Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,168
datasets available to search
ShareScore release 0.7.1
Dataset results
3,168 results for “maturity”
Impacts of Phloem Chilling on Mature Red Maples at Harvard Forest 2019
Whether tree growth is limited by carbon supply or demand is a crucial question due to wide-ranging repercussions for projections of carbon sequestration on land. By temporarily restricting phloem transport using stem chilling, which increases phloem sap viscosity to create local bottlenecks to phloem transport, we created gradients of carbon supply in stems of mature red maples during the first half of the growing season. These carbon supply gradients had clear effects on tree physiology with radial growth in particular varying up to almost seven-fold with carbon supply. Local bulk nonstructural carbon concentrations in stems and roots remained relatively stable, suggesting that they are not rapidly modulated in response to changes in supply and demand. However, phloem and leaf nonstructural carbon accumulated above chilling-induced bottlenecks and were associated with reductions in photosynthetic capacity as well as the advancement of leaf coloration and fall, supporting the idea of within-tree feedbacks reducing carbon supply when supply exceeds demand. Most strikingly, radial growth varied systematically with carbon supply up to almost seven-fold, indicating that growth of red maple during the early growing season is strongly carbon-supply limited. The code to process these data and reproduce our results is available at https://github.com/TTRademacher/Exp2019Analysis. For more details pertaining to the methods see Rademacher et al. (2021) and contact the investigator.
Impacts of Phloem Chilling and Compression on Mature White Pine at Harvard Forest 2018
Wood formation is a crucial process for carbon sequestration on land, yet how variations in phloem-transported carbon and temperature affect wood formation, respiration and nonstructural carbon pools remains poorly understood. To better understand the role of carbon supply on allocation to wood formation, we constrained phloem transport using compression and chilling around the stem of 15 mature white pines to monitor the effects of contrasting carbon supply (enriched above and reduced below the manipulations) on local wood formation and respiration, as well as on nonstructural carbon pools in stems and roots. This data set contains all data measured during the experiment. This includes wood anatomical, xylogenetic, dendrochronological, stem CO2 efflux, and nonstructural carbon measurements in coarse roots and stems, as well as pre-dawn water potential measurements of needles and branches. Furthermore, we provide basic allometric measurements for all trees. The code to process these data and reproduce our results is available at https://github.com/TTRademacher/Exp2018Analysis. For more details pertaining to the methods see Rademacher et al. (2021) and contact the investigator.
InnoVine WP3: 105 phenolic compound quantification of 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated Vitis vinifera cultivars
<p>FP7/311775 InnoVine (Innovation in vineyard): Combining innovation in vineyard management and genetic diversity for a sustainable European viticulture</p> <p>WP3: Exploiting the genetic diversity in grapevine</p> <p>105 phenolic or related compounds, from 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated <em>Vitis vinifera</em> cultivars, were quantified by UPLC-TQ-MRM Mass Spectrometry (Lambert M<em> et al., Molecules</em> <strong>2015</strong>, <em>20</em>(5), 7890-7914; doi:10.3390/molecules20057890 & Pinasseau L <em>et al.</em>, <em>Molecules</em> <strong>2016</strong>, <em>21</em>(10), 1409; doi:10.3390/molecules21101409).</p> <p>3 parameters were added:<br> - water/drought status (delta C13)<br> - sugar content (refractive index, brix degree)<br> - weight of 100 grape berries</p> <p>All plant material was collected at the Vassal repository: French National Grapevine Germplasm Collection, INRA Domaine de Vassal, 34340 Marseillan-Plage, France (Centre de Ressources Biologiques de la Vigne (CRB-Vigne) de Vassal-Montpellier).</p>
Steady state carbon, nitrogen, phosphorus, and water budgets for twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics
We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. The carbon, nitrogen, phosphorus, and water budgets presented here are used to calibrate the MEL model prior to running the climate change simulations. Citations and calculations for the data presented here are described in the individual site html files included in this dataset.
