖный Сахалин. 1963 г. Фото З.В. Кунцевич. in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Рис. 7. A – Е.Н. ГруЗов, О.А. Скарлато и А.Н. Голиков на водолаЗных работах. Дальний Восток. 1962 или 1963 г. Архив А.А. Голикова; B – О.А. Скарлато готовитсЯ к погруЖению. ЮЖный Сахалин. 1963 г. Фото З.В. Кунцевич.
Рис. 5. A – О.А. Скарлато и Зав. Кафедрой Зоологии беспоЗвоночных Ленинградского государственного университета, член-корр. АН СССР профессор В.А. Догель. Май 1955 г. Архив С.О. Скарлато; B – В.В. Хлебович, П.В. Ушаков, О.А. Скарлато и китайский Зоолог У Бао Лин. Конец 1950-х гг. Архив В.В. Хлебовича. in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Рис. 5. A – О.А. Скарлато и Зав. Кафедрой Зоологии беспоЗвоночных Ленинградского государственного университета, член-корр. АН СССР профессор В.А. Догель. Май 1955 г. Архив С.О. Скарлато; B – В.В. Хлебович, П.В. Ушаков, О.А. Скарлато и китайский Зоолог У Бао Лин. Конец 1950-х гг. Архив В.В. Хлебовича.
Fig. 4. A – O.A in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Fig. 4. A – O.A. Scarlato, student of the 2nd year. October 14, 1947. Archive of the S.O. Skarlato; B – Post-graduate student O.A. Scarlato in office. 1950. Archive of I.M. Likharev.
Fig. 3. A in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Fig. 3. A – Lieutenant (junior grade) O.A. Scarlato. 1943. Archive of S.O. Scarlato; B – Sergeant O.A. Scarlato. April 27, 1940. Archive of S.O. Scarlato.
Fig. 2. A – O.A in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Fig. 2. A – O.A. Scarlato with students of the same year of the Biological Faculty, Leningrad University drafted into the Red Army in 1939. From left to right: E.V. Zhukov, V.Yegorov, V.V. Pinevich,A.V. Zhirmunsky, O.A. Scarlato. Zaporozhye. 1940. Archive of S.O. Scarlato; B – Company commander Lieutenant O.A. Scarlato (far left) with his comrades. Odessa. 1945. Archive of S. O. Scarlato.
Supplementary information for "Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability"
<p>Reproducibility data for the manuscript "<em>Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability</em>", it contains data and script for :</p> <ol> <li> <p>The R script <code>01_HDSfreq_Calibration.R</code> of the developed approach to estimate national breeding bird population size using Hierarchical Distance Sampling (HDS) and the secondary candidate set model selection method (Morin et al., 2020)</p> </li> <li> <p>The R script <code>02_pglmm_figures.R</code> for the calibration of the Phylogenetic Generalised Mixed Model (PGLMM) used in the manuscript to compare previous population size estimates to ones modelled using <code>01_HDSfreq_Calibration.R</code>, while accounting for species phylogenetic relatedness</p> </li> <li> <p>The R script <code>03_results_tables.R</code>, used to generate supplementary tables S2.1-3 and S6.1-2.</p> </li> </ol> <ul> <li> <p>Column names are highlighted in italics.</p> </li> </ul> <h2>Data description</h2> <h5>A. BirdPhylo_Burleigh_et_al.tre</h5> <p>A phylogenetic tree from Burleigh et al., 2015. Phylogenetic distances are used as random effect for the PGLMM in script <code>02_pglmm_figures.R</code></p> <h5>B. Conservation_status.txt</h5> <p>A <code>.txt</code> file of the conservation status for France (<em>Statut_FR</em>) and Europe (<em>Statut_EU</em>) for the studied species retrieved from (UICN France et al., 2016). Only <em>Statut_FR</em> is used for the table S6.1.</p> <h5>C. FBBS_trends_20122023.txt</h5> <p>A <code>.txt</code> file containing species trend of the French Breeding Bird Survey data from 2012 to 2023.