Find research datasets worth reusing

openGEO-OpenMar 2022View details →

Local euchromatin enrichment in lamina-associated domains anticipates their re-positioning in the adipogenic lineage

GEO Series GSE185066. Homo sapiens. 82 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenDec 2012View details →

Rapid and synchronous clearance of PcG histone modifications from Hox genes anticipates motor neuron differentiation

GEO Series GSE19450. Mus musculus. 33 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Genome binding/occupancy profiling by genome tiling array.

openGEO-OpenJul 2025View details →

Circadian clocks and periodic anticipated fasting prevent fasting-associated hepatic steatosis in calorie restriction

GEO Series GSE278669. Mus musculus. 38 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJun 2024View details →

Bacteria can anticipate and adaptively respond to the seasons

GEO Series GSE252562. Synechococcus elongatus PCC 7942 = FACHB-805. 36 samples. Type: Expression profiling by high throughput sequencing.

opencc-by-4.0Apr 2020View details →

One Thing Leads to Another: Anticipating Visual Object Identity Based on Associative-Memory Templates

Probabilistic associations between stimuli afford memory templates that guide perception through proactive anticipatory mechanisms. The current work isolates the behavioral benefits and electrophysiological signatures of memory-guided iden- tity-based anticipation, while equating anticipation of space, time, motor responses, and task relevance. Our results show that anticipation of the specific identity of a forthcoming percept impacts performance and is associated with states of attenuated alpha oscillations and the contingent negative variation, extending previous work implicating these neural substrates in spa- tial and temporal preparatory attention. Together, this work bridges fields of attention, memory, and perception, providing new insights into the neural mechanisms that support complex attentional templates.

opencc-by-4.0Oct 2020View details →

Anticipating Identification of Technical Debt Items in Model-Driven Software Projects

Model-driven development (MDD) and Technical Debt (TD) are software engineering approaches that look for promoting quality on systems under development. MDD uses high-level abstraction models that can be transformed into application code, potentially improving system understanding and maintainability. TD, on the other hand, promotes quality through the use of strategies for detecting, quantifying, monitoring, and correcting software development problems that may hinder its maintenance and evolution. Most research on TD focuses on the application code as primary TD sources. In an MDD project, however, dealing with technical debt only on the source code may not be an adequate strategy because MDD projects should focus their software building efforts on models instead of source code. Besides, in MDD projects, code generation is often done at a later stage than creating models, then dealing with TD only in source code can lead to unnecessary interest payments due to unmanaged debts, such as model and source codes artifacts desynchronization. The use of TD concept in an MDD context is also known as Model-Driven Technical Debt (MDTD). Recent works concluded that MDD project codes are not technical debt free, making it necessary to investigate the possibility and benefits of applying TD identification techniques in earlier stages of the development process, such as in modeling phases. This paper intends to analyze whether it is possible to use source code technical debt detection strategies to identify TD on code-generating models in the context of model-driven development projects. A catalog of nine different model technical debt items for platform-independent code-generating models was specified. Each catalog item provides a detection strategy to automatically find elements suspected to incur the corresponding TD type in the models. An evaluation was performed in order to observe the effectiveness of the proposed catalog compared to existing source code identification techniques found in the literature. Through three different open source software projects, more than 78 thousand lines of code were investigated. Results revealed that, although the catalog items present different precision rates, it is possible to identify and deal with these model-driven technical debts before source code is generated. We hope that sharing this initial version provides future contributions and improvements for this catalog.

opencc-by-4.0Aug 2017View details →

Gut microbiome in ADHD and its relation to neural reward anticipation (PLOS ONE, 2017)

fMRI group data belonging to publication Gut microbiome in ADHD and its relation to neural reward anticipation (PLOS ONE, 2017)

opencc-by-4.0May 2024View details →

Data from: Island biodiversity in peril: anticipating a loss of mammals' functional diversity with future species extinctions

