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Supplementary data files for JGR 2021JB022729R, Initial results from the Oman Drilling Project Multi-Borehole Observatory: Petrogenesis and ongoing alteration of mantle peridotite in the weathering horizon
<p>These are the four supplementary data files for a paper accepted in the Journal of Geophysical Research (JGR), "Initial results from the Oman Drilling Project Multi-Borehole Observatory: Petrogenesis and ongoing alteration of mantle peridotite in the weathering horizon", 2021JB022729R, which are being uploaded here in conformance with JGR's requirement that data be placed in an open archive. The date of publication is not yet known, but JGR requires that the data be uploaded when the paper is accepted, so we have used the date of acceptance, November 20, 2021.</p>
Covidiagnostix Project CSS Dataset
<p>The file contains the dataset produced for the Covidiagnostix project in "Casa Sollievo della Sofferenza" hospital, Italy. It includes the antibodies levels measured with ELISA technique before the vaccination, after 1 week, 21 days, 3 and 6 months over a population of 362 volunteers.</p>
Figure 2 in Taxonomy and distribution of Icelandic Fauveliopsidae (Annelida) collected during the BIOICE project
Figure 2. Values of temperature versus depth for BIOICE samples with Fauveliopsidae specimens. (a) Fauveliopsis olgae Hartman-Schröder, 1983. (b) Laubieriopsis brevis (Hartman, 1967). (c) Laubieriopsis cabiochi (Amoureux, 1982). Numbers as in Figure 1.
Figure 5 in Taxonomy and distribution of Icelandic Fauveliopsidae (Annelida) collected during the BIOICE project
Figure 5. Fauveliopsis olgae Hartmann-Schröder, 1983. SEM micrographs, BIOICE sample 2435 (IINH40690). (a) Complete specimen, left lateral view. (b) Detail of anterior region, left lateral view (framed in (a)). (c) Median region chaetigers, left lateral view (framed in (a)). (d) Bifid interramal papilla (framed in (c)). Abbreviations: A – acicular chaeta; a – thin acicular chaeta; AR – anterior body region; c – capillary chaeta; CH – chaetiger; ian – integumental annulations; irp – interramal papilla; isb – intersegmental border; MR – median body region; PR – posterior body region.
Figure 1 in Taxonomy and distribution of Icelandic Fauveliopsidae (Annelida) collected during the BIOICE project
Figure 1. Geographical distribution of Fauveliopsidae species found in BIOICE samples. (a) BIOICE samples with fauveliopsids. (b) Fauveliopsis olgae Hartman-Schröder, 1983. (c) Laubieriopsis brevis (Hartman, 1967). (d) Laubieriopsis cabiochi (Amoureux, 1982). (a): blue area marking position of samples with specimens of the three species; (b–d): red areas marking samples/group of samples with high number of specimens; numbers (1–4): marking samples/group of samples with high number of specimens (see also Figure 2).
Figure 8 in Taxonomy and distribution of Icelandic Fauveliopsidae (Annelida) collected during the BIOICE project
Figure 8. Laubieriopsis cabiochi (Amoureux, 1982). SEM micrographs, BIOICE samples 2712 ((a,b); IINH40859), 2282 ((c–e); IINH40828), 2717 ((f); IINH40863) and 2398 ((g); IINH40843). (a) Anterior body region, ventral view. (b) Anterior end, ventral view. (c) Anterior end, dorsal view. (d) Anterior end, dorsal view. (e) Chaetiger 3, detail. (f) Anterior end, ventral view. (d) Anterior end, dorsal view. (e) Chaetiger 3, detail (framed in (a)). (f) Anterior end, ventral view. (g) Chaetigers 1–3, ventral view. Abbreviations: A – acicular chaeta; a – thin acicular chaeta; CH – chaetiger; gp – genital papilla; imp – integumental micropapillae; irp – interramal papilla; isb – intersegmental border; mo – mouth; per – peristomium; pr – prostomium.
Figure 7 in Taxonomy and distribution of Icelandic Fauveliopsidae (Annelida) collected during the BIOICE project
Figure 7. Fauveliopsis olgae Hartmann-Schröder, 1983. SEM micrographs, BIOICE samples 2864 ((a–c); IINH40701) and 3501 ((d–f); IINH40711). (a) Complete specimen, dorsal view. (b) Anterior end, detail, dorsal view (framed in (a)). (c) Median region chaetigers, dorsal view (framed in (a)). (d) Posterior region chaetigers, lateral view. (e) Median region, two chaetigers, lateral view. (f) Acicular chaeta, scale covering. Abbreviations: A – acicular chaeta; AR – anterior body region; c – capillary chaeta; CH – chaetiger; gp – genital papilla; ian – integumental annulations; irp – interramal papilla; isb – intersegmental border; MR – median body region; PR – posterior body region.
