TY  - JOUR
AB  - Spheroids are widely used in oncology for testing drugs, but models composed of a single cell line do not fully capture the complexity of the in vivo tumours targeted by chemotherapy. Developing 3D in vitro models that better mimic tumour architecture is a crucial step for the scientific community. To enable more reliable drug testing, we generated multiculture spheroids and analysed cell morphology and distribution over time. This dataset is the first publicly available single-cell light-sheet fluorescence microscopy image collection of 3D multiculture tumour models comprising of three different cell lines analysed at different time points. Specifically, we created models composed of one cancer cell line (melanoma, breast cancer, or osteosarcoma) alongside two stromal cell lines (fibroblasts and endothelial cells). Then, we acquired single-cell resolution light-sheet fluorescence 3D images of the spheroids to analyse spheroid morphology after 24, 48, and 96 hours. The image collection, whole spheroid annotations, and extracted features are publicly available for further research and can support the development of automated analysis models.
AU  - Diosdi, A.*
AU  - Piccinini, F.*
AU  - Boroczky, T.*
AU  - Dobra, G.*
AU  - Castellani, G.*
AU  - Buzas, K.*
AU  - Horvath, P.
AU  - Harmati, M.*
C1  - 73746
C2  - 57211
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - Single-cell light-sheet fluorescence 3D images of tumour-stroma spheroid multicultures.
JO  - Sci. Data
VL  - 12
IS  - 1
PB  - Nature Portfolio
PY  - 2025
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care.
AU  - Garrucho, L.*
AU  - Kushibar, K.*
AU  - Reidel, C.A.*
AU  - Joshi, S.*
AU  - Osuala, R.
AU  - Tsirikoglou, A.*
AU  - Bobowicz, M.*
AU  - Del Riego, J.*
AU  - Catanese, A.*
AU  - Gwoździewicz, K.*
AU  - Cosaka, M.L.*
AU  - Abo-Elhoda, P.M.*
AU  - Tantawy, S.W.*
AU  - Sakrana, S.S.*
AU  - Shawky-Abdelfatah, N.O.*
AU  - Salem, A.M.A.*
AU  - Kozana, A.*
AU  - Divjak, E.*
AU  - Ivanac, G.*
AU  - Nikiforaki, K.*
AU  - Klontzas, M.E.*
AU  - García-Dosdá, R.*
AU  - Gulsun-Akpinar, M.*
AU  - Lafcı, O.*
AU  - Mann, R.M.*
AU  - Martín-Isla, C.*
AU  - Prior, F.*
AU  - Marias, K.*
AU  - Starmans, M.P.A.*
AU  - Strand, F.*
AU  - Diaz, O.*
AU  - Igual, L.*
AU  - Lekadir, K.*
C1  - 73728
C2  - 57196
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations.
JO  - Sci. Data
VL  - 12
IS  - 1
PB  - Nature Portfolio
PY  - 2025
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Indoor air quality (IAQ) significantly influences human health, as individuals spend up to 90% of their time indoors, where air pollutants can accumulate and interact dynamically. Despite advancements in monitoring technology, challenges remain in capturing the temporal and spatial variability of pollutants and understanding the interaction between indoor and outdoor environments. This study addresses these gaps by introducing a comprehensive dataset from a controlled experimental room in Croatia, leveraging a multi-instrumental approach to monitor IAQ across various real-life scenarios. The dataset integrates measurements from low-cost sensors, reference-grade devices, and auxiliary systems to track pollutants such as particulate matter (PM), black carbon (BC), volatile organic compounds (VOC), and indoor events deemed relevant for the assessment of pollutant levels. Key experiments simulated household activities, including cooking, cleaning, human presence, and ventilation, capturing their impacts on IAQ with high temporal resolution. The resulting dataset comprises over 19 subsets. This work contributes to the Horizon EDIAQI project, supporting the development of evidence-driven strategies to improve IAQ.
AU  - Lovrić, M.*
AU  - Petric, V.*
AU  - Strbad, D.*
AU  - Terzić, T.*
AU  - Frka, S.*
AU  - Kušan, A.C.*
AU  - Fermoso, J.*
AU  - Düsing, S.*
AU  - Alas, H.D.*
AU  - Ladavac, M.D.*
AU  - Bilić, I.*
AU  - Batrac, M.*
AU  - Kecorius, S.
AU  - Pehnec, G.*
AU  - Horvat, T.*
AU  - Jakovljević, I.*
AU  - Racic, N.*
AU  - Bešlić, I.*
AU  - Brzoja, D.*
AU  - Gugec, V.*
AU  - Figols, M.*
AU  - Aláez, X.*
AU  - Matanović, H.*
AU  - Žigman, A.*
AU  - Forsmann, M.*
AU  - Toomis, A.*
AU  - Preden, J.S.*
AU  - Battaglia, A.*
AU  - Battaglia, I.*
AU  - Karanasiou, G.*
AU  - Weis, F.*
AU  - Switters, J.*
AU  - Mureddu, F.*
C1  - 75671
C2  - 58165
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - Indoor and ambient air pollution dataset using a multi-instrument approach and total event monitoring.
JO  - Sci. Data
VL  - 12
IS  - 1
PB  - Nature Portfolio
PY  - 2025
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Ensuring the integrity of research data is crucial for the accuracy and reproducibility of any data-based scientific study. This can only be achieved by establishing and implementing strict rules for the handling of research data. Essential steps for achieving high-quality data involve planning what data to gather, collecting it in the correct manner, and processing it in a robust and reproducible way. Despite its importance, a comprehensive framework detailing how to achieve data quality is currently unavailable. To address this gap, our study proposes guidelines designed to establish a reliable approach to data handling. They provide clear and practical instructions for the complete research process, including an overall data collection strategy, variable definitions, and data processing recommendations. In addition to raising awareness about potential pitfalls and establishing standardization in research data usage, the proposed guidelines serve as a reference for researchers to provide a consistent standard of data quality. Furthermore, they improve the robustness and reliability of the scientific landscape by emphasising the critical role of data quality in research.
AU  - Miller, G.
AU  - Spiegel, E.
C1  - 73094
C2  - 56911
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - Guidelines for Research Data Integrity (GRDI).
JO  - Sci. Data
VL  - 12
IS  - 1
PB  - Nature Portfolio
PY  - 2025
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Distinguishing cell types in a peripheral blood smear is critical for diagnosing blood diseases, such as leukemia subtypes. Artificial intelligence can assist in automating cell classification. For training robust machine learning algorithms, however, large and well-annotated single-cell datasets are pivotal. Here, we introduce a large, publicly available, annotated peripheral blood dataset comprising >40,000 single-cell images classified into 18 classes by cytomorphology experts from the Munich Leukemia Laboratory, the largest European laboratory for blood disease diagnostics. By making our dataset publicly available, we provide a valuable resource for medical and machine learning researchers and support the development of reliable and clinically relevant diagnostic tools for diagnosing hematological diseases.
