Find research datasets worth reusing

opencc-by-3.0-usJul 2021View details →

GDC-PANCAN.htseq_counts and associated meta-data no longer available from gdc.xenahubs.net

The RNA-seq data and associated meta-data I used for my publications, downloaded from gdc.xenahubs.net but no longer available in this form (i.e. raw counts) from the website.

opencc-by-4.0Mar 2023View details →

Supplementary data for "Influence of prey availability on habitat selection during the non-breeding period in a resident bird of prey"

Abstract Background: For resident birds of prey in the temperate zone, the cold non-breeding period can have strong impacts on survival and reproduction with implications for population dynamics. Therefore, the non-breeding period should receive the same attention as other parts of the annual life cycle. Birds of prey in intensively managed agricultural areas are repeatedly confronted with unpredictable, rapid changes in their habitat due to agricultural practices such as mowing, harvesting, and ploughing. Such a dynamic landscape likely affects prey distribution and availability and may even result in changes in habitat selection of the predator throughout the annual cycle. Methods:  In the present study, we 1) quantified barn owl prey availability in different habitats across the annual cycle, 2) quantified the size and location of barn owl breeding and non-breeding home ranges using GPS-data, 3) assessed habitat selection in relation to prey availability during the non-breeding period, and 4) discussed differences in habitat selection during the non-breeding period to habitat selection during the breeding period. Results: The patchier prey distribution during the non-breeding period compared to the breeding period led to habitat selection towards grassland during the non-breeding period. The size of barn owl home ranges during breeding and non-breeding  were similar, but there was a small shift in home range location which was more pronounced in females than males. The changes in prey availability led to a mainly grassland-oriented habitat selection during the non-breeding period. Further, our results showed the importance of biodiversity promotion areas and undisturbed field margins within the intensively managed agricultural landscape.  Conclusions: We showed that different prey availability in habitat categories can lead to changes in habitat preference between the breeding and the non-breeding period. Given these results we show how important it is to maintain and enhance structural diversity in intensive agricultural landscapes, to effectively protect birds of prey specialised on small mammals. Hereafter we provide the datasets and R script to reproduce the resource selection functions.

opencc-by-4.0Apr 2023View details →

NHS England COVID-19 Exposure Notification App and Test Availability Data

This dataset was scraped from the API serving the NHS COVID-19 App for England and Wales, and the NHS COVID-19 test availability service. It contains the following files: exposure_keys.csv Metadata associated with the published exposure keys for the Bluetooth Google/Apple Exposure Notification (GAEN) system. The actual broadcast keys were not collected, only the metadata attached to them. The columns in the table match those in the <a href="https://developers.google.com/android/exposure-notifications/exposure-key-file-format">exposure key export format</a>, with the exception of the "export_date" field which is the "end_timestamp" of the key export in which that key was first seen. This file is believed to be complete between 2020-09-13 and 2023-04-29 when the NHS COVID-19 app was retired. exposure_configuration.csv This table contains the NHS COVID-19 App's exposure configuration JSON file fetched from the API, with a new record inserted whenever this changed. History of this file is also available in the <a href="https://github.com/ukhsa-collaboration/covid19-app-system-public">app's git repository</a>, and entries in this file from before 2021-07-11 were imported from there. The timestamps for entries dated since that point will match the time that the configuration was published to the API, which may not be the case for the git repository as this was normally updated after a delay. risky_venues.csv "Risky venue" notification data for the COVID-19 App. This was an NHS App-specific feature, not part of the GAEN specification, which allowed users to "check in" to a venue and receive a notification if they were present at the same time as someone who subsequently tested positive for COVID-19. This file is believed to be complete between 2020-09-24 and 2022-02-22 (when it appears the "risky venue" feature was retired), with a known data collection gap between 2021-08-03 and 2021-08-06. walk_in_pcr_availability.csv Walk-in PCR test availability for the entire UK, used by the NHS PCR test booking service. This contains JSON objects provided by the API, which are broken down by region. A new row was inserted whenever this JSON object changed. This file is believed to be complete between 2021-12-27 and 2022-03-30 (after which it appears the online test booking service was retired). home_test_availability.csv Home test (PCR or Lateral Flow Device) availability, for the public and for "key workers" who had priority ordering PCR tests. A new row was inserted whenever the availability changed. This file is believed to be complete between 2021-12-27 and 2022-08-22 when the data ended.   The final version of the source code used to fetch this data is <a href="https://doi.org/10.5281/zenodo.7883754">available here</a>.

opencc-by-4.0Jun 2023View details →

Unveiling metastable ensembles of GRB2 and the relevance of interdomain communication during folding - available data.

