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2,516 results for “Seri”
1 km Monthly Maximum Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
1 km Monthly Average Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
1 km Monthly Minimum Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
Meteorological time series recorded at the station implemented in OAL-Austria
<p>Time series of meteorological variables recorded at the station implemented in OAL-Austria with the following senors:</p> <p>Variable | Unit | Sensors | Measurement<br> AT | degC | Judd, HMP45 | 60 min Avg<br> RH | % | HMP45 | 60 min Avg<br> SR | W/m2 | Pyranometer SP-Lite | 60 min Avg<br> WS | m/s | Wind wheel A100R | 60 min Avg + Max<br> JD | cm | Judd Ultraschall | 60 min burst<br> VSWD | cm | Yard stick+DCIM | daily<br> SLT | degC | TDR-315L in 15, 30 and 50cm depth | 60 min burst - Avg<br> SLVW | % | TDR-315L in 15, 30 and 50cm depth | 60 min burst - Avg<br> P | mm/m2 | ARG100 (during summer season) | 60 min Total</p> <p> </p> <p>Location: 11.596024 E, 47.267723 N, elevation: 1090m a.s.l.</p>
Global temperature time series from IPCC AR6
<p>Annual global mean temperature time series used in IPCC Sixth Assessment Report (AR6). Includes both consolidated mulit-dataset mean and individual component data sets. Further documentation is available in AR6 (Working Group I, section 2.3.1). </p>
FIG. 2 in Taxonomy of Mexican and Central American Members of Solanum Series Conicibaccata (sect. Petota)
FIG. 2. Distributions of the Mexican and Central American species of Solanum series Conicibaccata.
Approximate Entropy of Spiking Series Reveals Different Dynamical States in Cortical Assemblies
<p>Self-organized criticality theory proved that information transmission and computational performances of neural networks are optimal in critical state. By using recordings of the spontaneous activity originated by dissociated neuronal assemblies coupled to Micro-Electrode Arrays (MEAs), we tested this hypothesis using Approximate Entropy (ApEn) as a measure of complexity and information transfer. We analysed 60 min of electrophysiological activity of three neuronal cultures exhibiting either sub-critical, critical or super-critical behaviour. The firing patterns on each electrode was studied in terms of the inter-spike interval (ISI), whose complexity was quantified using ApEn. We assessed that in critical state the local complexity (measured in terms of ApEn) is larger than in sub- and super-critical conditions. Our estimations were stable when considering epochs as short as 5 min. These preliminary results indicate that ApEn has the potential of being a reliable and stable index to monitor local information transmission in a neuronal network during maturation. Thus, ApEn applied on ISI time series appears to be potentially useful to reflect the overall complex behaviour of the neural network, even monitoring a single specific location.</p>
dataset related to article "EXPANDING THE GENETIC SPECTRUM OF PRIMARY FAMILIAL BRAIN CALCIFICATION DUE TO SLC2OA2 MUTATIONS: A CASE SERIES"
<p>Sanger sequences (.abi files) of all patients of our Fondazione Besta Cohort.</p>
Global forest reference set with time series annual change information
<p><strong>Brief introduction of the reference set:</strong></p> <ul> <li>The initial sample points were stratified. The ratio of non-forest, forest and forest change stratum area is about 67:27:6. In order to increase the number of forest and forest change sample points, the ration of forest sample, forest change sample and non-forest sample points is roughly 4:1:1. The information of stratum identifier is provided in this sample set. The study generated 12925 initial sample points which were further reduced to 10339 points (6252 persisting forest sample points, 2049 change sample points and 2038 persisting non-forest sample points). The details are shown in Table 1. </li> </ul> <table> <caption>Table 1. Initial and finial sample size in each stratum</caption> <tbody> <tr> <td><strong>Stratum