Ecosystem responses to changes in climate and carbon dioxide in twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics
We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. This dataset consists of the MEL model Windows executable, the driver and parameter file for each site, and the output files for each of the six simulations listed above.
Phenology of flowers and leaves following experimental warming in the initiation and maturation years for 7 understory boreal plants at the Bonanza Creek Long Term Ecological Research (BNZ LTER) site in Interior Alaska: 2017-2019
This dataset contains the results of experimental warming of flower and leaf buds for 7 understory boreal plants: Rhododendron groenlandicum, Rosa acicularis, Rubus chamaemorus, Shepherdia canadensis, Viburnum edule, Vaccinium uliginosum, and Vaccinium vitis-idaea. Plants in two cohorts were subjected to one of four treatments: warming in the initiation year (the year prior to flowering or leaf-out) only, warming in the maturation year (the year of flowering or leaf-out) only, warming in both years, or no warming (controls). The timing of flowering (both cohorts) and leaf-out (usually one cohort) was monitored. We also tracked developmental stages of the flower bud primordia using repeated desctructive sampling followed scanning electron microscopy throughout the initiation years. Environmental data associated with the plots, including air temperature throughout the summer, soil temperature and depth of ground thaw in late May, and canopy cover, are also reported.
Neurothreads: development of supportive carriers for mature dopaminergic neuron differentiation and implantation
<p>Raw data for the publication:</p> <p><strong>Neurothreads: development of supportive carriers for mature dopaminergic neuron differentiation and implantation</strong></p>
Data from: Structure and dynamics of secondary and mature rainforests: insights from South Asian long-term monitoring plots
<p><strong>1) DESCRIPTION </strong></p> <p>The dataset contains annual woody stems (shrubs and trees) census data collected from two long-term ecological monitoring plots spanning one hectare each in the Anamalai Hills of the Southern Western Ghats, India. These two plots represent one situated in a mature forest located within relatively undisturbed rainforest of the Anamalai Tiger Reserve (ATR) and one in secondary forest on the Valparai Plateau, respectively. Both plots have been censused and measured from 2017 to 2022 following the standardized protocol (RAINFOR-GEM, Marthews et al. 2014).</p> <p><br><strong>2) CONTACTS</strong></p> <p>CONTACT #1<br>1. Name: Akhil Murali<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 82812 97441<br>4. Email address: akhil@ncf-india.org<br>5. ORCID: 0000-0001-6149-6458</p> <p>CONTACT #2<br>1. Name: Srinivasan Kasinathan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: srini@ncf-india.org<br>5. ORCID: 0000-0001-7323-6653 </p> <p>CONTACT #3<br>1. Name: Kshama Bhat<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: kshama@ncf-india.org<br>5. ORCID: 000-0002-6190-2687</p> <p>CONTACT #4 <br>1. Name: Jayashree Ratnam <br>2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India<br>3. Work Phone: +91 80 23666001 <br>4. Email address: jratnam@ncbs.res.in <br>5. ORCID: 0000-0002-6568-8374</p> <p>CONTACT #5<br>1. Name: Mahesh Sankaran <br>2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India<br>3. Work Phone: +91 80 23666001<br>4. Email address: mahesh@ncbs.res.in <br>5. ORCID: 0000-0002-1661-6542</p> <p>CONTACT #6<br>1. Name: Divya Mudappa<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org<br>5. ORCID: 0000-0001-9708-4826</p> <p>CONTACT #7<br>1. Name: T. R. Shankar Raman<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org<br>5. ORCID: 0000-0002-1347-3953</p> <p>CONTACT #8<br>1. Name: Anand M Osuri <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: aosuri@ncf-india.org <br>5. ORCID: 0000-0001-9909-5633</p> <p><br><strong>3) GEOGRAPHIC COVERAGE and SITE DESCRIPTION</strong></p> <p>a) Site type: : Tropical Forest<br>b) Geography: : Anamalai Tiger Reserve, Southern Western Ghats.