The folding process of multidomain proteins is a highly intricate phenomenon involving the assembly of distinct domains into a functional three-dimensional structure. During this process, each domain may fold independently while interacting with other domains to form a functional protein. The folding of multidomain proteins can be influenced by various factors, including the composition and structure of each domain or the presence of disordered linker regions, as well as the surrounding environment. Misfolding of multidomain proteins can lead to the formation of non-functional structures associated with a range of diseases, including cancers and neurodegenerative disorders. Understanding this process is an essential step for many biophysical analyzes, such as stability, interaction, malfunctioning, and rational drug design. One such multidomain protein is the growth factor receptor-bound protein 2 (GRB2), an adaptor protein essential in regulating cell survival. GRB2 consists of one central Src Homology 2 (SH2) domain flanked by two Src Homology 3 (SH3) domains. The SH2 domain interacts with phosphotyrosine regions in other proteins, while the SH3 domains recognize proline-rich regions on protein partners during cell signaling. In this study, we combined computational and experimental techniques to investigate the folding process of GRB2. We sampled the conformational space through computational simulations and mapped the mechanisms involved by calculating free energy profiles, indicating possible intermediate states. From the molecular dynamics and trajectories, we used the Energy Landscape Visualization Method (ELViM), which allowed us to visualize a three-dimensional representation of the overall energy surface. We identified two possible parallel folding routes that cannot be seen in a one-dimensional analysis, with one occurring more frequently during folding. Supporting these results, we used DSC and fluorescence spectroscopy techniques to confirm these intermediate states in vitro. Finally, we analyzed the deletion of domains to compare our model outputs with previously published results, supporting the presence of interdomain modulation. Overall, our study highlights the significance of interdomain communication within the GRB2 protein and its impact on the formation, stability, and structural plasticity, which are crucial for its interaction with other proteins in key signaling pathways.

opencc-by-4.0Aug 2023View details →

Dataset for "Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data"

This dataset contains the MESWA (Middle East and Southwest Asia) seismic model and auxiliary data used in the creation of the model (Rodgers, 2023).  MESWA is a three-dimensional model of the seismic properties of crust and upper mantle of the Middle East and Southwest Asia.  The MESWA model is provided in NetCDF format (readable by for example, xarray, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with ParaView (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with Salvus (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>).    Also included are the earthquake source parameters for all 327 Global Centroid Moment Tensor events considered in this study in ASCII text format. Also included are lists of the selected 192 inversion events and 66 validation events in ASCII text format.  Lastly, we include a list of all receivers used in the creation and validation of MESWA.  This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).   The following table provides a listing of the files in the dataset: <table> <tbody> <tr> <td> File </td> <td> Description </td> </tr> <tr> <td> MESWA.nc </td> <td> MESWA model in NetCDF format </td> </tr> <tr> <td> MESWA.h5 </td> <td> MESWA model in HDF5 format, used by Salvus </td> </tr> <tr> <td> MESWA.xmdf </td> <td> Auxiliary file for MESWA.h5, used to import model into Paraview </td> </tr> <tr> <td> events_project.csv </td> <td> Table of event source parameters for all 327 events considered in the project </td> </tr> <tr> <td> inversion_events_192.csv </td> <td> Table of 192 inversion events  (ASCII comma separated value) </td> </tr> <tr> <td> validation_events_66.csv </td> <td> Table of 66 validation events  (ASCII comma separated value) </td> </tr> <tr> <td> events_receivers_inversion.csv </td> <td> Table of waveform (event-receiver-channel) data used in the inversion (ASCII comma separated value) </td> </tr> <tr> <td> events_receivers_validation.csv </td> <td> Table of waveform (event-receiver-channel) data used in the validation (ASCII comma separated value) </td> </tr> </tbody> </table>     References Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, Geophys. J. Int., 216(3), 1675–1692, doi: 10.1093/gji/ggy469   Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. The Visualization Handbook, 717(8). <a href="https://doi.org/10.1016/b978-012387582-2/50038-1">https://doi.org/10.1016/b978-012387582-2/50038-1</a>   Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1). <a href="https://doi.org/10.5334/jors.148">https://doi.org/10.5334/jors.148</a>   Rodgers, A. (2023). Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data, technical report, LLNL-TR- 851939.   Acknowledgements This project was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008 and the National Nuclear Security Administration.  This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-852402