AU  - Shetab Boushehri, S.
AU  - Kazeminia, S.
AU  - Gruber, A.
AU  - Matek, C.
AU  - Spiekermann, K.*
AU  - Pohlkamp, C.*
AU  - Haferlach, T.*
AU  - Marr, C.
C1  - 75989
C2  - 58318
TI  - A large expert-annotated single-cell peripheral blood dataset for hematological disease diagnostics.
JO  - Sci. Data
VL  - 12
IS  - 1
PY  - 2025
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - The German Center for Diabetes Research (DZD) established a core data set (CDS) of clinical parameters relevant for diabetes research in 2021. The CDS is central to the design of current and future DZD studies. Here, we describe the process and outcomes of FAIRifying the initial version of the CDS. We first did a baseline evaluation of the FAIRness using the FAIR Data Maturity Model. The FAIRification process and the results of this assessment led us to convert the CDS into the recommended format for spreadsheets, annotating the parameters with standardized medical codes, licensing the data set, enriching the data set with metadata, and indexing the metadata. The FAIRified version of the CDS is more suitable for data sharing in diabetes research across DZD sites and beyond. It contributes to the reusability of health research studies.
AU  - Inau, E.T.*
AU  - Dedié, A.*
AU  - Anastasova, I.*
AU  - Schick, R.*
AU  - Zdravomyslov, Y.*
AU  - Fröhlich, B.*
AU  - Birkenfeld, A.L.
AU  - Hrabě de Angelis, M.
AU  - Roden, M.*
AU  - Zeleke, A.A.*
AU  - Preusse, M.*
AU  - Waltemath, D.*
C1  - 72117
C2  - 56373
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - The Journey to a FAIR CORE DATA SET for Diabetes Research in Germany.
JO  - Sci. Data
VL  - 11
IS  - 1
PB  - Nature Portfolio
PY  - 2024
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.
AU  - Kecorius, S.
AU  - Madueno, L.*
AU  - Lovrić, M.*
AU  - Racic, N.*
AU  - Schwarz, M.
AU  - Cyrys, J.
AU  - Casquero-Vera, J.A.*
AU  - Alados-Arboledas, L.*
AU  - Conil, S.*
AU  - Sciare, J.*
AU  - Ondracek, J.*
AU  - Hallar, A.G.*
AU  - Gómez-Moreno, F.J.*
AU  - Ellul, R.*
AU  - Kristensson, A.*
AU  - Sorribas, M.*
AU  - Kalivitis, N.*
AU  - Mihalopoulos, N.*
AU  - Peters, A.
AU  - Gini, M.*
AU  - Eleftheriadis, K.*
AU  - Vratolis, S.*
AU  - Jeongeun, K.*
AU  - Birmili, W.*
AU  - Bergmans, B.*
AU  - Nikolova, N.*
AU  - Dinoi, A.*
AU  - Contini, D.*
AU  - Marinoni, A.*
AU  - Alastuey, A.*
AU  - Petäjä, T.*
AU  - Rodriguez, S.*
AU  - Picard, D.*
AU  - Brem, B.*
AU  - Priestman, M.*
AU  - Green, D.C.*
AU  - Beddows, D.C.S.*
AU  - Harrison, R.M.*
AU  - O'Dowd, C.*
AU  - Ceburnis, D.*
AU  - Hyvärinen, A.*
AU  - Henzing, B.*
AU  - Crumeyrolle, S.*
AU  - Putaud, J.P.*
AU  - Laj, P.*
AU  - Weinhold, K.*
AU  - Plauškaite, K.*
AU  - Byčenkiene, S.*
C1  - 72385
C2  - 56606
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - Atmospheric new particle formation identifier using longitudinal global particle number size distribution data.
JO  - Sci. Data
VL  - 11
IS  - 1
PB  - Nature Portfolio
PY  - 2024
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.
AU  - LaBella, D.*
AU  - Khanna, O.*
AU  - McBurney-Lin, S.*
AU  - McLean, R.M.*
AU  - Nedelec, P.*
AU  - Rashid, A.S.*
AU  - Tahon, N.H.*
AU  - Altes, T.*
AU  - Baid, U.*
AU  - Bhalerao, R.P.*
AU  - Dhemesh, Y.*
AU  - Floyd, S.*
AU  - Godfrey, D.I.*
AU  - Hilal, F.*
AU  - Janas, A.*
AU  - Kazerooni, A.*
AU  - Kent, C.*
AU  - Kirkpatrick, J.*
AU  - Kofler, F.
AU  - Leu, K.*
AU  - Maleki, N.*
AU  - Menze, B.*
AU  - Pajot, M.*
AU  - Reitman, Z.J.*
AU  - Rudie, J.D.*
AU  - Saluja, R.*
AU  - Velichko, Y.*
AU  - Wang, C.*
AU  - Warman, P.I.*
AU  - Sollmann, N.*
AU  - Diffley, D.*
AU  - Nandolia, K.K.*
AU  - Warren, D.I.*
AU  - Hussain, A.*
AU  - Fehringer, J.P.*
AU  - Bronstein, Y.*
AU  - Deptula, L.*
AU  - Stein, E.G.*
AU  - Taherzadeh, M.*
AU  - Portela de Oliveira, E.*
AU  - Haughey, A.*
AU  - Kontzialis, M.*
AU  - Saba, L.*
AU  - Turner, B.M.*
AU  - Brüßeler, M.M.T.*
AU  - Ansari, S.*
AU  - Gkampenis, A.*
AU  - Weiss, D.M.*
AU  - Mansour, A.*
AU  - Shawali, I.H.*
AU  - Yordanov, N.*
AU  - Stein, J.M.*
AU  - Hourani, R.*
AU  - Moshebah, M.Y.*
AU  - Abouelatta, A.M.*
AU  - Rizvi, T.*
AU  - Willms, K.*
AU  - Martin, D.C.*
AU  - Okar, A.*
AU  - D'Anna, G.*
AU  - Taha, A.*
AU  - Sharifi, Y.*
AU  - Faghani, S.*
AU  - Kite, D.*
AU  - Pinho, M.*
AU  - Haider, M.A.*
AU  - Alonso-Basanta, M.*
AU  - Villanueva-Meyer, J.*
AU  - Rauschecker, A.M.*
AU  - Nada, A.*
AU  - Aboian, M.*
AU  - Flanders, A.*
AU  - Bakas, S.*
AU  - Calabrese, E.*
C1  - 70692
C2  - 56016
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.