<br>c) Habit: : Mid elevation Wet evergreen Forest<br>d) Site History: : </p> <p>i) MANAMBOLI- The Mature Forest plot (10.357748° N, 76.889747° E; 825 m asl) is situated within a relatively undisturbed 200-hectare mid-elevation tropical wet evergreen rainforest tract at the core of the Anamalai Tiger Reserve (ATR). This area has been protected from logging and other significant disturbances since its establishment as a protected area in 1979.</p> <p>ii) CANDURA- The Secondary Forest plot (10.30855411° N, 76.83391853° E; 875 m asl) is situated within a 124-hectare rainforest remnant on the Valparai Plateau: the Candura rainforest remnant. The Candura site experienced episodic selective logging in the 1990s and early 2000s, with the last logging episode occurring in 2004. In the early 2000s, the understorey of the remnant was cleared for Vanilla (Vanilla planifolia) cultivation in the central and southern parts (abandoned in 2007), robusta coffee (Coffea canephora) in the northwestern corner (abandoned in the early 2000s), and pepper in 21 hectares in the northeastern part (established in 2015, abandoned in 2021).</p> <p>Climate: Humid tropical with about 2400 mm rainfall annually, falling mainly during the southwest monsoon.</p> <p><br><strong>4) TEMPORAL COVERAGE</strong></p> <p>a) Begins: 2017-11-30 (Year, Month, Day)<br>b) Ends: 2022-11-12 (Year, Month, Day)</p> <p><br>5) SAMPLING DESIGN AND METHODS </p> <p>a) Plot Design: Each 1 ha plot of 100 m × 100 m, sub-divided into 100 continuous sub-plots of 10 m × 10 m, was surveyed and mapped to maximum accuracy using a theodolite in the field, with grid corners permanently staked. <br>b) Data collection period and frequency: After the plot establishment in NOvember -- December 2017, the plots were recensused each year (around November). <br>c) Research Methods: All woody plant individuals with girth at breast height (GBH, at 1.3 m) ≥10 cm were tagged with numbered aluminum tags and spatially mapped. Plant species were identified using standard floral keys. Stem GBH was measured for all single stemmed individuals. For trees with buttresses, the GBH point of measurement (POM) was taken at 50 cm above the buttresses or at the height where the stem is regular. New saplings that recruited into the ≥10 cm GBH class were identified, mapped, tagged, and added to the monitoring. Stems that appeared to be dead were recorded at each monitoring and those that showed no signs of recovery in subsequent visits were recorded as mortality.</p> <p><br><strong>6) FILES INCLUDED</strong></p> <p>The dataset includes the following 9 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)</p> <p>01_README.txt<br>Metadata (this file) including information on the dataset explaining associated files and their contents.</p> <p>02_Candura_annual_census.csv <br>This contains the Annual census data with the following column headings: <br>site: Site name (Can = Candura)<br>cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022)<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ht1: Point of measurement at 1.3 m above the ground or 50 cm above the top of the highest buttress or stilt root (POM1)<br>c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97)<br>g1: Stem girth at POM1 (in centimetre)<br>ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached<br>c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97)<br>g2: Girth at POM2 (in centimetre)<br>dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No)<br>da: alive status of stem (d-dead,a-alive)<br>remarks: remarks or notes</p> <p>03_Manamboly_annual_census.csv<br>This contains Annual census data with the following column headings: <br>site: Site name (Man = Manamboli)<br>cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022)<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ht1: Point of measurement at 1.3 m above the ground or 50 cm above the top of the highest buttress or stilt root (POM1)<br>c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97)<br>g1: Stem girth at POM1 (in centimetre)<br>ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached<br>c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97)<br>g2: Girth at POM2 (in centimetre)<br>dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No)<br>da: alive status of stem (d-dead,a-alive)<br>remarks: remarks or notes</p> <p>04_Candura_vernier.csv<br>This file has the girth measurement of trees with lianas where digital vernier calipers were used to measure stem diameter since it was not possible to measure stem girth using measuring tape.