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  • 2020-2024  (166)
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  • 1
    Call number: AWI G3-22-94687
    Description / Table of Contents: Permafrost is warming globally, which leads to widespread permafrost thaw and impacts the surrounding landscapes, ecosystems and infrastructure. Especially ice-rich permafrost is vulnerable to rapid and abrupt thaw, resulting from the melting of excess ground ice. Local remote sensing studies have detected increasing rates of abrupt permafrost disturbances, such as thermokarst lake change and drainage, coastal erosion and RTS in the last two decades. All of which indicate an acceleration of permafrost degradation. In particular retrogressive thaw slumps (RTS) are abrupt disturbances that expand by up to several meters each year and impact local and regional topographic gradients, hydrological pathways, sediment and nutrient mobilisation into aquatic systems, and increased permafrost carbon mobilisation. The feedback between abrupt permafrost thaw and the carbon cycle is a crucial component of the Earth system and a relevant driver in global climate models. However, an assessment of RTS at high temporal resolution to determine the ...
    Type of Medium: Dissertations
    Pages: xxiv, 134 Seiten , Illustrationen, Diagramme, Karten
    Language: English
    Note: Dissertation, Universität Potsdam, 2021 , Table of Contents Abstract Zusammenfassung List of Figures List of Tables Abbreviations 1 Introduction 1.1 Scientific background and motivation 1.1.1 Permafrost and climate change 1.1.2 Permafrost thaw and disturbances 1.1.3 Abrupt permafrost disturbances 1.1.4 Remote sensing 1.1.5 Remote sensing of permafrost disturbances 1.2 Aims and objectives 1.3 Study area 1.4 General data and methods 1.4.1 Landsat and Sentinel-2 1.4.2 Google Earth Engine 1.5 Thesis structure 1.6 Overview of publications and authors’ contribution 1.6.1 Chapter 2 - Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions 1.6.2 Chapter 3 - Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions 1.6.3 Chapter 4 - Remote Sensing Annual Dynamics of Rapid Permafrost Thaw Disturbances with LandTrendr 2 Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions 2.1 Abstract 2.2 Introduction 2.3 Materials and Methods 2.3.1 Study Sites 2.3.2 Data 2.3.3 Data Processing 2.3.3.1 Filtering Image Collections 2.3.3.2 Creating L8, S2, and Site Masks 2.3.3.3 Preparing Sentinel-2 Surface Reflectance Images in SNAP 2.3.3.4 Applying Site Masks 2.3.4 Spectral Band Comparison and Adjustment 2.4 Results 2.4.1 Spectral Band Comparison 2.4.2 Spectral Band Adjustment 2.4.3 ES and HLS Spectral Band Adjustment 2.5 Discussion 2.6 Conclusions 2.7 Acknowledgements 2.8 Appendix Chapter 2 3 Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions 3.1 Abstract 3.2 Introduction 3.3 Materials and Methods 3.3.1 Study Sites 3.3.2 Data 3.3.3 Data Processing and Mosaicking Workflow 3.3.4 Data Availability Assessment 3.3.5 Mosaic Coverage and Quality Assessment 3.4 Results 3.4.1 Data Availability Assessment 3.4.2 Mosaic Coverage and Quality Assessment 3.5 Discussion 3.6 Conclusions 4 Remote Sensing Annual Dynamics of Rapid Permafrost Thaw Disturbances with LandTrendr 4.1 Abstract 4.2 Introduction 4.3 Study Area and Methods 4.3.1 Study area 4.3.2 General workflow and ground truth data 4.3.3 Data and LandTrendr 4.3.4 Index selection 4.3.5 Temporal Segmentation 4.3.6 Spectral Filtering 4.3.7 Spatial masking and filtering 4.3.8 Machine-learning object filter 4.4 Results 4.4.1 Focus sites 4.4.2 North Siberia 4.5 Discussion 4.5.1 Mapping of RTS 4.5.2 Spatio-temporal variability of RTS dynamics 4.5.3 LT-LS2 capabilities and limitations 4.6 Conclusion 4.7 Appendix 5 Synthesis and Discussion 5.1 Google Earth Engine 5.2 Landsat and Sentinel-2 5.3 Image mosaics and disturbance detection algorithm 5.4 Mapping RTS and their annual temporal dynamics 5.5 Limitations and technical considerations 5.6 Key findings 5.7 Outlook References Acknowledgements
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  • 2
    Call number: AWI G8-23-95167