Effects of permafrost thaw on nitrogen availability and plant-soil interactions in a boreal Alaskan lowland: I - Percent Cover Data 2013

This dataset examines shifts in plant community structure along a lowland boreal permafrost thaw chronosequence. Data was collected in 2013 at the APEX Beta and forested study plots, located adjacent to the BNZ experimental forest. Percent cover data was collected for subcanopy vascular and nonvascualr vegetation in July 2013.

Effects of permafrost thaw on nitrogen availability and plant-soil interactions in a boreal Alaskan lowland: II - Nitrogen Data 2013

This dataset examines shifts in extractable soil pore water chemistry along a lowland boreal permafrost thaw chronosequence. Data was collected in 2013 at the APEX Beta and forested study plots, located adjacent to the BNZ experimental forest. Measured parameters include: dissolved inorganic N (DIN), dissolved organic N (DON), free amino acids, total dissolved N (TDN), soil temp. at 10 cm, seasonal ice depth, and volumetric soil moisture at 10 cm. Soil pore water samples and environmental variables were collected every three to four weeks from late June to late September, 2013 for a total of five sampling events.

Effects of permafrost thaw on nitrogen availability and plant-soil interactions in a boreal Alaskan lowland: IV - Plant vs Nitrogen Data 2013

This dataset examines shifts in soil and plant characteristics, and extractable soil pore water chemistry along a lowland boreal permafrost thaw chronosequence. Data was collected in 2013 at the APEX Beta and forested study plots, located adjacent to the BNZ experimental forest. Data includes mean DIN, DON, free amino acids, and TDN from seasonal pore water measurements as well as aboveground vegetation C and N concentrations, and summertime, litterfall % N, and resorption efficiency for C. calyculata.

Effects of permafrost thaw on nitrogen availability and plant-soil interactions in a boreal Alaskan lowland: V - Isotape Data 2013

This dataset examines shifts in foliar del N 15 concentations for three plant species found across a lowland boreal permafrost thaw chronosequence. Data was collected in 2013 at the APEX Beta and forested study plots, located adjacent to the BNZ experimental forest.

openCC (other)Oct 2021View details →

Data to support "delta13C and delta15N values from a mesocosm experiment showing sea urchins mediate the availability of kelp detritus to benthic consumers"

The purple sea urchin Strongylocentrotus purpuratus is an important herbivore and detritivore in southern California giant kelp (Macrocystis pyrifera) forests that grow on shallow rocky reefs (~3-20m depth) off southern California, including Santa Barbara where this study was done. To investigate the shredder activity of purple urchins, we assembled communities of common detritivores and suspension feeders from local reefs in mesocosms. Three species of brittle stars (Ophiopteris papillosa, Ophioplocus esmarki, and Ophiothrix spiculata), one vermetid gastropod (Thylacodes squamigerus), two barnacles (Chthamalus sp. and Megabalanus californicus), one polychaete worm (Chaetopterus sp.), and three sea cucumbers (Cucumaria piperata, Pachythyone rubra, and Cucumaria salma) were collected from the seafloor of local kelp forests at 5-15m depth in the Santa Barbara Channel. Ten of each species were placed in each of six 50 L (51x38x27 cm) flow-through unfiltered seawater tanks, each containing two concrete bricks for hard substrate on top of 3 cm of sand. Because sea urchins are highly mobile, capable of bulldozing other occupants, we isolated them above the experimental communities on plastic mesh (1cm). Mesh dividers were secured horizontally across each mesocosm 16 cm above the bottom. Ten individuals of each consumer species, along with 2-3 blades, totaling 47.3 g (±1.2 SE), of isotopically labeled kelp (see below) were placed on the floor of each tank, below the divider. On top of the mesh divider, we placed an additional 229.8 g (±6.1 SE) of enriched kelp blades, and in half of the tanks, 10 adult urchins (total mass 343.3 g ±1.3 SE) per tank. The kelp below the mesh ensured that the detritivores had direct access to degrading kelp detritus regardless of the urchin treatment, similar to the situation in the kelp forest, allowing us to more clearly ascertain the degree to which the detritivores were dependent on urchins for kelp detritus assimilation. The experiment wa

opencc-by-4.0Jan 2020View details →

Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.

supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146

opencc-by-4.0Jul 2020View details →

Endocrine disruption: the noise in available data adversely impact the models' performance

This paper is devoted to the analysis of available experimental data and preparation of predictive models for binding affinity of molecules with respect to two nuclear receptors involved in endocrine disruption: the Estrogen (ER) and the Androgen (AR) receptor. The ED-relevant data were retrieved from multiple sources, including the CERAP, CoMPARA, and the Tox21 data challenge projects as well as ChEMBL and PubChem databases. Data analysis performed with the help of Generative Topographic Mapping technique revealed the problem of a low agreement between experimental values issued from different sources. Collected data were used to train both classification models for AR and ER binding activities and regression models for Relative Binding Affinity (RBA) and median Inhibition Concentration (IC50) models. These models displayed relatively poor performance in classification (sensitivities ER = 0.34, AR = 0.49) and in regression (determination coefficient R2 for the RBA and IC50 models in external validation varied from 0.44 to 0.76). Our analysis demonstrates that low models performances resulted from misinterpreted experimental endpoints or wrongly reported values. Developed models and collected data sets included of 6215 (ER) and 3789 (AR) unique compounds; they are freely available. The repository includes data on estrogen and androgen receptor binding behavior (binder, non-binder), median inhibitory concentration (IC50) and relative binding affinity (RBA).  SDF fields: <ul> <li>DB = database; where: COMPARA = Collaborative Modelling Project for Androgen Receptor Activity; CERAPP = Collaborative Estrogen Receptor Activity Prediction Project; Tox-DC = data from Tox21 program; PubChem = data from PubChem. </li> <li>Set = whether the compound was used in training or test set for the given model</li> <li>Receptor = AR stands for Androgen Receptor and ER stands for Estrogen Receptor</li> <li>binding_prp = binding behaviour for the classification model (ER and AR). 1 = binder;  = non-binder</li> <li>IC50 (nM) and logIC50 = median inhibitory concentration values in nanoMolar and log.</li> <li>RBA(%) and logRBA = relative binding affinity values in % and log.</li> </ul>

opencc-by-4.0May 2023View details →

Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.

Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. <ul><li>SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).</li></ul>

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Estimating historic N- and S-deposition with publicly available data – An example from Central Germany

The deposition of reactive nitrogen and sulphur has profound effects on ecosystem functioning. In the last decades, monitoring networks providing high resolution spatio-temporal deposition estimates have been set up, but equivalent information on historic deposition is mostly missing. However, understanding vegetation change and mitigate future loss of biodiversity and ecosystem functioning is only possible evaluating the effects of its strongest drivers, which includes deposition in many ecosystems. Here, we combine different data sources to provide estimates of historic deposition in forested ecosystems on a high spatio-temporal scale for a federal state in Central Germany from 1880 to present. We make use of data from field measurement stations together with elevation and precipitation data from the last three decades to build a simple deposition model, validate this model with a model publicly available covering the time range from 2000 to present, and extrapolate deposition from this joint model to the past using European deposition trends from the last 150 years. Our approach can easily be adapted to other data and spatial areas shows how to use raw deposition data together with publicly available data on elevation and precipitation to construct simple deposition models covering recent and historic times in areas and for times for which no data are available.

opencc-zeroNov 2021View details →

opencc-by-4.0May 2022View details →

Datasets from "Circulating miRNA and Lung Cancer: - a More Comprehensive Analysis of Available Data"