name</strong></td> <td><strong>Percent area of each stratum</strong></td> <td><strong>Initial sample size</strong></td> <td><strong>Final sample size</strong></td> <td><strong>Eliminated sample size</strong></td> </tr> <tr> <td>Forest</td> <td>27%</td> <td>8332</td> <td>6252</td> <td>1644</td> </tr> <tr> <td>Non-forest</td> <td>67%</td> <td>1892</td> <td>2038</td> <td>207</td> </tr> <tr> <td>Forest change</td> <td>6%</td> <td>2701</td> <td>2049</td> <td>735</td> </tr> <tr> <td>Sum</td> <td>100%</td> <td>12925</td> <td>10339</td> <td>2586</td> </tr> </tbody> </table> <p> </p> <ul> <li>The sample set will be provided in shapefile version. (The eliminated sample points will not be provided.) In this version, the provided attributions are shown in Table 2.</li> </ul> <p> </p> <table> <caption>Table 2. Details of provided attributions in this version</caption> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>per_f</td> <td>unchanged forest (0: No 1: Yes)</td> </tr> <tr> <td>per_nonf</td> <td>unchanged non-forest (0: No 1: Yes)</td> </tr> <tr> <td>change</td> <td>forest change (0: No 1: Yes)</td> </tr> <tr> <td>loss</td> <td>forest loss times</td> </tr> <tr> <td>gain</td> <td>forest gain times</td> </tr> <tr> <td>times</td> <td>change times (gain + loss)</td> </tr> <tr> <td>changeinfo</td> <td>year and order of change</td> </tr> <tr> <td>initial_s</td> <td>initial stratum identifier: change/perf (persisting forest)/nonf (persisting non-forest)</td> </tr> </tbody> </table> <p> </p> <ul> <li>This reference set will be updated regularly. Now it's version 1.1 (from 2000 to 2020).</li> </ul> <p> </p> <p><strong>NOTES:</strong></p> <ol> <li>Please be cautious while using the sample points with multiple change times. </li> <li>We encourage people to send us feedback if you found some mistakes while using this sample set via email.</li> <li>Welcome discussions around potential collaborations. 【You can email Jing (guoj15@tsinghua.org.cn).】</li> </ol> <p> </p> <p><strong>Citation:</strong></p> <p>Please cite the dataset including the version number and the following paper when using this data: </p> <p>Jing Guo, Zhiliang Zhu & Peng Gong (2022) A global forest reference set with time series annual change information from 2000 to 2020, International Journal of Remote Sensing, 43:9, 3152-3162, DOI: <a href="https://doi.org/10.1080/01431161.2022.2088256">10.1080/01431161.2022.2088256</a></p>
Sentinel-1 InSAR time-series and velocity map over the Bay Area (Descending track 42, 2015-2020)
<p>Supplemental material for <em>"Spatiotemporal variations of surface deformation, shallow creep rate, and slip partitioning between the San Andreas and southern Calaveras Fault"</em> at JGR-Solid Earth</p> <p>Citation: <strong>Li, Y</strong>., Bürgmann, R., & Taira, T. (2023). Spatiotemporal variations of surface deformation, shallow creep rate, and slip partitioning between the San Andreas and southern Calaveras Fault. <em>Journal of Geophysical Research: Solid Earth</em>, 128, e2022JB025363. <a href="https://doi.org/10.1029/2022JB025363">https://doi.org/10.1029/2022JB025363</a></p>
How is Transient Behavior Addressed in Practice? Insights from a Series of Expert Interviews - Supplementary Material
<p>This is the supplementary material of the following publication:</p> <p>Samuel Beck, Sebastian Frank, Alireza Hakamian, André van Hoorn. 2022. How is Transient Behavior Addressed in Practice? Insights from a Series of Expert Interviews. In <em>Companion of the 2022 ACM/SPEC International Conference on Performance Engineering (ICPE ’22 Companion)</em>, April 9–13, 2022, Bejing, China. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3491204.3527483.</p> <p>The supplementary material contains the questions that were asked during the interviews and the five transcripts that were produced as a result of the interviews. The interviews were conducted in German and the transcripts have not been translated into English.</p>
Wind and solar capacity factor time series by year and grid cell over the contiguous U.S.