</p> <ul> <li> <p>Species names (English, French) associated with FBBS trend estimated using data collected from 2012 - 2023</p> </li> <li> <p><em>Hab_specialization</em>, determined from Julliard et al. 2006 approach</p> </li> <li> <p><em>infPrec, supPerc, estimate, se, pval</em> : Species trends over 2012-2023 period in % | lower and upper confidence intervals, mean, standard error and significance</p> </li> </ul> <h5>D. PrepData_HDS.RData</h5> <p>A file containing <code>.RData</code> environment required to run <code>01_HDSfreq_Calibration.R</code> script, it contains :</p> <ul> <li> <p><strong>ATLAS12</strong> : A dataframe with breeding status information from 2012 breeding bird atlas (used to restrict model prediction grid, in regard of 2012 known breeding locations)</p> </li> <li> <p><strong>ConcordTBL</strong> : A concordance table for species names (English, French and scientific notation)</p> </li> <li> <p><strong>EPOC_ODF</strong> : observation dataset, each line corresponds to detected individuals</p> <ul> <li> <p><em>UUID, Ref, ID_liste, ID, ID_place, Grid_10x10</em> : Columns used to identify observations, lists, sites, locations, 10x10 grids</p> </li> <li> <p><em>ID_species_Biolovision, Nom_espece, english_name, scientific_name</em> : Species ID and names</p> </li> <li> <p><em>Date, Day, Month, Year, Julian_date, Obs_hour, Hour_list, Complete_checklist, Commentary, Project_name, Scheme, Observer, List_time, List_diversity, List_abundance</em> : Lists and Observation related effort covariates and metadata</p> </li> <li> <p><em>X_Lambert93_m, Y_Lambert93_m</em> : Observation locations in <code>(crs = 2154)</code></p> </li> <li> <p><em>GPS_loc_observer</em> : Logical, TRUE : location of observers corresponds to true GPS information ; FALSE : observer's location approximated as the barycenter of observations</p> </li> <li> <p><em>X_barycentre_L93, Y_barycentre_L93</em> : Observers location in <code>(crs = 2154)</code></p> </li> <li> <p><em>Use_distance_sampling, Observation_distance_m, Distance_bin_logical, Distance_class_0_25, Distance_class_25_100, Distance_class_100_200, Distance_class_200_more</em> : Distance sampling related informations</p> </li> <li> <p><em>Abudance_brut, Estimate, Number, Nb_male_identified, Nb_female_identified, Nb_juvenile_identified, Nb_grounded, Nb_flying, Nb_auditory, Nb_NA</em> : Observation metadata, used in case of <em>a priori</em> filter over male detection.</p> </li> </ul> </li> <li> <p><strong>grid_pred_envvar</strong> : Prediction grid with environmental covariates, see appendix S3 of the manuscript, covering metropolitan France</p> </li> <li> <p><strong>grid_pred.sf</strong> : corresponding sf object</p> </li> <li> <p><strong>L93_10x10</strong> : sf object corresponding to 10x10 grid used in 2012 atlas</p> </li> <li> <p><strong>ObsVar_EPOCODF</strong> : dataframe specifying lists effort covariates</p> </li> <li> <p><strong>OCCU_EPOC_ODF</strong> : Environmental covariate agregated over lists</p> </li> <li> <p><strong>OCCU_EPOC_ODF_sites_envvar</strong> : Environmental covariate agregated over sites</p> </li> <li> <p><strong>table.pheno</strong> : species table specifying related phenology filter</p> </li> </ul> <h5>E. ReadOutput_HDSfreq_comparison.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over areas determined as breeding in the 2012 atlas. <strong>Predictions were restrained over location known as breeding in 2012 for the sake of comparison.</strong></p> <ul> <li> <p><em>HDS_estimUnfenced_XXX</em> : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript)</p> </li> <li> <p><em>HDS_estim_ExtrapolFence_XXX</em> : average pop. size estimated with confidence interval after prediction post-treatment</p> </li> <li> <p><em>NB_data_calib</em> : Number of observations (distance data, not sites) used for calibration</p> </li> <li> <p><em>MALE_FILTERING</em> : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for the comparison with the previous atlas (in pairs). (cf . line 88-90 in <code>02_pglmm_figures.R</code>)</p> </li> <li> <p><em>EcartDTF_filtrage_maleOnly</em> : If