    Description / Table of Contents: The Arctic nearshore zone plays a key role in the carbon cycle. Organic-rich sediments get eroded off permafrost affected coastlines and can be directly transferred to the nearshore zone. Permafrost in the Arctic stores a high amount of organic matter and is vulnerable to thermo-erosion, which is expected to increase due to climate change. This will likely result in higher sediment loads in nearshore waters and has the potential to alter local ecosystems by limiting light transmission into the water column, thus limiting primary production to the top-most part of it, and increasing nutrient export from coastal erosion. Greater organic matter input could result in the release of greenhouse gases to the atmosphere. Climate change also acts upon the fluvial system, leading to greater discharge to the nearshore zone. It leads to decreasing sea-ice cover as well, which will both increase wave energy and lengthen the open-water season. Yet, knowledge on these processes and the resulting impact on the nearshore zone is scarce, because access to and instrument deployment in the nearshore zone is challenging. Remote sensing can alleviate these issues in providing rapid data delivery in otherwise non-accessible areas. However, the waters in the Arctic nearshore zone are optically complex, with multiple influencing factors, such as organic rich suspended sediments, colored dissolved organic matter (cDOM), and phytoplankton. The goal of this dissertation was to use remotely sensed imagery to monitor processes related to turbidity caused by suspended sediments in the Arctic nearshore zone. In-situ measurements of water-leaving reflectance and surface water turbidity were used to calibrate a semi-empirical algorithm which relates turbidity from satellite imagery. Based on this algorithm and ancillary ocean and climate variables, the mechanisms underpinning nearshore turbidity in the Arctic were identified at a resolution not achieved before. The calibration of the Arctic Nearshore Turbidity Algorithm (ANTA) was based on in-situ measurements from the coastal and inner-shelf waters around Herschel Island Qikiqtaruk (HIQ) in the western Canadian Arctic from the summer seasons 2018 and 2019. It performed better than existing algorithms, developed for global applications, in relating turbidity from remotely sensed imagery. These existing algorithms were lacking validation data from permafrost affected waters, and were thus not able to reflect the complexity of Arctic nearshore waters. The ANTA has a higher sensitivity towards the lowest turbidity values, which is an asset for identifying sediment pathways in the nearshore zone. Its transferability to areas beyond HIQ was successfully demonstrated using turbidity measurements matching satellite image recordings from Adventfjorden, Svalbard. The ANTA is a powerful tool that provides robust turbidity estimations in a variety of Arctic nearshore environments. Drivers of nearshore turbidity in the Arctic were analyzed by combining ANTA results from the summer season 2019 from HIQ with ocean and climate variables obtained from the weather station at HIQ, the ERA5 reanalysis database, and the Mackenzie River discharge. ERA5 reanalysis data were obtained as domain averages over the Canadian Beaufort Shelf. Nearshore turbidity was linearly correlated to wind speed, significant wave height and wave period. Interestingly, nearshore turbidity was only correlated to wind speed at the shelf, but not to the in-situ measurements from the weather station at HIQ. This shows that nearshore turbidity, albeit being of limited spatial extent, gets influenced by the weather conditions multiple kilometers away, rather than in its direct vicinity. The large influence of wave energy on nearshore turbidity indicates that freshly eroded material off the coast is a major contributor to the nearshore sediment load. This contrasts results from the temperate and tropical oceans, where tides and currents are the major drivers of nearshore turbidity. The Mackenzie River discharge was not identified as a driver of nearshore turbidity in 2019, however, the analysis of 30 years of Landsat archive imagery from 1986 to 2016 suggests a direct link between the prevailing wind direction, which heavily influences the Mackenzie River plume extent, and nearshore turbidity around HIQ. This discrepancy could be caused by the abnormal discharge behavior of the Mackenzie River in 2019. This dissertation has substantially advanced the understanding of suspended sediment processes in the Arctic nearshore zone and provided new monitoring tools for future studies. The presented results will help to understand the role of the Arctic nearshore zone in the carbon cycle under a changing climate.