JO  - Sci. Data
VL  - 11
IS  - 1
PB  - Nature Portfolio
PY  - 2024
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts. However, relevant information resides at differing stages across the data-lifecycle. Often, this information is defined and standardized only at publication stage, which can lead to data loss and workload increase. In this study, we developed Metadatasheet, a metadata standard based on interviews with members of two biomedical consortia and systematic screening of data repositories. It aligns with the data-lifecycle allowing synchronous metadata recording within Microsoft Excel, a widespread data recording software. Additionally, we provide an implementation, the Metadata Workbook, that offers user-friendly features like automation, dynamic adaption, metadata integrity checks, and export options for various metadata standards. By design and due to its extensive documentation, the proposed metadata standard simplifies recording and structuring of metadata for biomedical scientists, promoting practicality and convenience in data management. This framework can accelerate scientific progress by enhancing collaboration and knowledge transfer throughout the intermediate steps of data creation.
AU  - Seep, L.*
AU  - Grein, S.*
AU  - Splichalova, I.*
AU  - Ran, D.*
AU  - Mikhael, M.*
AU  - Hildebrand, S.*
AU  - Lauterbach, M.*
AU  - Hiller, K.*
AU  - Ribeiro, D.J.S.*
AU  - Sieckmann, K.*
AU  - Kardinal, R.*
AU  - Huang, H.*
AU  - Yu, J.*
AU  - Kallabis, S.*
AU  - Behrens, J.*
AU  - Till, A.*
AU  - Peeva, V.*
AU  - Strohmeyer, A.*
AU  - Bruder, J.*
AU  - Blum, T.*
AU  - Soriano-Arroquia, A.*
AU  - Tischer, D.*
AU  - Kuellmer, K.*
AU  - Li, Y.*
AU  - Beyer, M.*
AU  - Gellner, A.K.*
AU  - Fromme, T.*
AU  - Wackerhage, H.*
AU  - Klingenspor, M.*
AU  - Fenske, W.K.*
AU  - Scheja, L.*
AU  - Meissner, F.*
AU  - Schlitzer, A.*
AU  - Mass, E.*
AU  - Wachten, D.*
AU  - Latz, E.*
AU  - Pfeifer, A.*
AU  - Hasenauer, J.
C1  - 70734
C2  - 55729
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - From planning stage towards FAIR data: A practical metadatasheet for biomedical scientists.
JO  - Sci. Data
VL  - 11
IS  - 1
PB  - Nature Portfolio
PY  - 2024
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - The dataset presents a 43 year-long reanalysis of pollen seasons for three major allergenic genera of trees in Europe: alder (Alnus), birch (Betula), and olive (Olea). Driven by the meteorological reanalysis ERA5, the atmospheric composition model SILAM predicted the flowering period and calculated the Europe-wide dispersion pattern of pollen for the years 1980-2022. The model applied an extended 4-dimensional variational data assimilation of in-situ observations of aerobiological networks in 34 European countries to reproduce the inter-annual variability and trends of pollen production and distribution. The control variable of the assimilation procedure was the total pollen release during each flowering season, implemented as an annual correction factor to the mean pollen production. The dataset was designed as an input to studies on climate-induced and anthropogenically driven changes in the European vegetation, biodiversity monitoring, bioaerosol modelling and assessment, as well as, in combination with intra-seasonal observations, for health-related applications.
AU  - Sofiev, M.*
AU  - Palamarchuk, J.*
AU  - Kouznetsov, R.*
AU  - Abramidze, T.*
AU  - Adams-Groom, B.*
AU  - Antunes, C.M.*
AU  - Ariño, A.H.*
AU  - Bastl, M.*
AU  - Belmonte, J.*
AU  - Berger, U.E.*
AU  - Bonini, M.*
AU  - Bruffaerts, N.*
AU  - Buters, J.T.M.
AU  - Cariñanos, P.*
AU  - Celenk, S.*
AU  - Ceriotti, V.*
AU  - Charalampopoulos, A.*
AU  - Clewlow, Y.*
AU  - Clot, B.*
AU  - Dahl, A.*
AU  - Damialis, A.*
AU  - De Linares, C.*
AU  - de Weger, L.A.*
AU  - Dirr, L.*
AU  - Ekebom, A.*
AU  - Fatahi, Y.*
AU  - Fernández González, M.*
AU  - Fernández González, D.*
AU  - Fernández-Rodríguez, S.*
AU  - Galán, C.*
AU  - Gedda, B.*
AU  - Gehrig, R.*
AU  - Geller Bernstein, C.*
AU  - Gonzalez Roldan, N.*
AU  - Grewling, L.*
AU  - Hajkova, L.*
AU  - Hänninen, R.*
AU  - Hentges, F.*
AU  - Jantunen, J.*
AU  - Kadantsev, E.*
AU  - Kasprzyk, I.*
AU  - Kloster, M.*
AU  - Kluska, K.*
AU  - Koenders, M.*
AU  - Lafférsová, J.*
AU  - Leru, P.M.*
AU  - Lipiec, A.*
AU  - Louna-Korteniemi, M.*
AU  - Magyar, D.*
AU  - Majkowska-Wojciechowska, B.*
AU  - Mäkelä, M.*
AU  - Mitrovic, M.*
AU  - Myszkowska, D.*
AU  - Oliver, G.*
AU  - Östensson, P.*
AU  - Pérez-Badía, R.*
AU  - Piotrowska-Weryszko, K.*
AU  - Prank, M.*
AU  - Przedpelska-Wasowicz, E.M.*
AU  - Pätsi, S.*
AU  - Rajo, F.J.R.*
AU  - Ramfjord, H.*
AU  - Rapiejko, J.*
AU  - Rodinkova, V.*
AU  - Rojo, J.*
AU  - Ruíz-Valenzuela, L.*
AU  - Rybníček, O.*
AU  - Saarto, A.*
AU  - Sauliene, I.*
AU  - Seliger, A.K.*
AU  - Severova, E.*
AU  - Shalaboda, V.*
AU  - Sikoparija, B.*
AU  - Siljamo, P.*
AU  - Soares, J.*
AU  - Sozinova, O.*
AU  - Stangel, A.*
AU  - Stjepanovic, B.*
AU  - Teinemaa, E.*
AU  - Tyuryakov, S.*
AU  - Trigo, M.M.*
AU  - Uppstu, A.*
AU  - Vill, M.*
AU  - Vira, J.*
AU  - Visez, N.*
AU  - Vitikainen, T.*
AU  - Vokou, D.*
AU  - Weryszko-Chmielewska, E.*
AU  - Karppinen, A.*
C1  - 71911
C2  - 56541
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - European pollen reanalysis, 1980-2022, for alder, birch, and olive.