<br>site: Site name (Can = Candura)<br>cno: Census Number<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>vern1_d1 First measure of diameter at POM1 (in millimetre) <br>vern2_d1 Second measure of diameter at POM1 (in millimetre) <br>vern3_d1 Third measure of diameter at POM1 (in millimetre) <br>calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>vern1_d2 First measure of diameter at POM2 (in millimetre) <br>vern2_d2 Second measure of diameter at POM2 (in millimetre) <br>vern3_d2 Third measure of diameter at POM2 (in millimetre) <br>calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>Remarks Remarks and notes</p> <p>05_Manamboli_vernier.csv<br>This file has the girth measurement of trees with lianas where digital vernier calipers were used to measure stem diameter since it was not possible to measure stem girth using measuring tape.<br>site: Site name (Man = Manamboli)<br>cno: Census Number<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>vern1_d1 First measure of diameter at POM1 (in millimetre) <br>vern2_d1 Second measure of diameter at POM1 (in millimetre) <br>vern3_d1 Third measure of diameter at POM1 (in millimetre) <br>calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>vern1_d2 First measure of diameter at POM2 (in millimetre) <br>vern2_d2 Second measure of diameter at POM2 (in millimetre) <br>vern3_d2 Third measure of diameter at POM2 (in millimetre) <br>calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>Remarks Remarks and notes</p> <p>06_Candura_Height_data.csv<br>This contains data on the heights of individual trees in plot as measured in 2018.<br>site: Site name (Can = Candura)<br>ymd: Date in YYYY/MM/DD format (Year Month Day)<br>gno: Grid Number: <br>tno: unique tag number: <br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes: <br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>height: Height of tree in metres<br>remarks: Remarks: and notes</p> <p>07_Manamboli_Height_data.csv<br>This contains data on the heights of individual trees in plot as measured in 2018.<br>site: Site name (Man = Manamboli)<br>ymd: Date in YYYY-MM-DD format (Year Month Day)<br>gno: Grid Number: <br>tno: unique tag number: <br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes: <br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>height: Height of tree in metres<br>remarks: Remarks: and notes</p> <p>08_Species_name_match.csv<br>This file provides the combined list of species codes updated taxonomy and successional guild. Scientific names were updated to current taxonomy using the species name matching tool of the Global Biodiversity Information Facility, GBIF (www.gbif.org).<br>sps: Species codes<br>query : Scientific name of the plant at the time of data collection: <br>scientificName: : with auther citation: <br>key: GBIF key<br>rank: Taxonomic rank or level of identification (GENUS, SPECIES)<br>kingdom: Taxonomic Kingdom (plants) provided by GBIF name matching tool: <br>phylum: Taxonomic Phylum provided by GBIF name matching tool<br>class: Taxonomic Class provided by GBIF name matching tool<br>order: Taxonomic Order provided by GBIF name matching tool<br>family: Taxonomic Family provided by GBIF name matching tool<br>genus: Taxonomic Genus provided by GBIF name matching tool<br>botanical_name: Updated scientific name of the species provided by GBIF name matching tool<br>habt_new: Successional guild of the species (Mature = mature forest species; Secondary = secondary successional species; Int - Introduced species)</p> <p>09_R_scrpt_for_manuscript.R<br>Text file with code in the R statistical and programming environment (www.r-project.org).</p> <p><br><strong>Reference</strong><br>Marthews TR, Riutta T, Oliveras Menor I, Urrutia R, Moore S, Metcalfe D, Malhi Y, Phillips O, Huaraca Huasco W, Ruiz Jaén M, Girardin C, Butt N, Cain R and colleagues from the RAINFOR and GEM networks (2014). Measuring Tropical Forest Carbon Allocation and Cycling: A RAINFOR-GEM Field Manual for Intensive Census Plots (v3.0). Manual, Global Ecosystems Monitoring network, http: //gem.tropicalforests.ox.ac.uk/.</p> <p> </p>
