cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/q2k2-gm82
title: Topics modeling in computer science articles

description: By querying open data of notorious scientific databases via representational state transfers, and subsequently enforcing data management practices with a dynamic topic modeling approach on the referred metadata available, this work achieves a feasible form of article set analysis and classification. Research trends for a given field in specific moments are identified, and also the referred trends evolution throughout the years. It is then possible to detect the associated lexical variation overtime on published content, ultimately determining the so-called hot topics in arbitrary instants, including now. Three prominent scientific articles databases are probed by this work, they are arXiv, IEEExplore, and Springer Nature.The dataset contains:Identification of the articles used in the studyThe proportion of the topics in each documentNumber of articles per year per topicDistribution of the words that make up each topic
DOI: 10.5281/zenodo.3941809
title: Antibody Watch: Text Mining Antibody Specificityfrom the Literature

description: <strong>Abstract </strong> <strong>Motivation</strong> Antibodies are widely used experimental reagents to test expression of proteins. However, they might not always provide the intended tests because they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable and irreproducible research results. While many proposals have been developed to deal with the problem of antibody specificity, they may not scale well to deal with the millions of antibodies that have ever been designed and used in research. In this study, we investigate the feasibility of automatically extracting statements about antibody specificity reported in the literature by text mining, and generate reports to alert scientist users of problematic antibodies. <br> <strong>Results</strong> We developed a deep neural network system called Antibody Watch and tested its performance on a corpus of more than two thousand articles that report uses of antibodies. We leveraged the Research Resource Identifiers (RRID) to precisely identify antibodies mentioned in an input article and the BERT language model to classify if the antibodies are reported as nonspecific, and thus problematic, as well as inferred the coreference to link statements of specificity to the antibodies that the statements referred to. Our evaluation shows that Antibody Watch can accurately perform both classification and linking with F-scores over 0.8, given only thousands of annotated training examples. The result suggests that with more training, Antibody Watch will provide useful reports about antibody specificity to scientists.
DOI: 10.5281/zenodo.3941776
title: Col d'Olen, water chemistry (N-NO3) 2008-2019

description: Dataset provides information about soil temperature, soil C and N dynamics at different sites in the alpine tundra, along an elevational gradient from 2500 to 2900 m asl during the period 2005-2018. Moreover, in ordrer to evaluate how topsoil can influence the quality of surface water, lake water was sampled monthly at the same time that soil was sampled. The N-NO3 concentration is measured from 2008 to 2019 in water samples collected in triplicate in two lakes (Cimalegna and Bowditch). The ongoing studies aimed at investigating how the meteorological variables affect the biogeochemistry of seasonally snow covered areas, with a special focus on the effect of the snow cover duration on soil and water C and N dynamics during the growing season.
DOI: 10.5281/zenodo.3941649
title: Col d'Olen, water chemistry (N-NH4) 2008-2019

description: Dataset provides information about soil temperature, soil C and N dynamics at different sites in the alpine tundra, along an elevational gradient from 2500 to 2900 m asl during the period 2005-2018. Moreover, in ordrer to evaluate how topsoil can influence the quality of surface water, lake water was sampled monthly at the same time that soil was sampled. The N-NH4 concentration is measured in water samples collected in triplicate in two lakes (Cimalegna and Bowditch) from 2008 to 2019. The ongoing studies aimed at investigating how the meteorological variables affect the biogeochemistry of seasonally snow covered areas, with a special focus on the effect of the snow cover duration on soil and water C and N dynamics during the growing season.
DOI: 10.5281/zenodo.3941775
title: Col d'Olen, water chemistry (N-NO3) 2008-2019

description: Dataset provides information about soil temperature, soil C and N dynamics at different sites in the alpine tundra, along an elevational gradient from 2500 to 2900 m asl during the period 2005-2018. Moreover, in ordrer to evaluate how topsoil can influence the quality of surface water, lake water was sampled monthly at the same time that soil was sampled. The N-NO3 concentration is measured from 2008 to 2019 in water samples collected in triplicate in two lakes (Cimalegna and Bowditch). The ongoing studies aimed at investigating how the meteorological variables affect the biogeochemistry of seasonally snow covered areas, with a special focus on the effect of the snow cover duration on soil and water C and N dynamics during the growing season.
DOI: 10.5281/zenodo.3941648
title: Col d'Olen, water chemistry (N-NH4) 2008-2019