MALE_FILTERING == T, proportion of the remaining data used for calibration after removal of list with individual tagged as female/juvenile (in %)</p> </li> <li> <p><em>Max_dist_breaks</em> : Maximal distance for detection function, after right-side truncation of 5%</p> </li> <li> <p><em>Chat</em> : Coefficient of overdisperion of the best model in the second candidate set</p> </li> <li> <p><em>MED_MEAN_Prob_Detect</em> (.._SE) : weighted averaged median of intercept from the availability state from HDS models, weigthed AICc-wise</p> </li> <li> <p><em>MED_MEAN_Density</em> : weighted averaged median of intercept from the abundance state from HDS models, weigthed AICc-wise</p> </li> <li> <p><em>Significant_phi/lambda</em> : Categorial (Significant/Near/Not), are availability/abundance intercepts significatively different from 0 (significant : alpha = 0.05, near : alpha = 0.1)</p> </li> <li> <p><em>KeyFun_used</em> : Key function used for distance sampling</p> </li> <li> <p><em>Mixtured_used</em> : Mixture used in the abundance state for HDS</p> </li> </ul> <h5>F. ReadOutput_HDSfreq_comparison_20212022.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2022 EPOC-ODF data over areas determined as breeding in the 2012 atlas. Used for the robustness analysis of HDS estimated population size, see appendix S2 and table S2.2 of the manuscript.</p> <h5>G. ReadOutput_HDSfreq_EstimMetropole.csv</h5> <p>A <code>.csv</code> table of species population size estimated using 2021-2023 EPOC-ODF data over <strong>metropolitan France</strong>.</p> <ul> <li> <p><em>HDS_estimUnfenced_XXX</em> : average pop. size estimated with confidence interval before prediction post-treatment (describe in fig 2. of the manuscript)</p> </li> <li> <p><em>HDS_estim_ExtrapolFence_XXX</em> : average pop. size estimated with confidence interval after prediction post-treatment</p> </li> <li> <p><em>MALE_FILTERING</em> : (logical) indicating if female individuals could be detected in the same proportion of males during list recording. FALSE : we considered that estimated pop.size corresponded to the number of individuals leading to a division by 2 for conversion to pop. size in breeding pairs</p> </li> <li> <p><em>Chat</em> : Coefficient of overdisperion of the best model in the second candidate set</p> </li> <li> <p><em>KeyFun_used</em> : Key function used for distance sampling</p> </li> <li> <p><em>Mixtured_used</em> : Mixture used in the abundance state for HDS</p> </li> </ul> <h5>H. TABLE_SpeciesFilters_and_2012Estimates.txt</h5> <p>A <code>.txt </code>table containing species names (English, French and scientific notation), filters and 2012 French atlas pop. size estimates</p> <ul> <li> <p><em>debut_jour</em> : starting day of the month for phenology filter</p> </li> <li> <p><em>debut_mois</em> : starting month for phenology filter</p> </li> <li> <p><em>fin_jour</em> : ending day of the month for phenology filter</p> </li> <li> <p><em>fin_mois</em> : ending month for phenology filter</p> </li> <li> <p><em>Estim_low/up_Atlas2012</em> : Lower and Upper interval of estimated pop. size in 2012 (number in breeding pairs)</p> </li> <li> <p><em>gregarious</em> : logical (0,1) specifying if the species is considered gregarious during its breeding season</p> </li> </ul> <h5>I. sessionInfo_script_XX</h5> <p>User R session information, obtained from <code>sessionInfo()</code> R function, used for running R script.</p> <h2>Code</h2> <h5>A. <code>01_HDSfreq_Calibration.R</code></h5> <p>R script showcasing data formatting and model calibration of the HDS based upon frequentist aproach from <code>unmarked</code> R package. For more details of the model calibration approach, see appendix S4 of the manuscript.