    Type of Medium: Dissertations
    Pages: xv, ii, 85, xvii Seiten , Illustrationen, Diagramme, Karten
    Language: English
    Note: Dissertation, Universität Potsdam, 2022 (kumulative Dissertation) , TABLE OF CONTENTS Abstract Zusammenfassung Allgemeinverständliche Zusammenfassung List of Figures List of Tables Funding Chapter 1 Introduction 1.1 Scientific Background 1.1.1 Arctic Climate Change 1.1.2 The Arctic Nearshore Zone 1.1.3 Ocean Color Remote Sensing 1.2 Objectives 1.3 Study Area 1.4 Methods 1.4.1 Field Sampling 1.4.2 Data Processing 1.4.3 Satellite Imagery Processing 1.5 Thesis Structure 1.6 Author Contributions Chapter 2 Long-Term High-Resolution Sediment and Sea Surface Temperature Spatial Patterns in Arctic Nearshore Waters retrived using 30-year Landsat Archive Imagery 2.1 Abstract 2.2 Introduction 2.3 Material and Methods 2.3.1 Regional Setting 2.3.2 Landsat Images Acquisition and Processing 2.3.3 Landsat Turbidity Retrieval 2.3.4 Transects in the nearshore zone 2.3.5 Wind Data 2.4 Results 2.4.1 Brightness Temperature 2.4.2 Surface Reflectance and Turbidity Mapping 2.4.3 Gradients in the nearshore zone 2.5 Discussion 2.6 Conclusion Appendix A Chapter 3 The Arctic Nearshore Turbidity Algorithm (ANTA) - A Multi Sensor Turbidity Algorithm for Arctic Nearshore Environments 3.1 Abstract 3.2 Introduction 3.3 Methods 3.3.1 Regional setting 3.3.2 In-situ sampling 3.3.3 Optical data processing 3.3.4 Algorithm tuning 3.3.5 Satellite imagery processing 3.4 Results and Discussion 3.4.1 Turbidity and SPM 3.4.2 ANTA performance 3.4.3 Comparison with the Dogliotti et al., (2015) algorithm 3.4.4 Test and transfer of the ANTA 3.5 Conclusion Chapter 4 Drivers of Turbidity and its Seasonal Variability in the Nearshore Zone of Herschel Island Qikiqtaruk (western Canadian Arctic) 4.1 Abstract 4.2 Introduction 4.3 Methods 4.3.1 Study Area 4.3.2 Satellite Imagery 4.3.3 In-situ data 4.3.4 Reanalysis data 4.4 Results and Discussion 4.4.1 Time Series Analysis 4.4.2 Drivers of turbidity 4.5 Conclusion Chapter 5 Synthesis 5.1 Applicability of Remote Sensing Algorithms in the Arctic Nearshore Zone 5.2 Drivers of Nearshore Turbidity 5.3 Spatial Variations of Nearshore Turbidity 5.4 Challenges and Outlook List of Acronyms Bibliography Danksagung
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  • 3
    Publication Date: 2024-02-07
    Description: Permafrost degradation in the catchment of major Siberian rivers, combined with higher precipitation in a warming climate, could increase the flux of terrestrially derived dissolved organic matter (tDOM) into the Arctic Ocean (AO). Each year, ∼ 7.9 Tg of dissolved organic carbon (DOC) is discharged into the AO via the three largest rivers that flow into the Laptev Sea (LS) and East Siberian Sea (ESS). A significant proportion of this tDOM-rich river water undergoes at least one freeze–melt cycle in the land-fast ice that forms along the coast of the Laptev and East Siberian seas in winter. To better understand how growth and melting of land-fast ice affect dissolved organic matter (DOM) dynamics in the LS and ESS, we determined DOC concentrations and the optical properties of coloured dissolved organic matter (CDOM) in sea ice, river water and seawater. The data set, covering different seasons over a 9-year period (2010–2019), was complemented by oceanographic measurements (T, S) and determination of the oxygen isotope composition of the seawater. Although removal of tDOM cannot be ruled out, our study suggests that conservative mixing of high-tDOM river water and sea-ice meltwater with low-tDOM seawater is the major factor controlling the surface distribution of tDOM in the LS and ESS. A case study based on data from winter 2012 and spring 2014 reveals that the mixing of about 273 km3 of low-tDOM land-fast-ice meltwater (containing ∼ 0.3 Tg DOC) with more than 200 km3 of high-tDOM Lena River water discharged during the spring freshet (∼ 2.8 Tg DOC yr−1) plays a dominant role in this respect. The mixing of the two low-salinity surface water masses is possible because the meltwater and the river water of the spring freshet flow into the southeastern LS at the same time every year (May–July). In addition, budget calculations indicate that in the course of the growth of land-fast ice in the southeastern LS, ∼ 1.2 Tg DOC yr−1 (± 0.54 Tg) can be expelled from the growing ice in winter, together with brines. These DOC-rich brines can then be transported across the shelves into the Arctic halocline and the Transpolar Drift Current flowing from the Siberian Shelf towards Greenland. The study of dissolved organic matter dynamics in the AO is important not only to decipher the Arctic carbon cycle but also because CDOM regulates physical processes such as radiative forcing in the upper ocean, which has important effects on sea surface temperature, water column stratification, biological productivity and UV penetration.
    Type: Article , PeerReviewed
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  • 4
    Publication Date: 2024-02-07
    Description: Here we describe the LegacyPollen 1.0, a dataset of 2831 fossil pollen records with metadata, a harmonized taxonomy, and standardized chronologies. A total of 1032 records originate from North America, 1075 from Europe, 488 from Asia, 150 from Latin America, 54 from Africa, and 32 from the Indo-Pacific. The pollen data cover the late Quaternary (mostly the Holocene). The original 10 110 pollen taxa names (including variations in the notations) were harmonized to 1002 terrestrial taxa (including Cyperaceae), with woody taxa and major herbaceous taxa harmonized to genus level and other herbaceous taxa to family level. The dataset is valuable for synthesis studies of, for example, taxa areal changes, vegetation dynamics, human impacts (e.g., deforestation), and climate change at global or continental scales. The harmonized pollen and metadata as well as the harmonization table are available from PANGAEA (https://doi.org/10.1594/PANGAEA.929773; Herzschuh et al., 2021). R code for the harmonization is provided at Zenodo (https://doi.org/10.5281/zenodo.5910972; Herzschuh et al., 2022) so that datasets at a customized harmonization level can be easily established.