description: Dataset provides information about soil temperature, soil C and N dynamics at different sites in the alpine tundra, along an elevational gradient from 2500 to 2900 m asl during the period 2005-2018. Moreover, in ordrer to evaluate how topsoil can influence the quality of surface water, lake water was sampled monthly at the same time that soil was sampled. The N-NH4 concentration is measured in water samples collected in triplicate in two lakes (Cimalegna and Bowditch) from 2008 to 2019. The ongoing studies aimed at investigating how the meteorological variables affect the biogeochemistry of seasonally snow covered areas, with a special focus on the effect of the snow cover duration on soil and water C and N dynamics during the growing season.
DOI: 10.15129/4ec18371-3911-4e3e-823a-c65e38b7830f
title: A Hough Evaluation Platform with PYNQ and Mathworks' HDL Coder

description: This repository contains a PYNQ (Python Productivity for Zynq) evaluation platform for Field Programmable Gate Array (FPGA) architectures of the Line Hough Transform (LHT). This system is named the Hough Evaluation Platform (HEP). The HEP can calculate the time taken to apply the LHT to an image using a custom FPGA architecture and is capable of inspecting the Hough Parameter Space (HPS) using PYNQ's signal tracing and visualisation capbilities. The HEP can also calculate a new quantitative measurement for analysing the HPSs produced from different LHT architectures. This measurement is named the Peak-to-Mean Vote Ratio (PMVR) and is useful for quantifying the ability to reliably extract and separate peaks in the HPS from other neighbouring locations.
DOI: 10.5258/soton/d1381
title: Dataset for: Superoscillatory quartz lens with numerical aperture greater than one

description: Dataset with supporting data for paper: Zheludev, N, Yuan, GH, Tsai, DP & Lin, Y-H 2020, 'Superoscillatory quartz lens with numerical aperture greater than one' Applied Physics Letters.
DOI: 10.5281/zenodo.3941884
title: ZeroCostDL4Mic - CycleGAN example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - CycleGAN example training and test dataset (see our Wiki for details) <strong>Data type</strong>: unpaired microscopy images (fluorescence) of microtubules (Spinning-disk and SRRF reconstructed images) <strong>Microscopy data type</strong>: Fluorescence microscopy. <strong>Microscope</strong>: Spinning disk confocal microscope with a 100x 1.4 NA objective. SRRF reconstructions were performed using the latest generation of SRRF and 200 frames. <strong>Cell type</strong>: U2OS cells <strong>File format</strong>: .png (8-bit) <strong>Image size</strong>: 1024x1024 (Pixel size: 62.5 nm) <strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup> <strong>Contact email</strong>: guillaume.jacquemet@abo.fi <strong>Affiliation(s)</strong> : 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku 3) ORCID: 0000-0002-9286-920X <strong>Associated publications</strong>: Jacquemet et al 2020, Journal of Cell Science DOI: https://doi.org/10.1242/jcs.240713 <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.5281/zenodo.3941883
title: ZeroCostDL4Mic - CycleGAN example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - CycleGAN example training and test dataset (see our Wiki for details) <strong>Data type</strong>: unpaired microscopy images (fluorescence) of microtubules (Spinning-disk and SRRF reconstructed images) <strong>Microscopy data type</strong>: Fluorescence microscopy. <strong>Microscope</strong>: Spinning disk confocal microscope with a 100x 1.4 NA objective. SRRF reconstructions were performed using the latest generation of SRRF and 200 frames. <strong>Cell type</strong>: U2OS cells <strong>File format</strong>: .png (8-bit) <strong>Image size</strong>: 1024x1024 (Pixel size: 62.5 nm) <strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup> <strong>Contact email</strong>: guillaume.jacquemet@abo.fi <strong>Affiliation(s)</strong> : 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku 3) ORCID: 0000-0002-9286-920X <strong>Associated publications</strong>: Jacquemet et al 2020, Journal of Cell Science DOI: https://doi.org/10.1242/jcs.240713 <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.5281/zenodo.3941860
title: Chlorophyll-a concentrations estimated from particulate absorption coefficients and collocated particulate beam-attenuation coefficients between 620 and 710 nm.