Catalogues of Semantic Artefacts - Maturity Dimensions and Features
<p>This dataset contains, in three different formats (XSLX, CSV, and PDF), a description of twelve maturity dimensions identified from the literature that can be used to measure the maturity of the semantic artefacts catalogues (SAC). For each dimension, a number from 2 to 6 features has been added, for a total of 43 features overall.</p>
Movies of mouse oocyte maturation in transmitted light
<p>This dataset has been presented in our paper "An interpretable and versatile machine learning approach for oocyte phenotyping", in bioRxiv.</p> <p>It contains 466 movies of mouse oocytes maturation acquired in transmitted light every 3 min. Spatial resolution is 0.227 µm/pixel.</p>
Neurodevelopmental Patterns of Early Postnatal White Matter Maturation Represent Distinct Underlying Microstructure and Histology
<p>This dataset includes:</p> <ol> <li>T2w Template from the dHCP datasets.</li> <li>4D weekly average maps from the dHCP study: i) average T2w; ii) average T2w/T1w signal ratio; iii) average neurite density index [from NODDI]; iv) average free water map [from NODDI].</li> <li>NMF results - NeWMaPs from the dHCP study across multiple resolutions (ranging from 2-20 NMFs).</li> </ol> <p> </p>
Mass spectrometry raw data for "Proteomics reveals substantial differences between in vitro matured abattoir-derived and in vivo matured oocytes in cattle"
<p><em><span>In vitro</span></em><span> production (IVP) of bovine embryos still has its limitations such as low blastocyst rate and lower embryo quality, resulting in lower pregnancy rates following the transfer of IVP embryos compared to <em>in vivo</em> produced embryos. </span><span>Given these differences in developmental competence, RNA sequencing and microarray technology have been applied to describe the differences in transcriptional activity between <em>in vitro</em> and <em>in vivo</em> produced embryos. All but one of these studies solely utilized oocytes obtained from slaughterhouse material for the <em>in vitro</em> production of embryos, thereby introducing the possibility, that differences between IVP and <em>in vivo</em> embryos are in part attributable to differing sources of oocytes. The aim of the present study was therefore to compare the proteome of oocytes retrieved from slaughterhouse material, with and without a period of <em>in vitro</em> maturation and <em>in vivo</em> matured oocytes obtained from donor cattle following superovulation. <span>For each group the protein pattern of four biological replicates containing ten oocytes each were analyzed via SWATH<sup>TM</sup>-MS.</span></span></p>
PARN and TOE1 constitute a 3′ end maturation module for nuclear non-coding RNAs
<p>HeLa cells were cultured in DMEM (Welgene) supplemented with 9% fetal bovine serum (Welgene). HeLa cells were transfected with 20 nM of siRNAs for four days using Lipofectamine 3000 (Thermo Fisher Scientific). Equal amounts of four different siRNAs were used for each knockdown. In the combinatorial knockdown, we mixed multiple siRNA pools to have a final concentration of 20 nM per siRNA pool. Total RNAs were extracted from siRNA-transfected HeLa cells using TRIzol reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol and treated with DNase I (Takara). mTAIL-seq libraries were prepared as previously described (Lim et al., 2016). Amplified cDNA libraries were sequenced on an Illumina MiSeq platform with 50% of the PhiX control library (Illumina).</p> <p>The uploaded file includes both intensity and sequence information for spike-ins and libraries used in the mTAIL-seq analysis. These data can be processed with Tailseeker 3.1.7 (Chang, 2017) according to the standard workflow of the software. The source codes and container images are available from Zenodo (https://zenodo.org/record/887547; doi:10.5281/zenodo.887546).</p>
Stable carbon and oxygen isotopes in tree rings and basal area increment from mature temperate forests within the AmeriFlux network.