    Type: Article , PeerReviewed
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  • 5
    Publication Date: 2023-06-27
    Description: This data set is part of a larger data harmonization effort to make lake sediment core data machine readable and comparable. Here we standardized radiocarbon age data of sediment core 16-KP-03-L10_Long_1, retrieved in 2016 from Lake Nutenvut (Chukotka, Russia). The glacial lake Nutenvut is in an exorheic basin in the tundra-taiga transition zone. It lies at an elevation of ca. 654 m a.s.l. with a surface area of ca. 9.2 km2 and a maximum lake water depth of estimated 60 m. The 1.26 m sediment core was retrieved by a UWITEC hammer action gravity corer during the RU-Land_2016_Keperveem expedition of the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI, Germany, Potsdam) in cooperation with the North Eastern Federal State University (NEFU, Russia, Yakutsk). Radiocarbon data have been analysed in Poznan and at AWI Bremerhaven (MICADAS).
    Keywords: 16-KP-03-L10_Long_1; Age, 14C AMS; Age, dated; Age, dated material; Age, dated standard error; AWI_Envi; AWI Arctic Land Expedition; Carbon; Category; DEPTH, sediment/rock; Hammer-modified gravity core, UWITEC; HGCUWI; Keperveem_2016; Laboratory; Laboratory code/label; Measurement identification; Polar Terrestrial Environmental Systems @ AWI; Pretreatment; Reservoir age; Reservoir age, standard error; RU-Land_2016_Keperveem; Thickness; Tschukotka, Sibiria, Russia
    Type: Dataset
    Format: text/tab-separated-values, 202 data points
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  • 6
    Publication Date: 2023-07-11
    Description: This data set is part of a larger data harmonization effort to make lake sediment core data machine readable and comparable. Here we standardized grain-size data of sediment core 16-KP-03-L10_Long_1, retrieved in 2016 from Lake Nutenvut (Chukotka, Russia). The glacial lake Nutenvut is in an exorheic basin in the tundra-taiga transition zone. It lies at an elevation of ca. 654 m a.s.l. with a surface area of ca. 9.2 km2 and a maximum lake water depth of estimated 60 m. The 1.26 m sediment core was retrieved by a UWITEC hammer action gravity corer during the RU-Land_2016_Keperveem expedition of the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI, Germany, Potsdam) in cooperation with the North Eastern Federal State University (NEFU, Russia, Yakutsk). Grain-size was measured by laser-based particle sizing at AWI Potsdam.
    Keywords: 16-KP-03-L10_Long_1; AWI_Envi; AWI Arctic Land Expedition; Clay; DEPTH, sediment/rock; Gravel; Hammer-modified gravity core, UWITEC; HGCUWI; Keperveem_2016; Laser particle size analyzer; Measurement identification; Polar Terrestrial Environmental Systems @ AWI; RU-Land_2016_Keperveem; Sand; Silt; Size fraction 0.0063-0.002 mm, fine silt; Size fraction 0.020-0.0063 mm, medium silt; Size fraction 0.063-0.020 mm, coarse silt; Size fraction 0.200-0.063 mm, fine sand; Size fraction 0.630-0.200 mm, medium sand; Size fraction 2.000-0.630 mm, coarse sand; Tschukotka, Sibiria, Russia
    Type: Dataset
    Format: text/tab-separated-values, 319 data points
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  • 7
    Publication Date: 2023-11-01
    Description: This data set is part of a larger data harmonization effort to make lake sediment core data machine readable and comparable. Here we standardized diatom data of sediment core PG1972, retrieved in 2009 from Lake Bezrybnoe (Lena Delta, Russia) at 4.7 m water depth. The thermokarst lake Bezrybnoe is small basin in tundra region and has one outflow and three inflows. It lies at an elevation of ca. 6 m a.s.l. with a surface area of ca. 0.77 km2 and a maximum lake water depth of estimated 5.3 m. The 1.08 m sediment core was retrieved by a UWITEC hammer action gravity corer (60mm) during the RU-Land_2009_Lena-transect expedition of the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI, Germany, Potsdam) in cooperation with the North Eastern Federal State University (NEFU, Russia, Yakutsk). Pollen have been identified by Larisa Saveljeva using a light microscope at AWI Potsdam.