description: Latest Version uploaded on: 2020/07/13 The dataset was produced using measurements collected from the NASA SeaBASS database and during three Atlantic Meridional Transect cruises (https://www.amt-uk.org/) and it is described in more details in a paper currently under review [1]. The dataset consists of chlorophyll-a concentrations estimated from particulate absorption coefficients using the line-height method [2]. The dataset also contains the latitude, longitude, UTC timestamp and the name of the research expeditions during which each point was collected. Finally, the dataset contains collocated particulate beam-attenuation coefficients at 620 nm to 710 nm every 2 nm. <strong>References</strong> [1] S. Graban, G. Dall'Olmo, S. Gould, R. Sauzede "Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient", (in review, May 2020). [2] E. S. Boss, R. Collier, G. Larson, K. Fennel, and W. S. Pegau, “Measurements of spectral optical properties and theirrelation to biogeochemical variables and processes in Crater Lake, Crater Lake National Park, OR,” Hydrobiologia574, 149–159 (2007).
DOI: 10.5519/qd.f182gh9x
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (299 records)
DOI: 10.5281/zenodo.3941908
title: ZeroCostDL4Mic - YoloV2 example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - YoloV2 example training and test dataset (see our Wiki for details) <strong>Data type</strong>: 2D grayscale .png images with corresponding bounding box annotations in .xml PASCAL Voc format. <strong>Microscopy data type</strong>: Phase contrast microscopy data (brightfield) <strong>Microscope</strong>: Inverted Zeiss Axio zoom widefield microscope equipped with an AxioCam MRm camera, an EL Plan-Neofluar 20 × /0.5 NA objective (Carl Zeiss), with a heated chamber (37 °C) and a CO2 controller (5%). <strong>Cell type</strong>: MDA-MB-231 cells migrating on cell-derived matrices generated by fibroblasts. <strong>File format</strong>: .png (8-bit) <strong>Image size</strong>: 1388 x 1040 px (323 nm) <strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup>, Lucas von Chamier<sup>4,5</sup> <strong>Contact email</strong>: lucas.chamier.13@ucl.ac.uk and guillaume.jacquemet@abo.fi <strong>Affiliation(s)</strong>:<strong> </strong> 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku 3) ORCID: 0000-0002-9286-920X 4) MRC-Laboratory for Molecular Cell Biology. University College London, London, UK 5) ORCID: 0000-0002-9243-912X <strong>Associated publications</strong>: Jacquemet et al 2016. DOI: 10.1038/ncomms13297 <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.5281/zenodo.3941907
title: ZeroCostDL4Mic - YoloV2 example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - YoloV2 example training and test dataset (see our Wiki for details) <strong>Data type</strong>: 2D grayscale .png images with corresponding bounding box annotations in .xml PASCAL Voc format. <strong>Microscopy data type</strong>: Phase contrast microscopy data (brightfield) <strong>Microscope</strong>: Inverted Zeiss Axio zoom widefield microscope equipped with an AxioCam MRm camera, an EL Plan-Neofluar 20 × /0.5 NA objective (Carl Zeiss), with a heated chamber (37 °C) and a CO2 controller (5%). <strong>Cell type</strong>: MDA-MB-231 cells migrating on cell-derived matrices generated by fibroblasts. <strong>File format</strong>: .png (8-bit) <strong>Image size</strong>: 1388 x 1040 px (323 nm) <strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup>, Lucas von Chamier<sup>4,5</sup> <strong>Contact email</strong>: lucas.chamier.13@ucl.ac.uk and guillaume.jacquemet@abo.fi <strong>Affiliation(s)</strong>:<strong> </strong> 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku 3) ORCID: 0000-0002-9286-920X 4) MRC-Laboratory for Molecular Cell Biology. University College London, London, UK 5) ORCID: 0000-0002-9243-912X <strong>Associated publications</strong>: Jacquemet et al 2016. DOI: 10.1038/ncomms13297 <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.17632/nv6frvt537.1
title: Data for: 3649210

description: OP = Oil Price GDP = Gross Domestic Product EXR = Exchange Rate FBI = Foreign Direct Investment NX = Net Export INF = inflation Βo = Intercept B1 = co-efficient E = Error Term r = Pearson Correlation t = time series
DOI: 10.17632/8n2ymkttkp.4
title: Building Information Modeling (BIM) adoption level in Lima and Callao, Dataset