Data were used to investigate long-term changes in tree intrinsic water use efficiency (iWUE, i.e., the ratio between CO2 assimilation and stomatal conductance) and the underlying physiological mechanisms. We used delta18O to estimate the 18O enrichment in leaf water above the source water, Delta18OLW. Moreover we assessed the relationship between isotope-derived parameters and atmospheric CO2 (ca) and climate factors. Isotope-related parameters included in the dataset are: alpha-cellulose delta13C, carbon isotope discrimination (Delta13C), intercellular CO2 concentration (ci) and the ratio of intercellular to atmospheric CO2 concentrations (ci/ca), alpha-cellulose delta18O, estimated delta18O in precipitation (see Method), oxygen isotope discrimination above the source water (Delta18O). The dataset includes also the following climate parameters: growing season temperature (Tgrs), precipitation (Pgrs) and vapor pressure deficit (VPDgrs) and mean annual temperature (Ta), precipitation (Pa) and vapor pressure deficit (VPDa), and standard precipitation-evaporation index relative to August, with 3 months lag (SPEI8_3) from the global database. Finally, we also include the ca values that were used to calculate delta13C, iWUE and ci/ca. All the equations used to calculate the isotope-derived parameters, including the leaf water Delta18O (see Figure 3 in Guerrieri et al. 2019 PNAS) are also provided.
EJR01 Foraging decisions underlying restricted space-use: effects of fire and forage maturation on large herbivore nutrient uptake on Konza Prairie
Recent models suggest that herbivores optimize nutrient intake by selecting patches of low to intermediate vegetation biomass. We assessed the application of this hypothesis to plains bison (Bison bison) in an experimental grassland managed with fire by estimating daily rates of nutrient intake in relation to grass biomass and by measuring patch selection in experimental watersheds in which grass biomass was manipulated by prescribed burning. Digestible crude protein content of grass declined linearly with increasing biomass, and the mean digestible protein content relative to grass biomass was greater in burned watersheds than watersheds not burned that spring (intercept; F1,251 = 50.57, P < 0.0001). Linking these values to published functional response parameters, ad libitum protein intake, and protein expenditure parameters, Fryxell's (Am. Nat., 1991, 138, 478) model predicted that the daily rate of protein intake should be highest when bison feed in grasslands with 400 - 600 kg/ha. In burned grassland sites, where bison spend most of their time, availability of grass biomass ranged between 40 and 3650 kg/ha, bison selected foraging areas of roughly 690 kg/ha, close to the value for protein intake maximization predicted by the model. The seasonal net protein intake predicted for large grazers in this study suggest feeding in burned grassland can be more beneficial for nutrient uptake relative to unburned grassland as long as grass regrowth is possible. Foraging site selection for grass patches of low to intermediate biomass help explain patterns of uniform space use reported previously for large grazers in fire-prone systems. This data set was used to test the forage maturation hypothesis in the Konza Prairie bison enclosure from 2012-2013. Our objectives were to quantify foraging site selection of Plains bison in order to determine if bison in a fire-prone grassland selected sites of low-to-intermediate forage biomass as posited by Fryxell’s (1991) forage mat
ECOBREED WP2 T2.1 Winter common wheat (Triticum aestivum) - Late maturity group
<p>Description of the winter common wheat (Triticum aestivum) late maturity group nursery. Tested within T2.1 in Germany (by Secobra), Czech Republic (by Selgen) and Slovakia (by NPPC) in 2019/2020.</p>
Data for the paper Lemoine, Gmel, Foster, Marmet, Studer (2020). Multiple trajectories of alcohol use and the development of alcohol use disorder: do Swiss men mature-out of problematic alcohol use during emerging adulthood? Plos One. https://doi.org/10.1371/journal.pone.0220232