    Keywords: 09-TIK-03; Alnaster; Apiaceae; Armeria; Artemisia; Asteraceae; AWI_Envi; AWI Arctic Land Expedition; Betula sect. Albae; Betula sect. Nanae; Brassicaceae; Caryophyllaceae; Chenopodiaceae; Cyperaceae; DEPTH, sediment/rock; Encalypta; Equisetum; Ericales; Fabaceae; Hand push corer; HSR; Huperzia selago; Indeterminata; Lamiaceae; Larix; Light microscope; Lycopodium sp.; Measurement identification; Papaver; Pediastrum; Pedicularis; PG1972-1; Picea; Pinus subgen. Haploxylon; Poaceae; Polar Terrestrial Environmental Systems @ AWI; Polemonium boreale; Polygonum; Polygonum bistorta; Polypodiaceae; Potentilla; Ranunculaceae; Ranunculus sp.; Rosaceae; Rubus chamaemorus; RU-Land_2009_Lena-transect; Rumex; Salix; Saxifraga sp.; Sediment core; Selaginella sibirica; Sphagnum; Taraxacum; Thalictrum; Tiksi2009
    Type: Dataset
    Format: text/tab-separated-values, 1118 data points
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  • 8
    Publication Date: 2023-08-29
    Keywords: 11-CH-12A; 880; ACHIT8; ACHITNUR; Achit-Nur, Mongolia; ACHITNUR8; Achit Nur 8; Achit Nur Lake; ACHITNUUR; Achit Nuur; Adycha_River; Adycha River; Age, maximum/old; Age, minimum/young; AGHNAGHA; Aghnaghak; AHUNG; Ahung Co; AIBI; Aibi Lake; AKKOL; Akkol Lake; AKTEREK; Ak Terek; AKULININ; Akulinin Exposure P1282; AL3; ALMALOU3; ALUT; Alut Lake; AMBA; Amba River; AMGUEMA1; AMGUEMA2; Amguema River 1; Amguema River 2; Anguli Nur Lake; ANGUNUR; ARAL86; Arctic Ocean; Asia; AWI_Envi; AWI Arctic Land Expedition; AYONGWA; Ayongwama Co; Baahar Nuur Lake; BAAHNUUR; BAIDARA; Baidara, Russia; Baihe; BAIHE; Baika; BAIKA2; Baikal_Lake-CON01-603-5; Baikal_Lake-CON01-605-3; Baikal_Lake-CON01-605-5; BAIKAL606-3; Baikal CON01-606-3; Baikal Lake-CON01-603-5; Baikal Lake-CON01-605-3; Baikal Lake-CON01-605-5; Baiyangdian Lake GY; BAIYANGGY; BAJIAO; Bajiaotian; Bakaly; BAKALY_neotoma; BALIKUN; Balikun Lake; BANGONG; Bangong Co Lake; BARABA; Barbarina_Tumsa; BARKOLBLK6E; Barkol Lake BLK06E; BAYANBY; BAYANCH; Bayanchagan Lake BY; BEIDAWA; Beidawan; BEIHAIGK10; Beihai GK10; BEILIKEK; Beilikekule Lake; Belaya Skala Exposure; BELSKALA; Berelyekh River; BETENK; Betenkyos Adycha river; Big_Yarovoe_Lake_2008-3; Big Yarovoe Lake; BLUD; Bludlivaya River; BOGUDA; BOLOTNYI; Bolotnyii Stream Exposure 117; Bolshaya Kuobakh-Baga River; Bolshaya Kuropatochya River; Bolshaya Kuropatochya River P7; Bolshoe Eravnoe Lake, Russia; Bolshoe Toko; Bolshoi Khomus; BOLSHOTO; Bosten; Boyiqiao ZK01; BOYIQZK1; BUGRIST; Bugristoe, Russia; Byllatskoye; CAMPING; CANGUMI; Cangumiao; CAOTAN2002; Caotanhu 2002; Cape Shpindler, Yugorski Peninsula, Russia; Caspian Sea; CHABADA1; CHABADA2; ChabadaII; Chabada Lake, Russia; CHADIAN; Chadianpo; Chaginskoe; CHAIWOCKF; Chaiwopu Lake CKF; Changjiang CM97; Changjiang HQ98; CHANGJICM97; CHANGJIHQ98; Changshan; CHANGSHAN; CHANGXING; Changxing Island; CHAOCH1; Chaohu Lake CH1; CHARIMUCH; Charisu/Muchang profile; Chatanga2011; CHENGCH2; Chenghai CH2; Cheremushka_Bog; Cheremushka Bog; Chernaya_Gorka; CHERNOE; Chernoe Lake, Russia; CHERNYAR; Chernyii Yar Exposure 955; Cherny