</p> <h5>B. <code>02_pglmm_figures.R</code></h5> <p>Script for the calibration of the PGLMM and generation of figure 5 of the manuscript.</p> <h5>C. <code>03_results_tables.R</code></h5> <p>Script to generate tables depicted in Appendices S2 (S2.1-3) and S6 (S6.1-2)</p> <h5>D. <code>HDS_functions.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>, contains 2 functions:</p> <ul> <li> <p><code>Try_HDS()</code> : Function implementing a try-catch permitting calibration of multiple species in a loop.</p> <ul> <li> <p>Species with non convergent models are skipped sending a notification to the user R interface.</p> </li> <li> <p>Used in all sub-candidate sets (i.e. "null", "p", "phi", "lambda")</p> </li> <li> <p>When phase="ALL" corresponding to the second candidate set (i.e. ensemble of best model candidates, with delta_AIC <= 10, from previous sub-candidate sets), it permits the use of previous sub-candidates set coefficients as starting values, with <code>StartValues </code>argument</p> </li> <li> <p>Later part of the function hack the call of the unmarkedFit class, in order to accommodate from calibrating a gdistsamp using characters formulas</p> </li> </ul> </li> <li> <p><code>fitstats()</code> : Function from unmarked::parboot(), available with <code>help(parboot)</code>. Allow estimation of multiple goodness-of-git statistic (Freeman-Tukey, Chi-squared and Sum of Squared Estimate of errors) through parametric bootstrap. In the manuscript, only chi-squared metric is used.</p> </li> </ul> <h5>E. <code>dsmextra_modif_function.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>. Miscellaneous adjustment of core function from <code>dsmextra </code>package (main change being the integration of tolerance argument (<code>tol</code>) in the chain of function.</p> <h5>F. <code>misc_unmarked.R</code></h5> <p>R script called in <code>01_HDSfreq_Calibration.R</code>. modify Setmethods for unmarked function, in particular for <code>unmarked::parboot</code>, allowing parallelization of parametric bootstrap with prior unmarked version (<code>unmarked < 1.3.0</code>).</p> <h2>References</h2> <p>Data was derived from the following sources:</p> <ul> <li> <p>Burleigh, J.G., Kimball, R.T., Braun, E.L., 2015. Building the avian tree of life using a large-scale, sparse supermatrix. Molecular Phylogenetics and Evolution 84, 53–63. <a href="https://doi.org/10.1016/j.ympev.2014.12.003">https://doi.org/10.1016/j.ympev.2014.12.003</a></p> </li> </ul> <p>Other sources :</p> <ul> <li> <p>Julliard, R., Clavel, J., Devictor, V., Jiguet, F., Couvet, D., 2006. Spatial segregation of specialists and generalists in bird communities. Ecology Letters 9, 1237–1244. <a href="https://doi.org/10.1111/j.1461-0248.2006.00977.x">https://doi.org/10.1111/j.1461-0248.2006.00977.x</a></p> </li> <li> <p>Morin, D.J., Yackulic, C.B., Diffendorfer, J.E., Lesmeister, D.B., Nielsen, C.K., Reid, J., Schauber, E.M., 2020. Is your ad hoc model selection strategy affecting your multimodel inference? Ecosphere 11, e02997. <a href="https://doi.org/10.1002/ecs2.2997">https://doi.org/10.1002/ecs2.2997</a></p> </li> <li> <p>UICN France, MNHN, LPO, SEOF, ONCFS, 2016. La Liste rouge des espèces menacées en France - Chapitre Oiseaux de France métropolitaine. Paris, France.</p> </li> </ul>
Figure 4 in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 4. Agricultural Research Service researchers have focused on understanding how climate change influences weeds/invasive plants and their impacts and management. Image shows a study of how precipitation change influences cheatgrass (Bromus tectorum) invasion in rangelands of northeast Wyoming, USA. (Credit: Anna Kuhne)
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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)
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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.