<p>Data for the paper Lemoine, Gmel, Foster, Marmet, Studer (2020). Multiple trajectories of alcohol use and the development of alcohol use disorder: do Swiss men mature-out of problematic alcohol use during emerging adulthood?</p> <p>Plos One. <a href="https://doi.org/10.1371/journal.pone.0220232">https://doi.org/10.1371/journal.pone.0220232</a></p> <p>Please refer to the paper for further information about the data.</p> <p> </p> <p>The dataset contains all data needed to reproduce the results in the above cited paper. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the paper.</p> <p>The dataset contains data for three waves that was collected between September 2010 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch/">www.c-surf.ch</a>). Participants were on average 20 years old at wave 1, 21 at wave 2 and 25 at wave 3 when they answered the questionnaires. The final sample size used in the paper is 4746 after excluding those that did not reply to a questionnaire or to a variable of interest for the main analysis.</p> <p>The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493).</p>
scRNA-seq data for article: Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis
<p>Single-cell RNA-seq dataset from sorted CD11bInt, F4/80Hi, CD64+ mouse liver cells in naive or Leishmania infantum-infected animals at 42 d.p.i.. Data analyses and results are described in manuscript: "Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis". Data files are Seurat objects in RDS format. Filtered-out potential doublets, low quality cells and dying cells (excluded cells with <1000 genes detected, cells with >6000 genes detected, cells with mitochondrial gene expression > 10% and cells with <5000 transcript molecules). Data normalization, scaling and integration performed using Seurat.</p> <p>Filtered dataset containing all KCs and macrophages is in the "pessenda_KC_Macro_seurat" file.</p> <p>Our data were then mapped onto a reference dataset published by Remmerie et al. (DOI: 10.1016/j.immuni.2020.08.004) for annotation consistent with the literature. The reference mapped object can be found in the "pessenda_refmap_KC_Macro_seurat" file.</p> <p>Dataset containing the additional analysis of CLEC4F-TIM4+ FACS-sorted KCs can be found in the "pessenda_refmap_KCTimPos_seurat" file.</p>
Dataset: maturity of transparency of open data ecosystems in 22 smart cities
<p>This dataset contains data collected during a study "<a href="https://www.sciencedirect.com/science/article/pii/S2210670722002281?casa_token=8xHhtKug0xEAAAAA:POKIQswXhPdbwqgi5A8q98xitcUju_VS8T7oSP6YujXdABZlc5bNn4vEHzHGoxoW16mT6hA-HZ4#!">Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities</a>" (Sustainable Cities and Society (SCS), vol.82, 103906) conducted by Martin Lnenicka (University of Pardubice), Anastasija Nikiforova (University of Tartu), Mariusz Luterek (University of Warsaw), Otmane Azeroual (German Centre for Higher Education Research and Science Studies), Dandison Ukpabi (University of Jyväskylä), Visvaldis Valtenbergs (University of Latvia), Renata Machova (University of Pardubice).</p> <p>This study inspects smart cities’ data portals and assesses their compliance with transparency requirements for open (government) data by means of the expert assessment of 34 portals representing 22 smart cities, with 36 features.</p> <p>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities.</p> <p>***Purpose of the expert assessment***<br> The data in this dataset were collected in the result of the applying the developed benchmarking framework for assessing the compliance of open (government) data portals with the principles of transparency-by-design proposed by Lněnička and Nikiforova (2021)* to 34 portals that can be considered to be part of open data ecosystems in smart cities, thereby carrying out their assessment by experts in 36 features context, which allows to rank them and discuss their maturity levels and (4) based on the results of the assessment, defining the components and unique models that form the open data ecosystem in the smart city context.</p> <p>***Methodology***<br> Sample selection: the capitals of the Member States of the European Union and countries of the European Economic Area were selected to ensure a more coherent political and legal framework. They were mapped/cross-referenced with their rank in 5 smart city rankings: IESE Cities in Motion Index, Top 50 smart city governments (SCG), IMD smart city index (SCI), global cities index (GCI), and sustainable cities index (SCI). A purposive sampling method and systematic search for portals was then carried out to identify relevant websites for each city using two complementary techniques: browsing and searching.