JO  - Sci. Data
VL  - 11
IS  - 1
PB  - Nature Portfolio
PY  - 2024
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Metadata from epidemiological studies, including chronic disease outcome metadata (CDOM), are important to be findable to allow interpretability and reusability. We propose a comprehensive metadata schema and used it to assess public availability and findability of CDOM from German population-based observational studies participating in the consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health). Additionally, principal investigators from the included studies completed a checklist evaluating consistency with FAIR principles (Findability, Accessibility, Interoperability, Reusability) within their studies. Overall, six of sixteen studies had complete publicly available CDOM. The most frequent CDOM source was scientific publications and the most frequently missing metadata were availability of codes of the International Classification of Diseases, Tenth Revision (ICD-10). Principal investigators' main perceived barriers for consistency with FAIR principles were limited human and financial resources. Our results reveal that CDOM from German population-based studies have incomplete availability and limited findability. There is a need to make CDOM publicly available in searchable platforms or metadata catalogues to improve their FAIRness, which requires human and financial resources.
AU  - Schwedhelm, C.*
AU  - Nimptsch, K.*
AU  - Ahrens, W.*
AU  - Hasselhorn, H.M.*
AU  - Jöckel, K.H.*
AU  - Katzke, V.*
AU  - Kluttig, A.*
AU  - Linkohr, B.
AU  - Mikolajczyk, R.*
AU  - Nöthlings, U.*
AU  - Perrar, I.*
AU  - Peters, A.
AU  - Schmidt, C.O.*
AU  - Schmidt, B.*
AU  - Schulze, M.B.*
AU  - Stang, A.*
AU  - Zeeb, H.*
AU  - Pischon, T.*
C1  - 68951
C2  - 53674
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - Chronic disease outcome metadata from German observational studies - public availability and FAIR principles.
JO  - Sci. Data
VL  - 10
IS  - 1
PB  - Nature Portfolio
PY  - 2023
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions (https://doi.org/10.5281/zenodo.7153326). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge (https://www.isles-challenge.org/) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.
AU  - Hernandez Petzsche, M.R.*
AU  - de la Rosa, E.*
AU  - Hanning, U.*
AU  - Wiest, R.*
AU  - Valenzuela, W.*
AU  - Reyes, M.*
AU  - Meyer, M.*
AU  - Liew, S.L.*
AU  - Kofler, F.
AU  - Ezhov, I.*
AU  - Robben, D.*
AU  - Hutton, A.*
AU  - Friedrich, T.*
AU  - Zarth, T.*
AU  - Bürkle, J.*
AU  - Baran, T.A.*
AU  - Menze, B.*
AU  - Broocks, G.*
AU  - Meyer, L.*
AU  - Zimmer, C.*
AU  - Boeckh-Behrens, T.*
AU  - Berndt, M.*
AU  - Ikenberg, B.*
AU  - Wiestler, B.*
AU  - Kirschke, J.S.*
C1  - 66949
C2  - 53383
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.
JO  - Sci. Data
VL  - 9
IS  - 1
PB  - Nature Portfolio
PY  - 2022
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Plant growth and development are regulated by a tightly controlled interplay between cell division, cell expansion and cell differentiation during the entire plant life cycle from seed germination to maturity and seed propagation. To explore some of the underlying molecular mechanisms in more detail, we selected different aerial tissue types of the model plantArabidopsis thaliana, namely rosette leaf, flower and silique/seed and performed proteomic, phosphoproteomic and transcriptomic analyses of sequential growth stages using tandem mass tag-based mass spectrometry and RNA sequencing. With this exploratory multi-omics dataset, development dynamics of photosynthetic tissues can be investigated from different angles. As expected, we found progressive global expression changes between growth stages for all three omics types and often but not always corresponding expression patterns for individual genes on transcript, protein and phosphorylation site level. The biggest difference between proteomic- and transcriptomic-based expression information could be observed for seed samples. Proteomic and transcriptomic data is available via Proteome Xchange and ArrayExpress with the respective identifiers PXD018814 and E-MTAB-7978.
AU  - Mergner, J.*
AU  - Frejno, M.*
AU  - Messerer, M.
AU  - Lang, D.
AU  - Samaras, P.*
AU  - Wilhelm, M.*
AU  - Mayer, K.F.X.
AU  - Schwechheimer, C.*
AU  - Kuster, B.*
C1  - 60293
C2  - 49371
CY  - Heidelberger Platz 3, Berlin, 14197, Germany
TI  - Proteomic and transcriptomic profiling of aerial organ development in Arabidopsis.
JO  - Sci. Data
VL  - 7
IS  - 1
PB  - Nature Research
PY  - 2020
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Researchers around the world join forces to reconstruct the molecular processes of the virus-host interactions aiming to combat the cause of the ongoing pandemic.
AU  - Ostaszewski, M.*
AU  - Mazein, A.*
AU  - Gillespie, M.E.*
AU  - Kuperstein, I.*
AU  - Niarakis, A.*
AU  - Hermjakob, H.*
AU  - Pico, A.R.*
AU  - Willighagen, E.L.*
AU  - Evelo, C.T.*
AU  - Hasenauer, J.
AU  - Schreiber, F.*
AU  - Dräger, A.*
AU  - Demir, E.*
AU  - Wolkenhauer, O.*
AU  - Furlong, L.I.*
AU  - Barillot, E.*
AU  - Dopazo, J.*
AU  - Orta-Resendiz, A.*
AU  - Messina, F.*
AU  - Valencia, A.*
AU  - Funahashi, A.*
AU  - Kitano, H.*
AU  - Auffray, C.*
AU  - Balling, R.*
AU  - Schneider, R.*
C1  - 59038
C2  - 48656
CY  - Macmillan Building, 4 Crinan St, London N1 9xw, England
TI  - COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.
JO  - Sci. Data
VL  - 7
IS  - 1
PB  - Nature Publishing Group
PY  - 2020
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - An amendment to this paper has been published and can be accessed via a link at the top of the paper.
AU  - Ostaszewski, M.*
AU  - Mazein, A.*
AU  - Gillespie, M.E.*
AU  - Kuperstein, I.*
AU  - Niarakis, A.*
AU  - Hermjakob, H.*
AU  - Pico, A.R.*
AU  - Willighagen, E.L.*
AU  - Evelo, C.T.*
AU  - Hasenauer, J.