<p>This data set contains hourly capacity factor time series of wind and solar resources over the contiguous U.S.</p> <p> </p> <p>The included time series cover four individual years and 2,586 grid cells. The years range from 2016 to 2019. The grid cells correspond to the grid cells of the NASA's MERRA-2 reanalysis data set into which the contiguous U.S. is subdivided. The grid cells have a spatial resolution of 0.5° latitude x 0.625° longitude with dimensions ranging from about 55 km x 45 km to 55 km x 62 km.</p> <p> </p> <p>This data set is used to generate the results of the following journal article:</p> <p>Enrico G. A. Antonini, Tyler H. Ruggles, David J. Farnham, Ken Caldeira, "The quantity-quality transition in the value of expanding wind and solar power generation", iScience 25 (4), 104140, 2022.</p> <p> </p> <p>Code and instructions required to reproduce the results reported in the above paper are available in the GitHub repositories at <a href="https://github.com/eantonini/Distributed_wind_and_solar_generation">https://github.com/eantonini/Distributed_wind_and_solar_generation</a> and <a href="https://github.com/carnegie/MEM_public/tree/Antonini_et_al_2022">https://github.com/carnegie/MEM_public/tree/Antonini_et_al_2022</a>.</p>
Photos of participants of Turin Workshop (11 and 12 April 2022): Skull collections. Series, standardization and instruments
<p>During the two days of the workshop in the Museum of Human Anatomy was inaugurated the Exhibition: "The art of trepanning".</p> <p>The artworks of the Peruvian artist Fernando Bedoya, exhibited near the showcase where the two South American mummies are preserved, are intended to dialogue with the human findings as well as evoking the ancient surgical practices of the Incas, known as trepanation, which consisted of the perforation of the skull.</p> <p>Bedoya, through his ceramic replicas of ancient heads, imitates the act of trepanation by perforating twice the head. One hole is left open, following the sign of the surgical intervention, while the other hole is covered with a bottle cap of Coca-Cola, a brand of the modern global exploiter.</p> <p>On display are also four of his engravings from the “Fardos” series, where the use of collage and silkscreen techniques bind together like the bandaging of a mummy.</p>
3D ED tilt series of Argyrin D
<p>The electron diffraction tilt series (3D ED) was collected in continuous rotation mode using the dedicated TEM-controlling script (Gorelik at al., 2021). </p> <p>The sample was ultrasonically dispersed in EtOH. A drop of the suspension was placed onto carbon-coated TEM cupper grid. 3D ED data were collected using a Thermofischer TALOS TEM, operated at 200kV (Ulm University). The data were collected at liquid nitrogen temperature. Dry sample was inserted into the TEM and then cooled – no cryo-transfer procedure was applied. Cryo-transfer sample holder allowing for high-tilt goniometer rotation was used. Electron diffraction was performed in nanodiffraction mode with condenser C2 aperture of 50 microns. The effective beam diameter on the sample was about 7 microns. The sample was scanned with the electron dose rate of 0.003 e/A2s, electron diffraction patterns were collected with the dose rate of 0.03 e/A2s. A single dataset within a tilt range of -70…70° containing 118 frames, had a total accumulated dose of 3.5 e/A2. Binning 4 of the CETA camera was used, camera length was 1.35m, resulting in the pixel size of 0.00166 A-1 of 1024x1024 frames, corresponding to the effective detector distance of 1,344 mm. Single frame exposure was 1 second, relative goniometer rotation speed 0.04, resulting in the effective oscillation range of 1.18°.</p> <p>The lateral crystal size was ca. 1.5 micron x 6 micron.</p> <p><em>Gorelik et al., 2021. Acta Cryst. B77, 662–675.</em></p>
Data from: A 26-year time series of mortality and growth of the Pacific oyster C. gigas recorded along French coasts
<p><strong>Contents:</strong> database of oyster growth (<em>i.e., </em>the changes in mass over time) and mortality along French coasts since 1993. To build this database, we took advantage of the Pacific oyster production monitoring network coordinated by IFREMER (the French Research Institute for the Exploitation of the Sea). This network monitors the growth and mortality of spat (less than one-year-old individuals) and half-grown (between one and two-year-old individuals) <em>Crassostrea gigas</em> oysters since 1993. As the number of sites monitored over the years varied, we focused on 13 sites that were almost continuously monitored during this period. For these locations, we modeled growth and cumulative mortality for spat and half-grown oysters as a function of time, to cope with changes in data acquisition frequency, and produced standardized growth and cumulative mortality indicators to improve data usability. Code to reproduce these analyses are archived here, as well as figures included in the companion data paper: "A 26-year time series of mortality and growth of the Pacific oyster <em>C. gigas</em> recorded along French coasts".