Islands are biodiversity hotspots that host unique assemblages. However, a substantial proportion of island species are threatened and their long-term survival is uncertain. Identifying and preserving vulnerable species has become a priority, but it is also essential to combine this information with other facets of biodiversity like functional diversity, to understand how future extinctions might affect ecosystem stability and functioning. Focusing on mammals, we (i) assessed how much functional space would be lost if threatened species go extinct, (ii) determined the minimum number of extinctions which would cause a significant functional loss, (iii) identified the characteristics (e.g., biotic, climatic, geographic, or orographic) of the islands most vulnerable to future changes in the functional space, and (iv) quantified how much of that potential functional loss would be offset by introduced species. Using trait information for 1,474 mammal species occurring in 318 islands worldwide, we built trait probability density functions to quantify changes in functional richness and functional redundancy in each island if the mammals categorized by IUCN as threatened disappeared. We found that the extinction of threatened mammals would reduce the functional space in 63% of the assessed islands, although these extinctions in general would cause a reduction of less than 15% of their overall functional space. Also, on most islands, the extinction of just a few species would be sufficient to cause a significant loss of functional diversity. The potential functional loss would be higher on small, isolated and/or species rich islands and, in general, the functional space lost would not be offset by introduced species. Our results show that the preservation of native species and their ecological roles remains crucial for maintaining the current functioning of island ecosystems. Therefore, conservation measures considering functional diversity are imperative to safeguard the unique functional roles of threatened mammal species on islands. Datasets and R scripts provided:  1. Mammals_occurrence_islands.xlxs - Matrix of presence/absence of mammals species (columns) per island (rows) and bibliographic sources from which the information has been extracted. Islands are grouped according to the zoogeographical regions proposed by Holt el al. (2013). 2. Traits_matrix.csv – Matrix of species functional traits included in this study and the bibliographic sources of this information. Note that trait values are scaled and centered and that some of them have been imputed (see Methods section of the original manuscript). 3. Functional_space_analysis.R – script to: ·       Build the functional space of islands ·       Calculate observed functional richness and functional redundancy ·       Calculate functional richness and functional redundancy after simulating the extinction of threatened species ·       Calculate the functional space offset by introduced species      4. mixed_models_islands.R - R script to perform the linear mixed models to explore whether islands with higher values of predicted functional diversity loss due to threatened species extinction share some characteristics. 5. data_models_islands.csv - Dataset used to perform the models. For each island the following information is provided: <ul> <ul> <li>Island ID (ID)</li> <li>Number of species (SppRich)</li> <li>Number of threatened species (Thre_sp)</li> <li>Functional richness (Island_FRic)</li> <li>Functional redundancy (Island_Red)</li> <li>Functional richness standard effect size (SES_FRic)</li> <li>Functional redundancy standard effect size (SES_FRed)</li> <li>Island group (Archipielago)</li> <li>Island past connectivity (Type)</li> <li>Percentage of protected area coverage (protected_percentageI_VI)</li> <li>Distance to the nearest continent (dContinent_km)</li> <li>Mean annual temperature (Anntemp_promedio)</li> <li>Mean annual precipitation (Annprec_promedio)</li> <li>Island area (Area_km2)</li> <li>Maximum elevation (Elev_max)</li> <li>Species richness (SR)</li> <li>Mean human footprint (Human_foot)</li> <li>Distance to the nearest larger landmass (distance_biggerLandmass)</li> </ul> </ul> Island area, distance to the nearest continent and distance to the nearest larger landmass were calculated with ArcMap (ESRI, 2019) and the ‘terra 1.7-71’ R package (Hijmans, 2023), using the shapefile of the world’s islands available in Martin et al. (2022). Distance to the nearest mainland and to the nearest larger landmass were calculated as the shortest distance between coastlines (Weigelt & Kreft, 2013). Maximum elevation of each island was extracted from the Global Bathymetry and Elevation Database (Becker et al., 2009). We also used this database to access the bathymetry around the continents and islands and determine whether an island was connected to the mainland during the Last Glacial Maximum (about 20,000 years ago), assuming a sea level of 122 m below the present level (glacial maximum mainland connection; Weigelt, Jetz and Kreft, 2013). Averaged values of annual temperature and annual precipitation for each island were calculated using the climatic variables available in the CHELSA 2.1 database (Karger et al., 2018) at a resolution of 30 arc seconds. To calculate the percentage of protected area on each island, we gathered the protected surface’s shapefile from The World Database on Protected Areas (UNEP-WCMC & IUCN, 2022). Finally, we used the mean human footprint index from Human Footprint maps (see Venter et al., 2018). References Becker, J. J., Sandwell, D. T., Smith, W. H. F., Braud, J., Binder, B., Depner, J., Fabre, D., Factor, J., Ingalls, S., Kim, S.-H., Ladner, R., Marks, K., Nelson, S., Pharaoh, A., Trimmer, R., Von Rosenberg, J., Wallace, G., & Weatherall, P. (2009). Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Marine Geodesy, 32(4), 355–371. <a href="https://doi.org/10.1080/01490410903297766">https://doi.org/10.1080/01490410903297766</a> ESRI (2019). ArcGis for Desktop. Retrieved from https://desktop.arcgis.com/en/ Hijmans, R. (2023). terra: Spatial Data Analysis_. R package version 1.7-3, <https://CRAN.R-project.org/package=terra>. Holt, B. G., Lessard, J.-P., Borregaard, M. K., Fritz, S. A., Araújo, M. B., Dimitrov, D., Fabre, P.-H., Graham, C. H., Graves, G. R., Jønsson, K. A., Nogués-Bravo, D., Wang, Z., Whittaker, R. J., Fjeldså, J., & Rahbek, C. (2013). An update of Wallace’s zoogeographic regions of the world. Science, 339(6115), 74–78. https://doi.org/10.1126/science.1228282 Karger D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2018). Data from: Climatologies at high resolution for the earth's land surface areas [Dataset]. Dryad. https://doi.org/10.5061/dryad.kd1d4 Martin, M., Sayre, R., VanGraafeiland, K., McDermott Long, O., Weatherdon, L., Will, D., Spatz, D. R., & Holmes, N. D. (2020). Global Islands (M. I. Goldstein & D. A. B. T.-E. of the W. B. DellaSala (eds.); pp. 47–50). Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.12475-3 UNEP-WCMC & IUCN (2022). Protected Planet: The World Database on Protected Areas (WDPA). Cambridge, UK: UNEP-WCMC and IUCN. Retrieved from <a href="http://www.protectedplanet.net">www.protectedplanet.net</a>. [Accessed 12/2022] Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J., Jones, K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete, B. M., Levy, M. A., & Watson, J. E. (2018). Last of the Wild Project, Version 3 (LWP-3): 2009 Human Footprint, 2018 Release. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). <a href="https://doi.org/10.7927/H46T0JQ4">https://doi.org/10.7927/H46T0JQ4</a> Weigelt, P., & Kreft, H. (2013). Quantifying island isolation – insights from global patterns of insular plant species richness. Ecography, 36(4), 417-429. https://doi.org/10.1111/j.1600-0587.2012.07669. Weigelt, P., Jetz, W., & Kreft, H. (2013). Bioclimatic and physical characterization of the world’s islands. Proceedings of the National Academy of Sciences, 110(38), 15307–15312. https://doi.org/10.1073/pnas.1306309110