AU  - Schreiber, F.*
AU  - Dräger, A.*
AU  - Demir, E.*
AU  - Wolkenhauer, O.*
AU  - Furlong, L.I.*
AU  - Barillot, E.*
AU  - Dopazo, J.*
AU  - Orta-Resendiz, A.*
AU  - Messina, F.*
AU  - Valencia, A.*
AU  - Funahashi, A.*
AU  - Kitano, H.*
AU  - Auffray, C.*
AU  - Balling, R.*
AU  - Schneider, R.*
C1  - 59694
C2  - 48978
TI  - Author Correction: COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms (Scientific Data, (2020), 7, 1, (136), 10.1038/s41597-020-0477-8).
JO  - Sci. Data
VL  - 7
IS  - 1
PY  - 2020
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).
AU  - Sieberts, S.K.*
AU  - Perumal, T.M.*
AU  - Carrasquillo, M.M.*
AU  - Allen, M.*
AU  - Reddy, J.S.*
AU  - Hoffman, G.E.*
AU  - Dang, K.K.*
AU  - Calley, J.*
AU  - Ebert, P.J.*
AU  - Eddy, J.*
AU  - Wang, X.*
AU  - Greenwood, A.K.*
AU  - Mostafavi, S.*
AU  - Omberg, L.*
AU  - Peters, M.A.*
AU  - Logsdon, B.A.*
AU  - de Jager, P.L.*
AU  - Ertekin-Taner, N.*
AU  - Mangravite, L.M.*
AU  - The AMP-AD Consortium (Arnold, M.)
C1  - 60315
C2  - 49380
TI  - Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions.
JO  - Sci. Data
VL  - 7
IS  - 1
PY  - 2020
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - X chromosome genetic variation has been proposed as a potential source of missing heritability for many complex diseases, including obesity. Currently, there is a lack of public available genetic datasets incorporating X chromosome genotype data. Although several X chromosome-specific statistics have been developed, there is also a lack of readily available implementations for routine analysis. Here, we aimed: (1) to make public and describe a dataset incorporating phenotype and X chromosome genotype data from a cohort of 915 normal-weight, overweight and obese children, and (2) to deeply describe a whole implementation of the special X chromosome analytic process in genetics. Datasets and pipelines like this are crucial to get familiar with the steps in which X chromosome requires special attention and may raise awareness of the importance of this genomic region.
AU  - Anguita-Ruiz, A.*
AU  - Plaza-Díaz, J.*
AU  - Ruiz Ojeda, F.J.
AU  - Rupérez, A.I.*
AU  - Leis, R.*
AU  - Bueno, G.*
AU  - Gil-Campos, M.*
AU  - Vázquez-Cobela, R.*
AU  - Cañete, R.*
AU  - Moreno, L.A.*
AU  - Gil, A.*
AU  - Aguilera, C.M.*
C1  - 56613
C2  - 47181
CY  - Macmillan Building, 4 Crinan St, London N1 9xw, England
TI  - X chromosome genetic data in a Spanish children cohort, dataset description and analysis pipeline.
JO  - Sci. Data
VL  - 6
IS  - 1
PB  - Nature Publishing Group
PY  - 2019
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Alzheimer's disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consortium (ADMC) in partnership with AD Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for patients in the ADNI1 cohort. We used the Biocrates Bile Acids platform to evaluate the association of metabolic levels with disease risk and progression. We detail the quantitative metabolomics data generated on the baseline samples from ADNI1 and ADNIGO/2 (370 cognitively normal, 887 mild cognitive impairment, and 305 AD). Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis. This data descriptor represents the third in a series of comprehensive metabolomics datasets from the ADMC on the ADNI.
AU  - St John-Williams, L.*
AU  - MahmoudianDehkordi, S.*
AU  - Arnold, M.
AU  - Massaro, T.*
AU  - Blach, C.*
AU  - Kastenmüller, G.
AU  - Louie, G.*
AU  - Kueider-Paisley, A.*
AU  - Han, X.*
AU  - Baillie, R.*
AU  - Motsinger-Reif, A.A.*
AU  - Rotroff, D.*
AU  - Nho, K.*
AU  - Saykin, A.J.*
AU  - Risacher, S.L.*
AU  - Koal, T.*
AU  - Moseley, M.A.*
AU  - Tenenbaum, J.D.*
AU  - Thompson, K.*
AU  - Kaddurah-Daouk, R.*
AU  - Alzheimer Disease Metabolomics Consortium*
C1  - 57146
C2  - 47572
CY  - Macmillan Building, 4 Crinan St, London N1 9xw, England
TI  - Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts.
JO  - Sci. Data
VL  - 6
IS  - 1
PB  - Nature Publishing Group
PY  - 2019
SN  - 2052-4463
ER  - 

TY  - JOUR
AU  - Flannick, J.*
AU  - Fuchsberger, C.*
AU  - Mahajan, A.*
AU  - Teslovich, T.M.*
AU  - Agarwala, V.*
AU  - Gaulton, K.J.*
AU  - Caulkins, L.*
AU  - Koesterer, R.*
AU  - Ma, C.*
AU  - Moutsianas, L.*
AU  - McCarthy, D.J.*
AU  - Rivas, M.A.*
AU  - Perry, J.R.B.*
AU  - Sim, X.*
AU  - Blackwell, T.W.*
AU  - Robertson, N.R.*
AU  - Rayner, N.W.*
AU  - Cingolani, P.*
AU  - Locke, A.E.*
AU  - Tajes, J.F.*
AU  - Highland, H.M.*
AU  - Dupuis, J.*
AU  - Chines, P.S.*
AU  - Lindgren, C.M.*
AU  - Hartl, C.*
AU  - Jackson, A.U.*
AU  - Chen, H.*
AU  - Huyghe, J.R.*
AU  - de Bunt, M.v.*
AU  - Pearson, R.D.*
AU  - Kumar, A.*
AU  - Müller-Nurasyid, M.