Figure 4 in Taxonomy and distribution of Icelandic Fauveliopsidae (Annelida) collected during the BIOICE project
Figure 4. Stereomicrographs of several BIOICE specimens of Fauveliopsis olgae Hartmann-Schröder, 1983 removed from mollusc shell during sorting process (a,b), in foraminiferan tubular thecae (c,d), in gastropod (e) and scaphopod empty shells (f). BIOICE samples 3504 (IINH40713, (a–d)) and 2864 (IINH40702, (e–f)).
Figure 3 in Taxonomy and distribution of Icelandic Fauveliopsidae (Annelida) collected during the BIOICE project
Figure 3. Line drawings of BIOICE fauveliopsids. Fauveliopsis olgae Hartman-Schröder, 1983: (a) Complete specimen, dorsal view. (b) Anterior end, dorsal view. (c) Chaetiger 12, lateral view. Laubieriopsis brevis (Hartman, 1967): (d) Complete specimen, dorsal view. (e) Chaetiger 3, lateral view. (f) Chaetiger 9, lateral view (e,f), same scale bar. Laubieriopsis cabiochi (Amoureux, 1982): (g) Complete specimen, dorsal view. (h) Anterior end, dorsal view. BIOICE samples 2404 (IINH40686, (a– c)), 3072 (IINH40767, (d–f)) and 2716 (IINH40862, (g,h)). Abbreviations: A – acicular chaeta; a – thin acicular chaeta; AR – anterior body region; c – capillary chaeta; gp – genital papilla; irp – interramal papilla; MR – median body region; PR – posterior body region.
Figure 9 in Taxonomy and distribution of Icelandic Fauveliopsidae (Annelida) collected during the BIOICE project
Figure 9. Laubieriopsis cabiochi (Amoureux, 1982). SEM micrographs, BIOICE samples 2308 ((a); IINH40834), 2273 ((b,d); IINH40825), 2712 ((c); IINH40859); 2282 ((e); IINH40828); 2398 ((f); IINH40843) and 2272 ((g); IINH40823). (a) Anterior region, parapodium. (b) Anterior region, acicular chaeta showing bifid distal end and scale covering. (c) Chaetigers 8–9, chaetae and genital papilla. (d) Midbody parapodium, chaetae (inset, detail of scale cover). (e) Posterior body end, dorsal view. (f,g) Pygidium, laterodorsal view. Abbreviations: A – acicular chaeta; c – capillary chaeta; CH – chaetiger; gp – genital papilla; imp – integumental micropapillae; irp – interramal papilla; pp – pygidium papillae; pyg – pygidium.
Data from: Towards robust evolutionary inference with integral projection models
Integral projection models (IPMs) are extremely flexible tools for ecological and evolutionary inference. IPMs track the distribution of phenotype in populations through time, using functions describing phenotype-dependent development, inheritance, survival and fecundity. For evolutionary inference, two important features of any model are the ability to (i) characterize relationships among traits (including values of the same traits across ages) within individuals, and (ii) characterize similarity between individuals and their descendants. In IPM analyses, the former depends on regressions of observed trait values at each age on values at the previous age (development functions), and the latter on regressions of offspring values at birth on parent values as adults (inheritance functions). We show analytically that development functions, characterized this way, will typically underestimate covariances of trait values across ages, due to compounding of regression to the mean across projection steps. Similarly, we show that inheritance, characterized this way, is inconsistent with a modern understanding of inheritance, and underestimates the degree to which relatives are phenotypically similar. Additionally, we show that the use of a constant biometric inheritance function, particularly with a constant intercept, is incompatible with evolution. Consequently, current implementations of IPMs will predict little or no phenotypic evolution, purely as artefacts of their construction. We present alternative approaches to constructing development and inheritance functions, based on a quantitative genetic approach, and show analytically and through an empirical example on a population of bighorn sheep how they can potentially recover patterns that are critical to evolutionary inference.
Predicted HHV values for woody biomass samples from USDA-AFRI project using the best performing models.