A collection of datasets on miRNA and lung cancer used in Berg, O.F.B.: Circulating miRNA and Lung Cancer: - a More Comprehensive Analysis of Available Data. NTNU Open (2022)   The datasets in this collection are processed and normalized from available raw datasets. The processing code that was used can be found on <a href="https://github.com/OleFredrik1/masterthesis">https://github.com/OleFredrik1/masterthesis</a>. The raw datasets are: Asakura2020: Asakura, K., Kadota, T., Matsuzaki, J., Yoshida, Y., Yamamoto, Y., Nakagawa, K., Takizawa, S., Aoki, Y., Nakamura, E., Miura, J., Sakamoto, H., Kato, K., Watanabe, S.-i., and Ochiya, T. (2020). A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy. Communications Biology, 3(1):1–9. Accession ID: GSE137140 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140</a>   Bianchi2011: Bianchi, F., Nicassio, F., Marzi, M., Belloni, E., Dall’Olio, V., Bernard, L., Pelosi, G., Maisonneuve, P., Veronesi, G., and Di Fiore, P. P. (2011). A serum circulating miRNA diagnostic test to identify asymptomatic high-risk individuals with early stage lung cancer. EMBO Molecular Medicine, 3(8):495–503. Link: <a href="https://www.embopress.org/action/downloadSupplement?doi=10.1002%2Femmm.201100154&file=emmm_201100154_sm_suppdata2.xls">https://www.embopress.org/action/downloadSupplement?doi=10.1002%2Femmm.201100154&file=emmm_201100154_sm_suppdata2.xls</a>   Chen2019: Accession ID: GSE71661 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71661">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71661</a>   Duan2021: Duan, X., Qiao, S., Li, D., Li, S., Zheng, Z., Wang, Q., and Zhu, X. (2021). Circulating miRNAs in Serum as Biomarkers for Early Diagnosis of Non-small Cell Lung Cancer. Frontiers in Genetics, 12:987. Accession ID: GSE137140 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140</a>   Fehlmann2020: Fehlmann, T., Kahraman, M., Ludwig, N., Backes, C., Galata, V., Keller, V., Geffers, L., Mercaldo, N., Hornung, D., Weis, T., Kayvanpour, E., Abu-Halima, M., Deuschle, C., Schulte, C., Suenkel, U., von Thaler, A.-K., Maetzler, W., Herr, C., Fähndrich, S., Vogelmeier, C., Guimaraes, P., Hecksteden, A., Meyer, T., Metzger, F., Diener, C., Deutscher, S., Abdul-Khaliq, H., Stehle, I., Haeusler, S., Meiser, A., Groesdonk, H. V., Volk, T., Lenhof, H.-P., Katus, H., Balling, R., Meder, B., Kruger, R., Huwer, H., Bals, R., Meese, E., and Keller, A. (2020). Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients. JAMA oncology, 6(5):714–723. Accession ID: E-MTAB-8026 Link: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8026/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8026/</a>   Halvorsen2016: Halvorsen, A. R., Bjaanæs, M., LeBlanc, M., Holm, A. M., Bolstad, N., Rubio, L., Peñalver, J. C., Cervera, J., Mojarrieta, J. C., López-Guerrero, J. A., Brustugun, O. T., and Helland, Å. (2016). A unique set of 6 circulating microRNAs for early detection of non-small cell lung cancer. Oncotarget, 7(24):37250–37259. Accession ID: GSE70080 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70080">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70080</a>   Jin2017: Jin, X., Chen, Y., Chen, H., Fei, S., Chen, D., Cai, X., Liu, L., Lin, B., Su, H., Zhao, L., Su, M., Pan, H., Shen, L., Xie, D., and Xie, C. (2017). Evaluation of Tumor-Derived Exosomal miRNA as Potential Diagnostic Biomarkers for Early-Stage Non–Small Cell Lung Cancer Using Next-Generation Sequencing. Clinical Cancer Research, 23(17):5311–5319. Link: <a href="https://aacrjournals.org/clincancerres/article/23/17/5311/123048/Evaluation-of-Tumor-Derived-Exosomal-miRNA-as">https://aacrjournals.org/clincancerres/article/23/17/5311/123048/Evaluation-of-Tumor-Derived-Exosomal-miRNA-as</a> (table s1)   Keller2009: Keller, A., Leidinger, P., Borries, A., Wendschlag, A., Wucherpfennig, F., Scheffler, M., Huwer, H., Lenhof, H.-P., and Meese, E. (2009). miRNAs in lung cancer - Studying complex fingerprints in patient’s blood cells by microarray experiments. BMC Cancer, 9(1):353. Accession ID: GSE17681 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17681">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17681</a>   Keller2014: Keller, A., Leidinger, P., Vogel, B., Backes, C., ElSharawy, A., Galata, V., Mueller, S. C., Marquart, S., Schrauder, M. G., Strick, R., Bauer, A., Wischhusen, J., Beier, M., Kohlhaas, J., Katus, H. A., Hoheisel, J., Franke, A., Meder, B., and Meese, E. (2014). miRNAs can be generally associated with human pathologies as exemplified for miR-144*. BMC Medicine, 12(1):224. Accession ID: GSE61741 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61741">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61741</a>   Keller2020: Keller, A., Fehlmann, T., Backes, C., Kern, F., Gislefoss, R., Langseth, H., Rounge, T. B., Ludwig, N., and Meese, E. (2020). Competitive learning suggests circulating miRNA profiles for cancers decades prior to diagnosis. RNA Biology, 17(10):1416–1426. Link: <a href="https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1771945">https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1771945</a> (Supplemental Table 9)   Kryczka2021: Kryczka, J., Migdalska-Sęk, M., Kordiak, J., Kiszałkiewicz, J. M., PastuszakLewandoska, D., Antczak, A., and Brzeziańska-Lasota, E. (2021). Serum Extracellular Vesicle-Derived miRNAs in Patients with Non-Small Cell Lung Cancer—Search for Non-Invasive Diagnostic Biomarkers. Diagnostics, 11(3):425. Link: <a href="https://www.mdpi.com/2075-4418/11/3/425/s1">https://www.mdpi.com/2075-4418/11/3/425/s1</a>   Leidinger2011: Leidinger, P., Keller, A., Borries, A., Huwer, H., Rohling, M., Huebers, J., Lenhof, H.