</p> <p><strong>Sampling protocol: </strong>in the oyster production monitoring network, oysters were mainly reared in plastic meshed bags fixed on iron tables, mimicking the oyster farmers practices. After their deployment at the beginning of the campaign (seeding dates from February to April depending on the year), growth and mortality were longitudinally monitored yearly. At each sampling date, local operators carefully emptied each bag in separate baskets, counted the dead individuals and alive ones, and removed the dead individuals. Then local operators weighed all alive individuals in each basket (mass taken at the bag level, protocol mainly used between 1993 and 1998 and since 2004) and/or collected 30 individuals to individually weigh them in the laboratory (mass taken at the individual level, protocol used between 1995 and 2010 for spat and since 1996 for half-grown oysters).</p> <p><strong>Data:</strong></p> <ul> <li>AllDataresco. csv is a csv file containing the raw observations of oyster growth and mortality recorded within the REMORA, RESCO and ECOSCOPA programs. This data set is a modified extraction (carried out on 2021-07-20) of the RESCO REMORA Database (https://doi.org/10.17882/53007) available in SEANOE, an academic publisher or marine research data. The table contains 571101 rows and 18 columns. Description of columns: <ul> <li>program: the name of the program. Blank cells indicate that this information was not available.</li> <li>mnemonic_site: the mnemonic is a unique identifier of the site and is constructed as follows: code of the marine area - P (for monitoring point) - order number of the monitoring location in the marine area. For example, 014-P-055.</li> <li>site: the name of the site.</li> <li>class_age: the age class of the oyster: N0 (spat), J1 (half-grown) or A2 (commercial size). Blank cells indicate that this information was not available.</li> <li>ploidy: the ploidy of the oysters: diploïdes or triploïdes (in English: diploid or triploid). Blank cells indicate that this information was not available.</li> <li>date: the date of data collection (format DD/MM/YYYY).</li> <li>mnemonic_date: mnemonic of the visit. The name of the quarterly operation (P0, P1, P2, P3 or RF: last data collection). For intermediate operations, we use the previous name of the operation followed by an underscore and the number of the week. For example, data collection on 2019-05-06 corresponds to P1_S19. Biométrie initiale (in English: initial biometrics) is equivalent to P0 (first data collected during the campaign).</li> <li>param: the name of the measured parameter: Nombre d'individus morts, Nombre d'individus vivants, Poids de l'individu or Poids total des individus vivants (in English: number of dead oysters, number of alive oysters, mass of the individual and total mass of alive individuals).</li> <li>code_param: code of the measured parameter. INDVVIVNB = number of alive oysters, INDVMORNB = number of dead oysters, INDVPOID = mass of the individual, TOTVIVPOI = total mass of alive individuals (i.e., the mass of the bag).</li> <li>unit_measure: the unit of measurement: Gramme or Unité de dénombrement (d'individus, de cellules, ...)</li> <li>fraction: either the measure was made at the bag level on which case the fraction is "Sans objet" = Not applicable or the measure was made at the individual level (code_param = INDVPOID), in which case the fraction indicates the part of the oyster that was measured: Chair totale égouttée or coquille (in English: total flesh drained or shell).</li> <li>method: the method used to obtain the data. For the number of alive and dead oysters (code_param = INDVVIVNB and INDMORNB), the method is comptage macroscopique (in English: macroscopic count). For mass taken at the individual level (code_param = INDVPOID), the method is Pesée après lyophilisation or Pesée simple sans préparation (in English: weighing without preparation or weighing after lyophilization).</li> <li>id_ind: the id of the individual oyster when code_param is INDVPOI or the id of the bag when code_param is INDVVIVNB, INDVPOID and TOTVIVPOI.</li> <li>value: numeric value of the measurement.