Yar, Russia; CHERYAR; CHESNOK; Chesnok Peat Irtysh River; Chifeng-Qiguo Mt.; CHIFEQIGU; China; Chistoye Lake; CHITSAI; Chitsai Lake; CHUANGY; Chuangye; COMPCORE; Composite Core; Continent; Co Qongjiamong; Core1; Core13; Core2; Core20; Core86; CS98-10; DABA8; DABANUR; Daba-Nur, Mongolia; Daba Nur Lake; DABSANCK181; Dabsan Lake CK1/81; Dadiwan_2007; Dadiwan_2008; DADIWAN07; DADIWAN08; DAHAIZI; Dahaizi Lake; DAHU; Dahu Lake; DAIHAI99A; DAIHAI99B; Daihai Lake 99a; Daihai Lake DH99B; Dajiuhu_2013; DAJIUHU2013; DAJIUHUC1; Dajiuhu Lake C1; DALAINUR; Dalai Nur Lake-Haiyan; DALINURHAO; Dali Nur-Haoluku Lake; DALINURJIAN; Dali Nur-Jiangjunpaozi Lake; DALINURLIU; Dali Nur-Liuzhouwan Lake; Damagou; DAMAGOU; Dashan; DASHAN; DASHUI; Dashuitang; Daxigou; DAXIGOU; DAZIYIN; Daziying; DENGJIAC; Dengjiacun; DERPUT; Derput, Russia; Description; DIAOJIAO; Diaojiaohaizi DJ; DIMA1; DIMA2; DIMA3; DIMA4; Dingnan; DINGNAN; Dingxi; DINGXI; Dlinnoye_Lake; Dlinnoye Lake; Dolgoe_Ozero; Dolgoe Ozero; DONGDAO; Dongdaohaizi B; DONGGAN; Dongganchi; DONGGI1790; Dongguan_PK16; DONGGUPK16; DOOD4; DOODNUR; Dood-Nur, Mongolia; Dood Nur Lake; DOUCO; Douco Lake; DRILL; Drilling/drill rig; East Siberian Sea Coast 11; EBINUR; Ebinur Lake; Ebinur Lake SW; EBINURSW; EK4; ELEVATION; ELGENNYA; Elgennya Lake; Elgygytgyn_Lake_P1; Elgygytgyn_Lake_P2; Elgygytgyn crater lake, Sibiria, Russia; Elgygytgyn lake; Elgygytgyn Lake P1; Elgygytgyn Lake P2; Elikchan 4 Lake; Emanda; EMANDA; ENM109; ENMYN; Enmynveem Malyi Anyui; Enmynveem River; ENTARNOY; Entarnoye; ERAVNOE; ERHAIES; Erhai Lake ES; ERLONGWA; Erlongwan Maar Lake; Event label; Faddeyevskiy; FADEYEVS; FENGNIN; Fengning; FENZHU; Fenzhuang; fossil pollen; Foyechi; FOYECHI; GANHAI; Ganhai Lake; GANLAN; Ganlanba; Gantang; GANTANG; GAOXI; Gaoximage; GC; GCUWI; GEK; Gek Lake; Geological profile sampling; GEOPRO; GLADKOYE; Gladkoye Bog; Global River Discharge; Glukhoye Lake; GLUPEAT; GOLUBOY; Goluboye Lake; Gomishan, Islamic Republic of Iran; GONGHAI; Gonghai Lake; Gounong Co KX-1; GOUNONGKX1; Gravity corer; Gravity corer, UWITEC; GRUSHA; Grusha Lake; GS18; GUANGFU; Guangfulin; GUANGRU1; Guangrunpo 1; GUANGTA; Guangtangtou; GUCHENG; Gucheng Lake; Gulf of Tartary; GUNNUR; Gun-Nur, Mongolia; GUNNURLA; Gun Nur Lake; Gur_sample; GURSKII; Gurskii Peat; GYTGYKAI; Gytgykai Lake; HACHIHAM; Hachihama; Hachiman-Numa; HACHI-NU; Hailaer; HAILAER; Haiyuan; HAIYUAN; Halali; HALALI; HALIGU; Haligu Lake; Hamaertai Lake Ha2; HAMAHA2; HANHAIH1; Hanhai Lake H1; HARBA; Harbaling; Headwaters Opasnaya River (mine shaft); Heming_2007; HEMING2007; HEMUDUHMD1; Hemudu HMD-1; HEXIGSANY; HEXIGSHI; Hexigten-Sanyixiang; Hexigten-Shidicun; HIDDENHL1; Hidden Lake HL1; HONGSHE; Hongsheng; HONGSHU03; Hongshui River_2003; Hongyuan Baihe; HONGYUBA; HOSOIKE; Hosoike Moor; HOTON; Hoton-Nur, Mongolia; HOTONNUR2; Hoton-Nur-2; HOVSGOLC8; Hovsgol Lake C8; HUANGH24; HUANGJI; Huangjiapu; Huangsha H24; HUASHAN; Huashankou; HUASHUWO; Huashuwozi; HUBSUGUL; HUBSUGUL_neotoma; HUGUAN; Huguangyan Maar lake; HULUNHL6; Hulun Lake HL06; HULUNNUR; Hulun Nur Lake; HURLEG; HURLEGHL5-2; Hurleg Lake; Hurleg