-P., and Meese, E. (2011). Specific peripheral miRNA profiles for distinguishing lung cancer from COPD. Lung Cancer, 74(1):41–47. Accession ID: GSE24709 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24709">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24709</a>   Leidinger2014: Leidinger, P., Backes, C., Dahmke, I. N., Galata, V., Huwer, H., Stehle, I., Bals, R., Keller, A., and Meese, E. (2014). What makes a blood cell based miRNA expression pattern disease specific? - A miRNome analysis of blood cell subsets in lung cancer patients and healthy controls. Oncotarget, 5(19):9484–9497. Accession ID: GSE55993 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55993">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55993</a>   Leidinger2016: Leidinger, P., Brefort, T., Backes, C., Krapp, M., Galata, V., Beier, M., Kohlhaas, J., Huwer, H., Meese, E., and Keller, A. (2016). High-throughput qRT-PCR validation of blood microRNAs in non-small cell lung cancer. Oncotarget, 7(4):4611–4623. Link: <a href="https://www.oncotarget.com/article/6566/text/">https://www.oncotarget.com/article/6566/text/</a> (supplementary files)   Li2017: Li, L.-L., Qu, L.-L., Fu, H.-J., Zheng, X.-F., Tang, C.-H., Li, X.-Y., Chen, J., Wang, W.-X., Yang, S.-X., Wang, L., Zhao, G.-H., Lv, P.-P., Zhang, M., Lei, Y.-Y., Qin, H.-F., Wang, H., Gao, H.-J., and Liu, X.-Q. (2017). Circulating microRNAs as novel biomarkers of ALK-positive non-small cell lung cancer and predictors of response to crizotinib therapy. Oncotarget, 8(28):45399–45414. Accession ID: GSE94536 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94536">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94536</a>   Marzi2016: Marzi, M. J., Montani, F., Carletti, R. M., Dezi, F., Dama, E., Bonizzi, G., Sandri, M. T., Rampinelli, C., Bellomi, M., Maisonneuve, P., Spaggiari, L., Veronesi, G., Bianchi, F., Di Fiore, P. P., and Nicassio, F. (2016). Optimization and Standardization of Circulating MicroRNA Detection for Clinical Application: The miR-Test Case. Clinical Chemistry, 62(5):743–754 Accession ID: GSE76462 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76462">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76462</a>   Nigita2018: Nigita, G., Distefano, R., Veneziano, D., Romano, G., Rahman, M., Wang, K., Pass, H., Croce, C. M., Acunzo, M., and Nana-Sinkam, P. (2018). Tissue and exosomal miRNA editing in Non-Small Cell Lung Cancer. Scientific Reports, 8(1):10222. Accession ID: GSE114711 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114711">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114711</a>   Patnaik2012: Patnaik, S. K., Yendamuri, S., Kannisto, E., Kucharczuk, J. C., Singhal, S., and Vachani, A. (2012). MicroRNA Expression Profiles of Whole Blood in Lung Adenocarcinoma. PLOS ONE, 7(9):e46045. Accession ID: GSE27486 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27486">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27486</a>   Patnaik2017: Patnaik, S. K., Kannisto, E. D., Mallick, R., Vachani, A., and Yendamuri, S. (2017). Whole blood microRNA expression may not be useful for screening non-small cell lung cancer. PLOS ONE, 12(7):e0181926. Accession ID: GSE40738 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40738">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40738</a>   Qu2017: Qu, L., Li, L., Zheng, X., Fu, H., Tang, C., Qin, H., Li, X., Wang, H., Li, J., Wang, W., Yang, S., Wang, L., Zhao, G., Lv, P., Lei, Y., Zhang, M., Gao, H., Song, S., and Liu, X. (2017). Circulating plasma microRNAs as potential markers to identify EGFR mutation status and to monitor epidermal growth factor receptor-tyrosine kinase inhibitor treatment in patients with advanced non-small cell lung cancer. Oncotarget, 8(28):45807–45824. Accession ID: GSE93300 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93300">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93300</a>   Reis2020: Reis, P. P., Drigo, S. A., Carvalho, R. F., Lopez Lapa, R. M., Felix, T. F., Patel, D., Cheng, D., Pintilie, M., Liu, G., and Tsao, M.-S. (2020). Circulating miR-16-5p, miR-92a-3p, and miR-451a in Plasma from Lung Cancer Patients: Potential Application in Early Detection and a Regulatory Role in Tumorigenesis Pathways. Cancers, 12(8):2071. Accession ID: GSE152702 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152702">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152702</a>   Wozniak2015: Wozniak, M. B., Scelo, G., Muller, D. C., Mukeria, A., Zaridze, D., and Brennan, P. (2015). Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer. PLOS ONE, 10(5):e0125026. Accession ID: GSE64591 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64591">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64591</a>   Yao2019: Yao, B., Qu, S., Hu, R., Gao, W., Jin, S., Liu, M., and Zhao, Q. (2019). A panel of miRNAs derived from plasma extracellular vesicles as novel diagnostic biomarkers of lung adenocarcinoma. FEBS Open Bio, 9(12):2149–2158. Accession ID: GSE111803 Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111803">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111803</a>   Zaporozhchenko2018: Zaporozhchenko, I. A., Morozkin, E. S., Ponomaryova, A. A., Rykova, E. Y., Cherdyntseva, N. V., Zheravin, A. A., Pashkovskaya, O. A., Pokushalov, E. A., Vlassov, V. V., and Laktionov, P. P. (2018). Profiling of 179 miRNA Expression in Blood Plasma of Lung Cancer Patients and Cancer-Free Individuals. Scientific Reports, 8(1):6348. Accession ID: E-MTAB-6304 Link: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6304/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6304/</a>   The Abdollahi2019 and Boeri2011 datasets are not included as I recived them by email and I did not recieve conformation that they were OK with me publishing the datasets.