AU  - Grarup, N.*
AU  - Stringham, H.M.*
AU  - Gamazon, E.R.*
AU  - Lee, J.*
AU  - Chen, Y.*
AU  - Scott, R.A.*
AU  - Below, J.E.*
AU  - Chen, P.*
AU  - Huang, J.*
AU  - Go, M.J.*
AU  - Stitzel, M.L.*
AU  - Pasko, D.*
AU  - Parker, S.C.J.*
AU  - Varga, T.V.*
AU  - Green, T.*
AU  - Beer, N.L.*
AU  - Day-Williams, A.G.*
AU  - Ferreira, T.*
AU  - Fingerlin, T.E.*
AU  - Horikoshi, M.*
AU  - Hu, C.*
AU  - Huh, I.*
AU  - Ikram, M.K.*
AU  - Kim, B.*
AU  - Kim, Y.*
AU  - Kim, Y.J.*
AU  - Kwon, M.S.*
AU  - Lee, S.*
AU  - Lin, K.*
AU  - Maxwell, T.J.*
AU  - Nagai, Y.*
AU  - Wang, X.*
AU  - Welch, R.P.*
AU  - Yoon, J.*
AU  - Zhang, W.*
AU  - Barzilai, N.*
AU  - Voight, B.F.*
AU  - Han, B.*
AU  - Jenkinson, C.P.*
AU  - Kuulasmaa, T.*
AU  - Kuusisto, J.*
AU  - Manning, A.*
AU  - Ng, M.C.Y.*
AU  - Palmer, N.D.*
AU  - Balkau, B.*
AU  - Stancáková, A.*
AU  - Abboud, H.E.*
AU  - Boeing, H.*
AU  - Giedraitis, V.*
AU  - Prabhakaran, D.*
AU  - Gottesman, O.*
AU  - Scott, J.*
AU  - Carey, J.*
AU  - Kwan, P.*
AU  - Grant, G.B.*
AU  - Smith, J.D.*
AU  - Neale, B.M.*
AU  - Purcell, S.*
AU  - Butterworth, A.S.*
AU  - Howson, J.M.M.*
AU  - Lee, H.M.*
AU  - Lu, Y.*
AU  - Kwak, S.H.*
AU  - Zhao, W.*
AU  - Danesh, J.*
AU  - Lam, V.K.L.*
AU  - Park, K.S.*
AU  - Saleheen, D.*
AU  - So, W.Y.*
AU  - Tam, C.H.T.*
AU  - Afzal, U.*
AU  - Aguilar, D.*
AU  - Arya, R.*
AU  - Aung, T.*
AU  - Chan, E.*
AU  - Navarro, C.*
AU  - Cheng, C.*
AU  - Palli, D.*
AU  - Correa, A.*
AU  - Curran, J.E.*
AU  - Rybin, D.*
AU  - Farook, V.S.*
AU  - Fowler, S.P.*
AU  - Freedman, B.I.*
AU  - Griswold, M.E.*
AU  - Hale, D.E.*
AU  - Hicks, P.J.*
AU  - Khor, C.C.*
AU  - Kumar, S.*
AU  - Lehne, B.*
AU  - Thuillier, D.*
AU  - Lim, W.Y.*
AU  - Liu, J.*
AU  - Loh, M.*
AU  - Musani, S.K.*
AU  - Puppala, S.*
AU  - Scott, W.R.*
AU  - Yengo, L.*
AU  - Tan, S.*
AU  - Taylor, H.A.*
AU  - Thameem, F.*
AU  - Wilson, G.*
AU  - Wong, T.Y.*
AU  - Njolstad, P.R.*
AU  - Levy, J.C.*
AU  - Mangino, M.*
AU  - Bonnycastle, L.L.*
AU  - Schwarzmayr, T.
AU  - Fadista, J.*
AU  - Surdulescu, G.L.*
AU  - Herder, C.*
AU  - Groves, C.J.*
AU  - Wieland, T.
AU  - Bork-Jensen, J.*
AU  - Brandslund, I.*
AU  - Christensen, C.*
AU  - Koistinen, H.A.*
AU  - Doney, A.S.F.*
AU  - Kinnunen, L.*
AU  - Esko, T.*
AU  - Farmer, A.J.*
AU  - Hakaste, L.*
AU  - Hodgkiss, D.*
AU  - Kravic, J.*
AU  - Lyssenko, V.*
AU  - Hollensted, M.*
AU  - Jorgensen, M.E.*
AU  - Jorgensen, T.*
AU  - Ladenvall, C.*
AU  - Justesen, J.M.*
AU  - Käräjämäki, A.*
AU  - Kriebel, J.
AU  - Rathmann, W.*
AU  - Lannfelt, L.*
AU  - Lauritzen, T.*
AU  - Narisu, N.*
AU  - Linneberg, A.*
AU  - Melander, O.*
AU  - Milani, L.*
AU  - Neville, M.*
AU  - Orho-Melander, M.*
AU  - Qi, L.*
AU  - Qi, Q.*
AU  - Roden, M.*
AU  - Rolandsson, O.*
AU  - Swift, A.*
AU  - Rosengren, A.H.*
AU  - Stirrups, K.*
AU  - Wood, A.R.*
AU  - Mihailov, E.*
AU  - Blancher, C.*
AU  - Carneiro, M.O.*
AU  - Maguire, J.*
AU  - Poplin, R.*
AU  - Shakir, K.*
AU  - Fennell, T.*
AU  - DePristo, M.*
AU  - Hrabě de Angelis, M.
AU  - Deloukas, P.*
AU  - Gjesing, A.P.*
AU  - Jun, G.*
AU  - Nilsson, P.M.*
AU  - Murphy, J.*
AU  - Onofrio, R.*
AU  - Thorand, B.
AU  - Hansen, T.*
AU  - Meisinger, C.
AU  - Hu, F.B.*
AU  - Isomaa, B.*
AU  - Karpe, F.*
AU  - Liang, L.*
AU  - Peters, A.
AU  - Huth, C.
AU  - O'Rahilly, S.P.*
AU  - Palmer, C.N.A.*
AU  - Pedersen, O.*
AU  - Rauramaa, R.*
AU  - Tuomilehto, J.*
AU  - Salomaa, V.*
AU  - Watanabe, R.M.*
AU  - Syvanen, A.C.*
AU  - Bergman, R.N.*
AU  - Bharadwaj, D.*
AU  - Bottinger, E.P.*
AU  - Cho, Y.S.*
AU  - Chandak, G.R.*
AU  - Chan, J.C.*
AU  - Chia, K.S.*
AU  - Daly, M.J.*
AU  - Ebrahim, S.B.*
AU  - Langenberg, C.*
AU  - Elliott, P.*
AU  - Jablonski, K.A.*
AU  - Lehman, D.M.*
AU  - Jia, W.*
AU  - Ma, R.C.W.*
AU  - Pollin, T.I.*
AU  - Sandhu, M.*
AU  - Tandon, N.*
AU  - Froguel, P.*
AU  - Barroso, I.*
AU  - Teo, Y.Y.*
AU  - Zeggini, E.*
AU  - Loos, R.J.F.*
AU  - Small, K.S.*
AU  - Ried, J.S.