<br> To evaluate the transparency maturity of data ecosystems in smart cities, we have used the transparency-by-design framework (<a href="https://www.sciencedirect.com/science/article/pii/S0736585321000447?casa_token=7K8YGcYWbQcAAAAA:_HnV50rvxwQmDYyTjLYCmUkhDM2Qpsu8TPPBgOxajkV6ammJ1BBwgtQEnMMZdVk5ONxrGNY8hOw">Lněnička & Nikiforova, 2021</a>)*.<br> The benchmarking supposes the collection of quantitative data, which makes this task an acceptability task. A six-point Likert scale was applied for evaluating the portals. Each sub-dimension was supplied with its description to ensure the common understanding, a drop-down list to select the level at which the respondent (dis)agree, and a comment to be provided, which has not been mandatory. This formed a protocol to be fulfilled on every portal. Each sub-dimension/feature was assessed using a six-point Likert scale, where strong agreement is assessed with 6 points, while strong disagreement is represented by 1 point.<br> Each website (portal) was evaluated by experts, where a person is considered to be an expert if a person works with open (government) data and data portals daily, i.e., it is the key part of their job, which can be public officials, researchers, and independent organizations. In other words, compliance with the expert profile according to the International Certification of Digital Literacy (ICDL) and its derivation proposed in <a href="https://www.emerald.com/insight/content/doi/10.1108/OIR-05-2020-0204/full/html?casa_token=6Yd7zSiQMg0AAAAA:yT8d_thrh84stDSVbax8eXLm5vP9LkrwZZFMzC_vql9vZNoQP_iYBHCZ0NOndkvusIx9TZAvJLWBp6lqe9bymm-xHaZ93k2mYfoHXKdVq52A0a7MlwGa">Lněnička et al. (2021)</a>* is expected to be met.<br> When all individual protocols were collected, mean values and standard deviations (SD) were calculated, and if statistical contradictions/inconsistencies were found, reassessment took place to ensure individual consistency and interrater reliability among experts’ answers.<br> *<a href="https://www.sciencedirect.com/science/article/pii/S0736585321000447?casa_token=7K8YGcYWbQcAAAAA:_HnV50rvxwQmDYyTjLYCmUkhDM2Qpsu8TPPBgOxajkV6ammJ1BBwgtQEnMMZdVk5ONxrGNY8hOw">Lnenicka, M., & Nikiforova, A. (2021). Transparency-by-design: What is the role of open data portals?. Telematics and Informatics, 61, 101605</a><br> *<a href="https://www.emerald.com/insight/content/doi/10.1108/OIR-05-2020-0204/full/html?casa_token=6Yd7zSiQMg0AAAAA:yT8d_thrh84stDSVbax8eXLm5vP9LkrwZZFMzC_vql9vZNoQP_iYBHCZ0NOndkvusIx9TZAvJLWBp6lqe9bymm-xHaZ93k2mYfoHXKdVq52A0a7MlwGa">Lněnička, M., Machova, R., Volejníková, J., Linhartová, V., Knezackova, R., & Hub, M. (2021). Enhancing transparency through open government data: the case of data portals and their features and capabilities. Online Information Review.</a></p> <p>***Test procedure***<br> (1) perform an assessment of each dimension using sub-dimensions, mapping out the achievement of each indicator<br> (2) all sub-dimensions in one dimension are aggregated, and then the average value is calculated based on the number of sub-dimensions – the resulting average stands for a dimension value - eight values per portal<br> (3) the average value from all dimensions are calculated and then mapped to the maturity level – this value of each portal is also used to rank the portals.</p> <p>***Description of the data in this data set***<br> Sheet#1 "comparison_overall" provides results by portal<br> Sheet#2 "comparison_category" provides results by portal and category<br> Sheet#3 "category_subcategory" provides list of categories and its elements<br> </p> <p>***Format of the file***<br> .xls</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p>For more info, see README.txt</p>
Privacy-by-Design Maturity Model: literature review, coding, model creation and evaluation
<p>Results from two multivocal literature reviews (MLRs) and subsequent coding, formulation of capabilities and dependencies, creation of maturity matrix and evaluation results. Used in the creation of a PbD domain model and extraction of core activities for PbD in Information Systems design. Part of the <a href="https://www.privacymaturity.org/" target="_blank" rel="noopener">Privacy-by-Design Maturity</a> research project by the <a href="https://www.uu.nl/en/research/ai-labs/ai-lab-for-the-public-services" target="_blank" rel="noopener">AI Lab for Public Services</a>.</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.