opencc-by-4.0Jul 2022View details →

Analysis of scholarly repositories' availability. Data and notebooks.

These datasets and companion Jupyter notebooks supplement the publication "Knock knock! Who's there?'' A study on scholarly repositories' availability" accepted at TPDL 2022, Padova, Italy.

opencc-by-4.0Dec 2019View details →

Рис. 7. Морские двустворчатые моллюски иЗ раскопа 1 поселениЯ Константиновка-1: A–M – Glycymeris (Glycymeris) yessoensis (Sowerby III, 1889) (A, B – данные не расшифрованы, длина раковины 44.6 мм; C, D – данные не расшифрованы, длина раковины 38.7 мм; E, F – раскоп 5, пл. 6, кв. Б-6, длина раковины 30.7 мм; G, H –?подъемный материал, длина раковины 33.8 мм; I, J – раскоп 3, кв. З-6, длина раковины 40.4 мм; K–M – раскоп 2, пл. 7, кв. Д-6, длина раковины 23.5 мм; N, O – Mya (Arenomya) japonica Jay, 1857 – подъемный материал, длина раковины 61.7 мм. Fig. 7. Marine bivalves from excavation 1 of the Konstantinovka-1 site: A–M – Glycymeris (Glycymeris) yessoensis (Sowerby III, 1889) (A, B – data not available, shell length 44.6 mm; C, D – data not available, shell length 38.7 mm; E, F – excavation 5, layer 6, square Б-6, shell lenth 30.7 mm; G, H –?surface scatter, shell length 33.8 mm; I, J – excavation 3, square З-6, shell length 40.4 mm; K–M – excavation 2, layer 7, square Д-6, shell length 23.5 mm; N, O – Mya (Arenomya) japonica Jay, 1857 – surface scatter, shell length 61.7 mm. in Mollusks from the archaeological site Konstantinovka-1 in Primorye (Russian Far East)