AU  - DeFronzo, R.A.*
AU  - Grallert, H.
AU  - Glaser, B.*
AU  - Metspalu, A.*
AU  - Wareham, N.J.*
AU  - Walker, M.*
AU  - Banks, E.*
AU  - Gieger, C.
AU  - Ingelsson, E.*
AU  - Im, H.K.*
AU  - Illig, T.
AU  - Franks, P.W.*
AU  - Buck, G.*
AU  - Trakalo, J.*
AU  - Buck, D.*
AU  - Prokopenko, I.*
AU  - Mägi, R.*
AU  - Lind, L.*
AU  - Farjoun, Y.*
AU  - Owen, K.R.*
AU  - Gloyn, A.L.*
AU  - Strauch, K.
AU  - Tuomi, T.*
AU  - Kooner, J.S.*
AU  - Park, T.*
AU  - Donnelly, P.*
AU  - Morris, A.D.*
AU  - Hattersley, A.T.*
AU  - Bowden, D.W.*
AU  - Collins, F.S.*
AU  - Atzmon, G.*
AU  - Chambers, J.C.*
AU  - Spector, T.D.*
AU  - Laakso, M.*
AU  - Strom, T.M.
AU  - Bell, G.I.*
AU  - Blangero, J.*
AU  - Duggirala, R.*
AU  - Tai, E.S.*
AU  - McVean, G.*
AU  - Hanis, C.L.*
AU  - Wilson, J.G.*
AU  - Seielstad, M.*
AU  - Frayling, T.M.*
AU  - Meigs, J.B.*
AU  - Cox, N.J.*
AU  - Sladek, R.*
AU  - Lander, E.S.*
AU  - Gabriel, S.*
AU  - Mohlke, K.L.*
AU  - Meitinger, T.
AU  - Groop, L.*
AU  - Abecasis, G.*
AU  - Scott, L.J.*
AU  - Morris, A.P.*
AU  - Kang, H.M.*
AU  - Altshuler, D.*
AU  - Burtt, N.P.*
AU  - Florez, J.C*
AU  - Boehnke, M.*
AU  - McCarthy, M.I.*
C1  - 52898
C2  - 44248
CY  - London
TI  - Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.
JO  - Sci. Data
VL  - 5
PB  - Nature Publishing Group
PY  - 2018
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
AU  - Flannick, J.*
AU  - Fuchsberger, C.*
AU  - Mahajan, A.*
AU  - Teslovich, T.M.*
AU  - Agarwala, V.*
AU  - Gaulton, K.J.*
AU  - Caulkins, L.*
AU  - Koesterer, R.*
AU  - Ma, C.*
AU  - Moutsianas, L.*
AU  - McCarthy, D.J.*
AU  - Rivas, M.A.*
AU  - Perry, J.R.B.*
AU  - Sim, X.*
AU  - Blackwell, T.W.*
AU  - Robertson, N.R.*
AU  - Rayner, N.W.*
AU  - Cingolani, P.*
AU  - Locke, A.E.*
AU  - Tajes, J.F.*
AU  - Highland, H.M.*
AU  - Dupuis, J.*
AU  - Chines, P.S.*
AU  - Lindgren, C.M.*
AU  - Hartl, C.*
AU  - Jackson, A.U.*
AU  - Chen, H.*
AU  - Huyghe, J.R.*
AU  - van de Bunt, M.*
AU  - Pearson, R.D.*
AU  - Kumar, A.*
AU  - Müller-Nurasyid, M.
AU  - Grarup, N.*
AU  - Stringham, H.M.*
AU  - Gamazon, E.R.*
AU  - Lee, J.*
AU  - Chen, Y.*
AU  - Scott, R.A.*
AU  - Below, J.E.*
AU  - Chen, P.*
AU  - Huang, J.*
AU  - Go, M.J.*
AU  - Stitzel, M.L.*
AU  - Pasko, D.*
AU  - Parker, S.C.J.*
AU  - Varga, T.V.*
AU  - Green, T.*
AU  - Beer, N.L.*
AU  - Day-Williams, A.G.*
AU  - Ferreira, T.*
AU  - Fingerlin, T.E.*
AU  - Horikoshi, M.*
AU  - Hu, C.*
AU  - Huh, I.*
AU  - Ikram, M.K.*
AU  - Kim, B.J.*
AU  - Kim, Y.*
AU  - Kim, Y.J.*
AU  - Kwon, M.S.*
AU  - Lee, S.*
AU  - Lin, K.H.*
AU  - Maxwell, T.J.*
AU  - Nagai, Y.*
AU  - Wang, X.*
AU  - Welch, R.P.*
AU  - Yoon, J.*
AU  - Zhang, W.*
AU  - Barzilai, N.*
AU  - Voight, B.F.*
AU  - Han, B.G.*
AU  - Jenkinson, C.P.*
AU  - Kuulasmaa, T.*
AU  - Kuusisto, J.*
AU  - Manning, A.*
AU  - Ng, M.C.Y.*
AU  - Palmer, N.D.*
AU  - Balkau, B.*
AU  - Stancáková, A.*
AU  - Abboud, H.E.*
AU  - Boeing, H.*
AU  - Giedraitis, V.*
AU  - Prabhakaran, D.*
AU  - Gottesman, O.*
AU  - Scott, J.*
AU  - Carey, J.*
AU  - Kwan, P.*
AU  - Grant, G.B.*
AU  - Smith, J.D.*
AU  - Neale, B.M.*
AU  - Purcell, S.*
AU  - Butterworth, A.S.*
AU  - Howson, J.M.M.*
AU  - Lee, H.M.*
AU  - Lu, Y.*
AU  - Kwak, S.H.*
AU  - Zhao, W.*
AU  - Danesh, J.*
AU  - Lam, V.K.L.*
AU  - Park, K.S.*
AU  - Saleheen, D.*
AU  - So, W.Y.*
AU  - Tam, C.H.T.*
AU  - Afzal, U.*
AU  - Aguilar, D.*
AU  - Arya, R.*
AU  - Aung, T.*
AU  - Chan, E.*
AU  - Navarro, C.*
AU  - Cheng, C.Y.*
AU  - Palli, D.*
AU  - Correa, A.*
AU  - Curran, J.E.*
AU  - Rybin, D.*
AU  - Farook, V.S.*
AU  - Fowler, S.P.*
AU  - Freedman, B.I.*
AU  - Griswold, M.E.*
AU  - Hale, D.E.*
AU  - Hicks, P.J.*
AU  - Khor, C.C.*
AU  - Kumar, S.*
AU  - Lehne, B.*
AU  - Thuillier, D.*
AU  - Lim, W.Y.*
AU  - Liu, J.*
AU  - Loh, M.*
AU  - Musani, S.K.*
AU  - Puppala, S.*
AU  - Scott, W.R.*
AU  - Yengo, L.*
AU  - Tan, S.T.*
AU  - Taylor, H.A.*
AU  - Thameem, F.*
AU  - Wilson, G.*
AU  - Wong, T.Y.*
AU  - Njølstad, P.R.*
AU  - Levy, J.C.*
AU  - Mangino, M.*
AU  - Bonnycastle, L.L.*
AU  - Schwarzmayr, T.