Lake HL05-2; ICDP_Elgygytgyn-Drilling-Project; ICDP5011-1; Identification; IGARKA; IKENOKOC; Ikenokochi Moor; Ilan; ILAN; Ilinka_Terrace; Ilinka Terrace; Ilirney; ILIRNEY; Indigirka_Lowland; Indigirka Lowland; Iwaya; Izylmetevskaya; JACKLOND; Jack London Lake; JIADINGP5; Jiading P5; Jiangcun; JIANGCUN; Jiangling; JIANGLING; JIANSHA; JINCHU; Jinchuan; JINGBO; Jingbo Lake; Jinsha; Julietta_Lake; Julietta Lake; JUYAN; Juyan Lake; K7/P2; KAKITU1; Kakitu Mountain; KALIST; Kalistratikha; Kamchatka2007; Kanas Lake; Kara-Bogaz Gol; Karakol; KARAKOL; KARAS; Karase Lake; Karase Lake, Kazakhstan; KARASEOZ; Karasieozerskoe, Russia; KARASYE; KARGA; Karginskii Cape; KARGOPOL; Kargopolovo; KAYAKSK; Kayakskoye Zaimitschye; KBG801; KENDEGEL; Kendegelukol Lake; Khanda; Khanda-1; KHARINEI; KHARTYM; Khlebnikova_Stream; Khlebnikova Stream; KHOMUR; KHONINNU; Khonin Nuga-River Terrace; Khorpiya; KHUISIIN; Khuisiin Lake; Kichikol; KICHIKOL; Kirek_Lake; Kirek Lake; Kirgirlakh Stream; Kiya; KNS11B; KOL; KOLYMA; Kosmodemyanskaya-1; Kosmodemyanskaya-2; Kosmodemyanskaya-3; KOTOKEL2010; KOTYR; Kotyrkol; KOTYRKOL; Kotyrkol Peat Bog, Kazakhstan; KOUCHA; Koucha Lake; KREST; Kresta Gulf; KTK1; KUAHUQ2; Kuahuqiao 2; KUBAGA; KUHAI_neotoma; KUHAI, Kuhai Lake; KULC; KULLENBERG corer; KURO11; KUROP7; KURPEAT; Kyurbe-Yuryakh-2; LAB2-95; Labaz Lake area; LAHI; Lake_Fernsehsee; Lake_Lama; Lake_Sokoch; Lake Almalou, Islamic Republic of Iran; Lake Aral, Kazakhstan; Lake Bayanchagan; Lake Billyakh, Verkhoyansk Mountains, Yakuti, Russia; Lake Boguda, Russia; Lake Fernsehsee; Lake Hill; Lake Kharinei; Lake Kotokel; Lake Kotokel_2010; Lake Lama; Lake Madjagara, Russia; Lake Nuochaga, Russia; Lake Sokoch; Lake Tianchi; Lake Ugii Nuur; Lake Urmia, Iran; Lake Zeribar, Iran; Langarud; Lanzhou; LANZHOU; LAO13-94; LAO6-96; LAOTANFA; Laotanfang; Laptev_PM9462; Laptev PM9462; LATITUDE; LEDOBOZ; Ledovyi Obryu; Ledovyi Obryv Exposure, Northern Section; Lesnaya_River; Lesnaya River; Lesnoye_Lake; Lesnoye Lake; Liaohe River-Wangxianggou; LIAOHEWANG; LINGDINGL2; Lingdingyang L2; LIUSHUW; Liushuwan; Livingstone piston corer; LL13; Location type; LONGGAN; Longgan Lake; LONGITUDE; Longquan Lake LC2; LONGQUANLC2; LOPNURK1; Lop Nur K1; LOPNURLUO4; Lop Nur Luo4; Lorino; Lower Barabaschevka River Exposure 594; LPC; LUANHAILH2; Luanhaizi LH2; LUKA; Lukaschin Yar; LUOCHU; Luochuan; LUOJI; Luojiang; Lyadhej-To; MACC; Mackereth corer; MADJAGA; MAHARLOU; Maharlou Lake, Islamic Republic of Iran; Maili; MAILI; MAIN; MAKSIMKI; Maksimkin Yar, Russia; MAKURA; Makura Moor; MALTAN; Maltan River; Malyi_Krechet_Lake; Malyi Krechet Lake; MAMENM; Manas Lake LM-1; MANASLM1; Maninskii Khartym; MANXING; Manxing Lake; MAOHEBE; Maohebei; Maqiao; MAQIAO; MAWEI32; Mawei ZK32; Maying; MAYING; Melkoye_Lake_core; Melkoye Lake; Mengcun; MENGCUN;
    Type: Dataset
    Format: text/tab-separated-values, 6869 data points
    Location Call Number Expected Availability
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  • 9
    Publication Date: 2023-08-29