closedIPD-NOFeb 2026View details →

Anticipated and Perceived Benefits Following Hepatitis C Treatment

ClinicalTrials.gov study NCT03000023. IPD Sharing: NO. Countries: 1. Publications: 0.

ADPKD and Peritoneal Dialysis: How Anticipate Peritoneal Pressure?

ClinicalTrials.gov study NCT03970018. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Anticipation of the Difficult Airway: the Preoperative Airway Assessment Form

ClinicalTrials.gov study NCT01037374. IPD Sharing: Not stated. Countries: 1. Publications: 0.

The Role of Consumption and Anticipation in Dopamine Release to Food Reward

ClinicalTrials.gov study NCT03447561. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

Anticipating Decline and Providing Therapy

ClinicalTrials.gov study NCT06182995. IPD Sharing: NO. Countries: 1. Publications: 0.

Comparison of Video Laryngoscopy and Conventional Laryngoscopy for Safe Intubation in Adult Thyroid Surgery Patients With Anticipated Difficult Airway

ClinicalTrials.gov study NCT07113171. IPD Sharing: Not stated. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

The Impact of Age and Sex on Anticipated and Experienced Pain in First-Time Outpatient Flexible Cystoscopy

ClinicalTrials.gov study NCT07375641. IPD Sharing: NO. Countries: 1. Publications: 0.

Anticipated Personalization of the Management in Day Hospital Unit Based on a Collection of the PROs Via a Digital Tool

ClinicalTrials.gov study NCT05552066. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Emapalumab Treatment For Anticipated Clinical Benefit In Sepsis Driven By The Interferon-Gamma Endotype (The EMBRACE Trial)

ClinicalTrials.gov study NCT06694701. IPD Sharing: Not stated. Countries: 1. Publications: 0.

What is the Gold Standard of Airway Management in the Anticipated Difficult Airway

ClinicalTrials.gov study NCT04158323. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Action-effect Anticipation in Patients With Parkinson's Disease : A Study of the Sensory Attenuation Marker.

ClinicalTrials.gov study NCT02894333. IPD Sharing: Not stated. Countries: 1. Publications: 0.

A Multi-National Study In Bladder Cancer Patients to Detect Recurrences After TURB (Trans-urethral Resection of the Bladder) Earlier With the Xpert Bladder Cancer Monitor Assay (ANTICIPATE X)

ClinicalTrials.gov study NCT03664258. IPD Sharing: Not stated. Countries: 9. Publications: 0.