</li> <li>mnemonic_sampling: This is a concatenated field. Its coding is not consistent throughout the dataset. Indeed, it is sometimes composed of the first letter of the program name attached to 2 numbers indicating the year of data collection and the age class (gj: spat, ga: half-grown or commercial size oysters) - 2 letters indicating the region attached to a 4-character site identifier- mnemonic passage. For example, R05gj-NOBV02-P0 corresponds to data collected in the program REMORA in 2005 on gigas spat (gj) in Normandy (NO) in the site Géfosse 02 (BV02) in the 1<sup>st</sup> quarter (P0). Other times the mnemonic_prelevement is composed of the first two letters of the program name attached to 2 numbers indicating the year of data collection _ the age class (GJ: spat, GA18: half-grown, GA30: commercial size oysters) attached to the origin of the initial spat group (this information is not always indicated) (CN + number: identifier of wild-caught site, ET + character: identifier of the hatchery, NSI: Argenton hatchery via a standardized protocol) _ a 4-character identifier for the site. For example, RE12_GJET2_BV02 corresponds to data collected in the program REMORA in 2012 (RE12) on gigas spat born in hatchery 2 (GJET2) in the site Géfosse 02 (BV02). Finally, mnemonic_prelevement is sometimes: Biométrie initiale (initial biometrics), Biométrie initiale 6 mois (initial biometrics of spat), Biométrie initiale 18 mois and Biométrie initiale adulte (both correspond to initial biometrics of half-grown oysters), Biométrie initiale 30 mois (initial biometrics of commercial size oysters), Biométrie initiale NSI (initial biometrics of spat batch produced in Argenton Ifremer hatchery via a standardized protocol).</li> <li>long: The longitudinal coordinate of the site given in decimal in the WGS 84 system.</li> <li>lat: The latitudinal coordinate of the site given in decimal in the WGS 84 system.</li> <li>pop_init_batch: this is a concatenated field. It is composed of the two first letters of the name of the program name attached to 2 numbers indicating the year of data collection _ the age class code (GJ: gigas spat, GA: gigas half-grown, GA30: gigas commercial size) _ the origin of the initial spat group (CN: wild-caught, ET: hatchery, NSI: Argenton hatchery) attached to two numbers indicating the year of birth of the initial spat group _ the birth place of the initial spat group (this one is optional). For example, RE00_GJ_CN99_AR corresponds to data collected in the program REMORA in 2000 (RE00) on spat oysters (GJ) born in 1999 and wild-caught (CN99) in the Bay of Arcachon (AR). Blank cells indicate that this information was not available.</li> </ul> </li> </ul> <p> </p> <ul> <li>sites.csv is a csv file of 7 columns and 13 rows containing information about the 13 sites. Description of the columns found in the data set: <ul> <li>num: a unique identifier for each site. Ranges between 1 and 13.</li> <li>site: the abbreviated name of the site.</li> <li>Name: the full name of the site.</li> <li>zone_fr: the French name of the zone where data collection took place.</li> <li>zone_en: the English name of the zone where data collection took place.</li> <li>lat: the latitudinal coordinate of the site given in decimal in the WGS 84 system.</li> <li>long: the longitudinal coordinate of the site given in decimal in the WGS 84 system.</li> </ul> </li> </ul> <p> </p> <ul> <li>DataResco_clean.csv is the curated data set of oyster growth and mortality (csv file). The table contains 5178 rows and 13 columns. Each row corresponds to the mean cumulative mortality and mean mass of oysters for a specific date x site x age class combination. This is the data set we used to fit logistic and Gompertz models to describe mean mass and cumulative mortality at time <em>t</em>. Description of the columns found in the data set: <ul> <li>num, site, name, zone_en, lat, long: see the description above for the data set sites.csv.</li> <li>campaign: the year of data collection. Ranges between 1993 and 2018.</li> <li>class_age: the age class of the oyster (i.e. spat: N0 or half-grown: J1).</li> <li>batch: the identifier of the batch (group of oysters born from the same reproductive event, having experienced strictly the same zootechnical route). It is a field that concatenates the campaign, the age class of oysters (spat: N0 or half-grown: J1), the origin of the initial spatgroup (wild-caught: CAPT or Ifremer hatchery: ECLO), ploidy (diploid: 2n) and birthplace of the original spatgroup (AR: Bay of Arcachon or E4: Ifremer hatchery of Argenton).</li> <li>date: the day of data collection (format YYYY-MM-DD).</li> <li>DOY: the day of the year (count of days since the beginning of the year). It ranges between 46 and 354.</li> <li>mean_CM: the mean cumulative mortality of oysters. It ranges between 0 and 0.956 (<em>i.e.</em>, between 0–95.6%). We first calculated the cumulative mortality for each bag x date x site x age class combination according to the following formula: CM<em><sub>t</sub></em> = 1 – ((1 – CM<em><sub>t-1</sub></em>) × (1 – IM<em><sub>t</sub></em>)). CM<em><sub>t</sub> </em>= Cumulative mortality at time <em>t</em>; CM<em><sub>t-1 </sub></em>= Cumulative mortality at time <em>t</em>-1; IM<em><sub>t</sub></em> = Mortality rate at time <em>t</em>. IM<em><sub>t</sub></em> was obtained by dividing the number of dead oysters by the sum of alive and dead oysters at time <em>t</em>. When several bags were followed, we then averaged the cumulative mortality per date x site x age class combinations. IM<sub>t</sub> was obtained by dividing the number of dead oysters by the sum of alive and dead oysters at time <em>t</em>. When several bags were followed, we then averaged the cumulative mortality by date x site x age class combination. NA values indicate that this information was not available.