description: This research data aims to measure the level of Building Information Modeling (BIM) adoption in urban building projects in Lima city and Callao by the end of 2017. This level helped us to determine in what category of adopter Lima city, Peru most important city in terms of urban buildings, is located according to the Diffusion of Innovations theory (Rogers, 2003). Our hypothesis was that 15% of urban building projects adopted BIM by the end of 2017. The level of adoption is estimated through sampling principles and the population data (N=1218) can be found in the publication “Urban buildings market in Lima city and Callao 2017: edition 22” (CAPECO, 2017). The survey (docx file) is divided in five sections: general data of the interviewee, BIM perception, BIM acceptance, BIM adoption and general data of the project. The final data (xlsx file) provides the results of the survey that was answer by 323 professionals related to the building industry (Civil engineers, architects and others) and each answer corresponded to a unique project. As it was mentioned the population was based on all new urban building projects in the geographical area of study and under construction process during the period of data collection. A project was considered under construction process when it was found at the beginning of earthworks or preliminary works until the delivery of the unoccupied project. In addition, remodeling projects that involved expanding their built-up area were also considered, but this expansion had to be at least 500 sm. On the other hand, all single family houses and multi-family buildings that do not had a public construction license were excluded. The data collection was carried out by a research manager and two research assistants. Each research assistant was assigned a certain number of clusters within the designed sample. The sampling frame used in this research is one of an area type which are geographical surfaces well-defined. These surfaces are clusters and in this case were districts of Lima city and Callao. The data collection was taken from October to December of 2017. The method to reach the sample size (n=323) was through an emailed virtual survey (52 answers) and by visiting building projects site (271 answers). Projects visited were found aleatory, with the only requirement to be inside a designed sample cluster. It has been considered that a project has adopted BIM if it has used any of its applications: 3D models visualization; 3D modeling; material quantification and budgets made from 3D models; structure, MEP or HVAC model coordination; 4D construction simulation; control of construction progress with BIM; procurement of precast components; or the generation of 2D drawings from 3D models. The main notable finding is that the BIM adoption level in urban building projects in Lima city and Callao in 2017 was 21.6% (70/323), which places this analyzed region in the category of “early majority” adopters (Rogers, 2003).
DOI: 10.5281/zenodo.3941889
title: ZeroCostDL4Mic - pix2pix example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - pix2pix example training and test dataset (see our Wiki for details) <strong>Data type</strong>: Paired microscopy images (fluorescence) of lifeact-RFP and sir-DNA <strong>Microscopy data type</strong>: Fluorescence microscopy <strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective <strong>Cell type</strong>: DCIS.COM Lifeact-RFP cells <strong>File format</strong>: .png (RGB images) <strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm) <strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup> <strong>Contact email</strong>: guillaume.jacquemet@abo.fi <strong>Affiliation(s)</strong> : 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku 3) ORCID: 0000-0002-9286-920X <br> <strong>Associated publications</strong>: Unpublished <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.5281/zenodo.3941888
title: ZeroCostDL4Mic - pix2pix example training and test dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - pix2pix example training and test dataset (see our Wiki for details) <strong>Data type</strong>: Paired microscopy images (fluorescence) of lifeact-RFP and sir-DNA <strong>Microscopy data type</strong>: Fluorescence microscopy <strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective <strong>Cell type</strong>: DCIS.COM Lifeact-RFP cells <strong>File format</strong>: .png (RGB images) <strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm) <strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2,3</sup> <strong>Contact email</strong>: guillaume.jacquemet@abo.fi <strong>Affiliation(s)</strong> : 1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland 2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku 3) ORCID: 0000-0002-9286-920X <br> <strong>Associated publications</strong>: Unpublished <strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
DOI: 10.17632/nbcfv7ktsp.1
title: MR Imaging raw data collected on Bruker 7T for chemotherapy induced neuropathic pain.

description: Neuropathic pain is an unfortunate side-effect in the use of chemotherapy for the treatment of cancer. To date there have been no reports using imaging to characterize chemo-therapy induced neuropathy. To that end, we used the common chemotherapy paclitaxel to induced cold allodynia followed by different MRI protocols to show a rapid reorganization of brain neural circuits. Rats received either paclitaxel (2 mg/kg/day i.p.; cumulative dose of 8 mg/kg) or its cremophor-based vehicle four times on alternate days (i.e. day 0, 2, 4, 6). At the cessation of treatments, rats were tested for cold allodynia followed by diffusion weighted magnetic resonance imaging and resting state functional connectivity. Imaging data was analyzed using a 3D MRI rat with 173 segmented and annotated brain areas. Paclitaxel treated rats were significantly more sensitive to a cold stimulus as compared to controls. Diffusion weighted imaging identified a constellation of brain areas e.g. prefrontal ctx, amygdala, hippocampus, hypothalamus and the striatal/accumbens dopaminergic system involved in the emotional and motivations response to chronic pain. This putative reorganization of gray matter microarchitecture forms a continuum of brain areas stretching from the basal medial/lateral forebrain to the midbrain. Resting state functional connectivity showed reorganization between the periaqueductal gray, a key node in pain neural circuitry, and connections to the brainstem. The data included has two groups. Group A is Control, Group B is treatrd with Paclitaxel. Data included in this folder is in original Bruker format. If you need any conversion help please contact Praveen Kulkarni (p.kulkarni@Northeastern.edu) Each study has Following scans, scan number may vary for study and can be determined from method file. 1) DWI : DTIEPI scan with 10 directions 2 averages and B=1000. 2) Resting state scan 1: 3 segment EPI, TR: 1sec 10 min scan. 3) Resting state scan 2: 3 segment EPI, TR: 1sec 10 min scan. 4) T2 weight high res Anatomy scan.