Рис. 7. Морские двустворчатые моллюски иЗ раскопа 1 поселениЯ Константиновка-1: A–M – Glycymeris (Glycymeris) yessoensis (Sowerby III, 1889) (A, B – данные не расшифрованы, длина раковины 44.6 мм; C, D – данные не расшифрованы, длина раковины 38.7 мм; E, F – раскоп 5, пл. 6, кв. Б-6, длина раковины 30.7 мм; G, H –?подъемный материал, длина раковины 33.8 мм; I, J – раскоп 3, кв. З-6, длина раковины 40.4 мм; K–M – раскоп 2, пл. 7, кв. Д-6, длина раковины 23.5 мм; N, O – Mya (Arenomya) japonica Jay, 1857 – подъемный материал, длина раковины 61.7 мм. Fig. 7. Marine bivalves from excavation 1 of the Konstantinovka-1 site: A–M – Glycymeris (Glycymeris) yessoensis (Sowerby III, 1889) (A, B – data not available, shell length 44.6 mm; C, D – data not available, shell length 38.7 mm; E, F – excavation 5, layer 6, square Б-6, shell lenth 30.7 mm; G, H –?surface scatter, shell length 33.8 mm; I, J – excavation 3, square З-6, shell length 40.4 mm; K–M – excavation 2, layer 7, square Д-6, shell length 23.5 mm; N, O – Mya (Arenomya) japonica Jay, 1857 – surface scatter, shell length 61.7 mm.

opencc-by-4.0Jun 2024View details →

Auxiliary data files for replication of "Augmenting the availability of historical GDP per capita estimates through machine learning"

This repository holds auxiliary data files needed for the replication "Augmenting the availability of historical GDP per capita estimates through machine learning". All further information and data is provided in the <a href="https://github.com/philmkoch/historicalGDPpc" target="_blank" rel="noopener">GitHub repository</a>. The data included in this auxiliary folder is based on the work by Laouenan et al. (https://www.nature.com/articles/s41597-022-01369-4).

opencc-by-nc-sa-4.0Oct 2018View details →

Green Roofs Footprints for New York City, Assembled from Available Data and Remote Sensing

Summary: The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md</a>). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016. These data are associated with an existing GitHub Repository, <a href="https://github.com/tnc-ny-science/NYC_GreenRoofMapping">https://github.com/tnc-ny-science/NYC_GreenRoofMapping</a>, and as needed and appropriate pending future work, versioned updates will be released here. Terms of Use: The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data. Associated Files: As of this release, the specific files included here are: <ul> <li>GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).</li> <li>GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li>GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li>GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.</li> </ul> Column Information for the datasets: Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below. <ul> <li>fid - Unique identifier</li> <li>bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: <a href="https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh">https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh</a>. Associated metadata for fields from that dataset are available at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md</a>.</li> <li>bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (<a href="http://gis.nyc.gov/taxmap/map.htm">http://gis.nyc.gov/taxmap/map.htm</a>) and PLUTO/MapPLUTO (<a href="https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page">https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page</a>). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.</li> <li>gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li>bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li>prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).</li> <li>cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.</li> <li>doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.</li> <li>heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.</li> <li>feat_code - Code describing the type of building, pulled from the Building Footprint Data.</li> <li>groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.</li> <li>qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'</li> <li>notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.</li> <li>classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)</li> <li>digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)</li> <li>newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)</li> <li>original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.</li> <li>address - Address based on MapPLUTO, joined to the dataset based on bbl.</li> <li>borough - Borough abbreviation pulled from MapPLUTO.</li> <li>ownertype - Owner type field pulled from MapPLUTO.</li> <li>zonedist1 - Zoning District 1 type pulled from MapPLUTO.</li> <li>spdist1 - Special District 1 pulled from MapPLUTO.</li> <li>bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)</li> </ul> For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263): <ul> <li>xcoord - Longitude in decimal degrees.</li> <li>ycoord - Latitude in decimal degrees.</li> </ul> Acknowledgements: This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.