</li> <li>mean_mass: The mean mass of oysters in grams. It ranges between 0.28 and 122.51 g. For mass data collected until 2008, we calculated the mean of the individual mass per date x site x age class combination by averaging the mass of the individuals. In other cases (mass data collected since 2009), we calculated the mean mass of individuals for each bag x date x site x class age combination by dividing the total mass of living oysters by the number of living individuals and then averaged data by date x site x age class combination. The mean mass is thus the mean of the individual mass until 2008 and the mean mass of individuals since 2009. NA values indicate that this information was not available.</li> </ul> </li> </ul> <p> </p> <ul> <li>data/clean/DataResco_predicted.csv is a csv file containing the cumulative mortality and mass of oysters predicted by the best sigmoid model. The table contains 148239 rows and 11 columns. Each row corresponds to the cumulative mortality and mass predicted for a specific day x campaign x site x age class combination. <ul> <li>num, site, name, zone_en, lat, long: see the description above for the data set sites.csv.</li> <li>campaign: the year of the data prediction. Ranges between 1993 and 2018.</li> <li>classe_age: the age class of the oyster (<em>i.e.,</em> spat: N0 or half-grown: J1).</li> <li>DOY: the day of the year (count of days since the beginning of the year). It ranges from 65 (median day of seeding date) to 337 (median day of the end of the monitoring).</li> <li>CM_pred: cumulative mortality predicted by the best model (<em>i.e.,</em> Gompertz model).</li> <li>mass_pred: mass predicted (in grams) by the best model (<em>i.e.,</em> logistic model).</li> </ul> </li> </ul> <p><strong>Code: </strong></p> <ul> <li>0_map_sampling_location.R contains the code to recreate the map of the sampling sites</li> <li>1_cleaning_data.R contains the code for data cleaning. This script also computes the mean cumulative mortality and mean mass of oysters per date x site x class age combination (DataResco_clean.csv)</li> <li>2_analysis_sigmoide.Rmd does the logistic and Gompertz models of the mean cumulative mortality and mean mass for spat and half-grown oysters. This script computes the cumulative mortality and mass of oysters predicted by the best sigmoid model (DataResco_predicted.csv).</li> </ul> <p>Contacts: for questions, please contact: elodie.fleury@ifremer.fr or Julien.normand@ifremer.fr</p>
Long series of semi-thin transverse sections of the vascular cambium of Robinia pseudoacacia L. (a-l)
<p>A series of consecutive transverse sections of the vascular cambium of black locust used for radial, and tangential planes reconstruction in: </p> <p>Miodek, A., Gizińska, A., Włoch, W. <em>et al.</em> Intrusive growth of initials does not affect cambial circumference in <em>Robinia pseudoacacia</em>. <em>Sci Rep</em> <strong>12, </strong>7428 (2022). https://doi.org/10.1038/s41598-022-11272-y</p> <p>Scale bar = 20 μm</p>
Data from: Girth increment changes in response to soil water availability in lowland dipterocarp forest in Borneo: an individualistic time-series analysis
<p><span>Time-series data offer a way of investigating the causes driving ecological processes as phenomena. To test for possible differences in water relations between species of different forest structural guilds at Danum (Sabah, NE Borneo), daily stem girth increments (gthi), of 18 trees across six species were regressed individually on soil moisture potential (SMP) and temperature (TEMP), accounting for temporal autocorrelation (in GLS-arima models), and compared between a wet and a dry period. The best-fitting significant variables were SMP the day before and TEMP the same day. The first resulted in a mix of positive and negative coefficients, the second largely positive ones. An adjustment for dry-period showers was applied. Interactions were stronger in dry than wet period. Negative relationships for overstorey trees can be interpreted in a reversed causal sense: fast transporting stems depleted soil water and lowered SMP. Positive relationships for understorey trees meant they took up most water at high SMP. The unexpected negative relationships for these small trees may have been due to their roots accessing deeper water supplies (if SMP was inversely related to that of the surface layer), and this was influenced by competition with larger neighbour trees. A tree-soil flux dynamics manifold may have been operating. Patterns of mean diurnal girth variation were more consistent among species, and time-series coefficients were negatively related to their maxima. Expected differences in response to SMP in the wet and dry periods did not clearly support a previous hypothesis differentiating drought and non-drought tolerant understorey guilds. Trees within species showed highly individual responses when tree size was standardized. Data on individual root systems and SMP at several depths are needed to get closer to the mechanisms that underlie the tree-soil water phenomena in these tropical forests. Neighborhood stochasticity importantly creates varying local environments experienced by individual trees.</span></p>