<p>Predicted HHV values for samples from the USDA-AFRI project using the best-performing models from the cross-validation process.</p>
DetecDiv Demo Project files
<p>This demo project contains datasets that can be used to learn how to use the <a href="https://github.com/gcharvin/DetecDiv/">DetecDiv</a> software. </p> <p>It contains : </p> <ul> <li>Raw microscopy images provided as individual .tiff files ( brightfield and fluorescence image series)</li> <li>Region of interests (ROIs) processed by DetecDiv to perform various classification tasks</li> <li>3 different classification models that include all relevant information: <ul> <li>Classifier definition parameters</li> <li>Training procedure parameters</li> <li>A trained classifier</li> <li>A formatted ground truth dataset ready to perform training</li> <li>An unformatted training and test set (ROIs) used to manually annotate images before training the classifier</li> </ul> </li> </ul> <p>This demo project allows the user to test all the features of the graphical user interface of DetecDiv. </p> <p>For further details regarding the software and its applications, please refer to: </p> <p><a href="https://www.biorxiv.org/content/10.1101/2021.10.05.463175v2">DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis</a></p> <p>Théo Aspert, Didier Hentsch, Gilles Charvin</p> <p>https://doi.org/10.1101/2021.10.05.463175</p> <p> </p>
Summary Report of the GenX and PowerGenome runs for generating Price Series (for ARPA-E FLECCS Project)
<p>The present work describes the different sensitivity scenarios and simulated conditions under which we conduct the capacity expansion planning studies using GenX, a highly reconfigurable Mixed-Integer Linear Programming (MILP) based capacity-expansion planning model. In this set of simulations, we consider </p>
Influences of the South-to-North Water Diversion Eastern Route Project on water storage variations across the water-receiving area over recent decades
<p>This file contains (1) the time series of monthly GRACE data(including CSR,GFZ,JPL, and Weighted average)from April 2002 to December 2020 (11 months of data from July 2017 to May 2018 were missing between GRACE and GRACE-FO) , (2) water consumption of the Huaihe River Basin and Shandong Peninsula from 2002 to 2019, (3) diverted water from Jiangsu to Shandong in 2014-2020, (4) variation trend for the groundwater level data from each groundwater monitoring well from 2005 to 2018 and (5) water volume of the reservoir in the Shandong Province.</p>
[GAS 2022] Software Project Management Anti-Patterns for Video Game Development
<p>This dataset contains an analysis of 440 project management problems described in game postmortems from gamedeveloper.com (formerly Gamasutra). We grouped these problems into candidate anti-patterns, an then we link these candidates with anti-patterns documented in the Software Engineering literature. Specifically, we aim to answer the following research question how video game anti-pattern fits into the software anti-patterns literature? </p>
Static Analysis Warnings and Automatic Fixing: A Replication for C# Projects : Dataset
<p>Dataset used in the paper accepted for publication at the REproducibility Studies and NEgative Results (RENE) Track of SANER 2022.</p> <p>Martin Odermatt, Diego Marcilio, Carlo A. Furia. Static Analysis Warnings and Automatic Fixing: A Replication for C# Projects, International Conference on Software Analysis, Evolution, and Reengineering (SANER) 2022.</p>
Clinical Trial Project Management System (CTPMS)
<p>The main features of CTPMS include project approval management, to-do task list, project list, document management, contract management, initial meeting management, investigator management, and CRC management. Through the organic combination of these features, the CTI realizes the project approval review and daily management of study projects.</p>
Adjacency matrix (.csv) of wood industry project network
<p>Adjacency matrix (.csv) of wood industry project network exported from R code.</p>
Input data for the Resources for the Future Socioeconomic Projections (RFF-SPs)
<p>This repository contains large input data files for generating the RFF-SPs, and small accompanying files with data necessary for interpreting the larger files.</p> <p>--------------------------------------------------------------------------------<br> /MSW_GDP/<br> --------------------------------------------------------------------------------</p> <p>-MSW.mat is a MATLAB data file based on Müller, Stock, and Watson (forthcoming) containing two series:<br> 1) Year-by-sample draws "F-paths" representing frontier values of ln(GDP/capita) for the OECD, and <br> 2) Year-by-country-by-sample deviations from those F-paths, denoted as the U-path.</p> <p>A country's ln(GDP/capita) is equal to the sum of the sample's F-path and the country-sample's U-path. There are 418 years representing 1900-2317, 113 countries with names in country_names.txt, and 2000 sample trajectories. GDP/capita is measured in 2011$.</p> <p>-country_names.txt contain iso codes for the 113 countries in the order in which they appear in MSW.mat's U-path object. OECD countries are indicated with an "x" for reference.</p> <p>This information was transmitted via email from Ulrich Mueller (Princeton) to Kevin Rennert (RFF) on January 28, 2020, with a link to the file on Mueller's website at http://www.princeton.edu/~umueller/matlabJan2020.mat.</p> <p>--------------------------------------------------------------------------------<br> /Raftery population/<br> --------------------------------------------------------------------------------</p> <p>-pop_trajectories_probmig.csv contains country-by-year-by-sample (here, a sample is labelled a "Trajectory") population estimates based on Raftery and Ševčíková (2021). Population is in units of thousands of people, with 1000 samples. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on August 11, 2021.</p> <p>-death_rates.csv contains country-by-year-by-sample death rates in units of annual deaths per one thousand people, with 1000 samples corresponding to the sample order of samples in pop_trajectories_probmig.csv. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on October 7, 2021.</p> <p>-iso_key.csv contains a key mapping ISO alpha-3 country codes ("ISO3") to ISO numeric-3 country codes (denoted "NumericCode" in this file and "LocID" in pop_trajectories_probmig.csv). This data was acquired from a search on https://www.iso.org/obp/ui#search in 2021. The "country codes" box was checked and no keywords were used. The list was trimmed to the 184 countries included in the current version of the GIVE model.</p> <p>--------------------------------------------------------------------------------<br> Information<br> --------------------------------------------------------------------------------</p> <p>The probabilistic economic and emissions projections are from Müller, Stock, and Watson (forthcoming). </p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936. </p> <p>--------------------------------------------------------------------------------<br> References <br> --------------------------------------------------------------------------------</p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth </p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: https://www.sciencedirect.com/science/article/pii/S0169207021001394 </p>
ScienceDex guides
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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.