AU  - Fadista, J.*
AU  - Surdulescu, G.L.*
AU  - Herder, C.*
AU  - Groves, C.J.*
AU  - Wieland, T.
AU  - Bork-Jensen, J.*
AU  - Brandslund, I.*
AU  - Christensen, C.*
AU  - Koistinen, H.A.*
AU  - Doney, A.S.F.*
AU  - Kinnunen, L.*
AU  - Esko, T.*
AU  - Farmer, A.J.*
AU  - Hakaste, L.*
AU  - Hodgkiss, D.*
AU  - Kravic, J.*
AU  - Lyssenko, V.*
AU  - Hollensted, M.*
AU  - Jørgensen, M.E.*
AU  - Jørgensen, T.*
AU  - Ladenvall, C.*
AU  - Justesen, J.M.*
AU  - Käräjämäki, A.*
AU  - Kriebel, J.
AU  - Rathmann, W.*
AU  - Lannfelt, L.*
AU  - Lauritzen, T.*
AU  - Narisu, N.*
AU  - Linneberg, A.*
AU  - Melander, O.*
AU  - Milani, L.*
AU  - Neville, M.*
AU  - Orho-Melander, M.*
AU  - Qi, L.*
AU  - Qi, Q.*
AU  - Roden, M.*
AU  - Rolandsson, O.*
AU  - Swift, A.*
AU  - Rosengren, A.H.*
AU  - Stirrups, K.*
AU  - Wood, A.R.*
AU  - Mihailov, E.*
AU  - Blancher, C.*
AU  - Carneiro, M.O.*
AU  - Maguire, J.*
AU  - Poplin, R.*
AU  - Shakir, K.*
AU  - Fennell, T.*
AU  - DePristo, M.*
AU  - Hrabě de Angelis, M.
AU  - Deloukas, P.*
AU  - Gjesing, A.P.*
AU  - Jun, G.*
AU  - Nilsson, P.*
AU  - Murphy, J.*
AU  - Onofrio, R.*
AU  - Thorand, B.
AU  - Hansen, T.*
AU  - Meisinger, C.
AU  - Hu, F.B.*
AU  - Isomaa, B.*
AU  - Karpe, F.*
AU  - Liang, L.*
AU  - Peters, A.
AU  - Huth, C.
AU  - O'Rahilly, S.P.*
AU  - Palmer, C.N.A.*
AU  - Pedersen, O.*
AU  - Rauramaa, R.*
AU  - Tuomilehto, J.*
AU  - Salomaa, V.*
AU  - Watanabe, R.M.*
AU  - Syvänen, A.C.*
AU  - Bergman, R.N.*
AU  - Bharadwaj, D.*
AU  - Bottinger, E.P.*
AU  - Cho, Y.S.*
AU  - Chandak, G.R.*
AU  - Chan, J.C.*
AU  - Chia, K.S.*
AU  - Daly, M.J.*
AU  - Ebrahim, S.B.*
AU  - Langenberg, C.*
AU  - Elliott, P.*
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AU  - DeFronzo, R.A.*
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AU  - Prokopenko, I.*
AU  - Mägi, R.*
AU  - Lind, L.*
AU  - Farjoun, Y.*
AU  - Owen, K.R.*
AU  - Gloyn, A.L.*
AU  - Strauch, K.
AU  - Tuomi, T.*
AU  - Kooner, J.S.*
AU  - Lee, J.Y.*
AU  - Park, T.*
AU  - Donnelly, P.*
AU  - Morris, A.D.*
AU  - Hattersley, A.T.*
AU  - Bowden, D.W.*
AU  - Collins, F.S.*
AU  - Atzmon, G.*
AU  - Chambers, J.C.*
AU  - Spector, T.D.*
AU  - Laakso, M.*
AU  - Strom, T.M.
AU  - Bell, G.I.*
AU  - Blangero, J.*
AU  - Duggirala, R.*
AU  - Tai, E.S.*
AU  - McVean, G.*
AU  - Hanis, C.L.*
AU  - Wilson, J.G.*
AU  - Seielstad, M.*
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AU  - Meigs, J.B.*
AU  - Cox, N.J.*
AU  - Sladek, R.*
AU  - Lander, E.S.*
AU  - Gabriel, S.*
AU  - Mohlke, K.L.*
AU  - Meitinger, T.
AU  - Groop, L.*
AU  - Abecasis, G.*
AU  - Scott, L.J.*
AU  - Morris, A.P.*
AU  - Kang, H.M.*
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AU  - Boehnke, M.*
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C1  - 52571
C2  - 44032
CY  - London
TI  - Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.
JO  - Sci. Data
VL  - 4
PB  - Nature Publishing Group
PY  - 2017
SN  - 2052-4463
ER  - 

TY  - JOUR
AB  - Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.
AU  - St John-Williams, L.*
AU  - Blach, C.*
AU  - Toledo, J.B.*
AU  - Rotroff, D.M.*
AU  - Kim, S.*
AU  - Klavins, K.*
AU  - Baillie, R.A.*
AU  - Han, X.*
AU  - MahmoudianDehkordi, S.*
AU  - Jack, J.R.*
AU  - Massaro, T.J.*
AU  - Lucas, J.E.*
AU  - Louie, G.*
AU  - Motsinger-Reif, A.A.*
AU  - Risacher, S.L.*
AU  - Saykin, A.J.*
AU  - Kastenmüller, G.
AU  - Arnold, M.
AU  - Koal, T.*
AU  - Moseley, M.A.*
AU  - Mangravite, L.M.*
AU  - Peters, M.A.*
AU  - Tenenbaum, J.D.*
AU  - Thompson, K.*
AU  - Kaddurah-Daouk, R.*
C1  - 52159
C2  - 43806
CY  - London
TI  - Targeted metabolomics and medication classification data from participants in the ADNI1 cohort.
JO  - Sci. Data
VL  - 4
PB  - Nature Publishing Group
PY  - 2017
SN  - 2052-4463
ER  -