Towards understanding the importance of time-series features in automated algorithm performance prediction
<p><strong>merged_feature_importance.csv</strong> - CSV with feature importance values with different meta-models, forecasting algorithms, and feature importance methods computed on 30 different train/test splits.</p> <p><strong>Catch22.csv</strong> - Catch22 features (raw time-series)</p> <p><strong>Catch22Log.csv</strong> - Catch22 features (log time-series)</p> <p><strong>Catch22Diff.csv</strong> - Catch22 features (differenced time-series)</p> <p><strong>TSFresh.csv</strong> - TSFresh features (raw time-series)</p> <p><strong>TSFreshLog.csv</strong> - TSFresh features (log time-series)</p> <p><strong>TSFreshDiff.csv</strong> - TSFresh features (differenced time-series)</p> <p><strong>mape.csv</strong> - sMAPE performance for all forecasting algoirthms</p>
3D+time nuclei tracking dataset of confocal fluorescence microscopy time series of C. elegans embryos
<p>The dataset consists of 3 confocal microscopy time series of <em>C. elegans</em> embryos, fully tracked with StarryNite followed by manual curation</p> <ul> <li>3 raw time-series and the corresponding tracks/lineage trees</li> <li>temporal resolution; 75s</li> <li>temporal extent: 400 frames, tracked for at least 370 frames</li> <li>spatial resolution (zyx): 0.75 x 0.15 x 0.15 μm</li> <li>spatial extent (zyx):/ 41 x 512 x 512px</li> <li>Microscope: Zeiss Axio Observer.Z1</li> </ul> <p>The annotations were created using the method described in:</p> <p><em> Santella, A., Du, Z. & Bao, Z. A semi-local neighborhood-based framework for probabilistic cell lineage tracing. BMC Bioinformatics 15, 217 (2014). <a href="https://doi.org/10.1186/1471-2105-15-217">https://doi.org/10.1186/1471-2105-15-217</a></em></p> <p>Additionally the data was extended and used for the development of a new tracking method in the following publication:</p> <p><em> Hirsch, P., Malin-Mayor C., Santella, A., Preibisch, S., Kainmueller, D., Funke, J. Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages. MICCAI 2022.</em></p> <p>For questions please contact Peter Hirsch (<a href="mailto:peter.hirsch@mdc-berlin.de">peterhirsch@posteo.de</a>).</p>
Are fine roots 'leaves underground' in terms of allometry? A test in a tropical forest successional series in southwest China
<p>Fine roots have been hypothesized to be "leaves underground" in terms of vascular network, but this hypothesis has rarely been tested within the framework of metabolic scaling theory (MST). We measured average fine-root (diameter < 1 mm) mass (M), surface area (A), volume (V), diameter (D) and length (L) for 216 soil cores from 24 plots across four successional stages in tropical forests of Xishuangbanna (southwest China), and examined eight scaling relationships between these variables at the individual root scale. We tested whether fine-root allometries conformed to MST's model for leaf (MSTl) or model (MSTw) for woody organs (e.g. trunk). We also assessed the relative effects of environmental factors, tree size, species composition and diversity, and stand structural factors on allometric relationships using structural equation models (SEMs). Our results showed that: 1) Fine-root scaling exponents rarely conformed to MSTl's predictions. 2) The scaling exponents between fine-root M, A, V and D all conformed to MSTw's predictions in later successional forests, but showed greater deviation towards early successional stage. 3) The scaling exponents associated with fine-root length differed markedly from MSTw's predictions. 4) Changes of some fine-root scaling exponents across successional stage were mainly affected by tree size or soil fertility, and species composition affected allometry only indirectly via tree size. Our results suggested that the allometries of individual fine roots largely conform to the scaling rules governing woody organs instead of leaves, probably because leaves are nearly two-dimensional objects while the other two are three-dimensional. We showed that MSTw can well predict some fine-root allometries in later successional forests, suggesting great potential of utilizing MSTw to better estimate fine-root biomass and productivity. However, the present MSTw still needs to be improved for predicting the scaling relationships concerning fine-root length, and also for better quantifying allometric exponents in earlier successional forests.</p>
ScienceDex guides
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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)
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.