    Keywords: 05-20-14; 05-21-2; 05-21-4; 05-21-5; 18 [Moraine Lake]; 38396; 3PINES; AC6107; AC6107-101; Acona; ADELINE; Adeline Lake; Age, maximum/old; Age, minimum/young; ALBANYON; Albany River Bog; Alexander Lake; Alexis Lake; ALEXISLK; ALEXLAKE; ALIUK; Aliuk Pond; AMBER; Amber Lake; ANDERS07; ANDERSLK; ANDERSON; Anderson Lake; Anderson Pond; ANDREE; Andree Bog; ANDRUSC2; Andrus Lake; ANGE; APPLEMAN; Appleman Lake; ARONOW; Aronow Bog; ARREPENT; Arrepentimientos; ARRINGTO; Arrington Marsh; ARTHURB; AT01; ATO; AWI_Envi; AXELAKE; Axe Lake; BAIRDIN; Baird Inlet [Rock Basin Lake]; BALLSTON; Ballston Lake; BALSAM; Balsam Lake; BARCHAMP; Barchampe Lake; BASEBALL; Baseball Bog; BASSK; Bass Lake; BASSNFLD; Bass Pond; BASSWOOD; Basswood Road Lake/Splan Pond; BASTIEN; Bates Marsh; BATESON; BATTAGLI; Battaglia Bog; BEARBOG; Bear Bog; Beaverhouse Lake; Beaver Lake; BEAVERWI; BEAVRHS; BELEC; Belec Lake Bog; BELMONT; Belmont Bog; Benacadie 4; BENSON; Benson Pond; BER; BER_neotoma; BERRYAN; BERRYPND; Berry Pond; BIAJACA; Big Cypress Bend [End boardwalk Fak10 endB]; BIGGSVIL; Biggsville [Cessford Quarry]; BIGJOHN; Big John Pond; Big Moose Lake; BIGREED; Big Reed Pond Hollow; BIGSANDY; Big Sandy Creek; BILLING; Billings Park; BILLYS; Billys Lake; Binnewater Pond; BINNEWTR; BLACKGMS; BLACKGUM; Black Gum Swamp; BLACKMA; BLACKPND; Black Pond; Blackwoods Hollow; Blackwood Sinkhole; BLANEYS; Blaneys Pond; BLBIGBEE; BLKWOODS; Blomidon Section MS-89-16; BLOODMA; Blood Pond; BL-Tombigbee; Blue Mounds Creek; BLUMOUND; BLWD-C2; BMOOSE_C2; BOGD; Bog D Pond; BONEYSPR; Boney Spring; Bonnett Lake; BONT14; BORDER; Border Beacon; BORIACKB; Boriack Bog; BOUC; BOUNDAR; BOUNDARY; Boundary Lake; Boundary Pond; BRANDRET; Brandreth Bog; BREWCRK1; Brewster Creek; BRI2; Brier Island Bog MS-85-22; Brisay 2; BROPHY; Brophy Ditch; BROWNC12; Brown Lake; Browns Bay, Lake Minnetonka; BROWNSBY; BROWNSLK; BROWNSPD; Browns Pond; BRULE; Buck Lake; BUCKLEY; Buckley Pond Hollow; BUCKMI; BUCYRUS; Bucyrus Bog; BURDEN; Burden Lake; Byron-Bergen Swamp (Site 1); Byron-Bergen Swamp (Site 2); BYRONMRL; BYRONSWP; CADDO1; CADDO3; Caddo Creek (Core 1); Caddo Creek (Core 3); CAHABA; Cahaba Pond; CAL; CAMEL; Camel Lake; CAMP; Camp 11 Lake; CAMP11LK; Camp 12 Lake; CAMP12LK; Camp Lake; Canyon Lake; CANYONMI; CAPITOLA; Capitola Lake; CAR; CAR_neotoma; CARIB; CARIBBOG; CARIBOU; CARIBOU_neotoma; Caribou Bog; CARTER; Carter Site; CAS; CAS_neotoma; CATACLR3; Catahoula Lake (core CLR3); CEDARBLK; Cedar Bog; Cedar Tree Neck Bog; Cenote San Jose Chulchaca; CHALCOLA; Chalco Lake; Chance Harbour Lake; CHASE; CHASE_neotoma; Chatsworth Bog; CHES2207; CHES2208; CHES2209; Chesapeake Bay, Patuxent River (2-P-5); Chesapeake Bay, Potomac River; Chesapeake Bay, Rhode River; Chesapeake Bay (MD99-2207); Chesapeake Bay (MD99-2208); Chesapeake Bay (MD99-2209); Chesapeake Bay (PTMC3-2); CHESPTMC; CHIPPEWA; Chippewa Bog; CHTSWRTH; CLEARBOG; Clear Lake; CLEARLIA; CLEARLK; CLEARPND; Clear Pond; Clearwater Bog; CLOUGH; Cobweb Swamp (Sawgrass Core); COGHILL; Coghill Lake; Collins Pond Site (PL-86-56); Colo Marsh; COLOMSH; COLPNDNS; COMPASS; Compass Pond; CONNE; Conne River; Conroy Lake; CONROYLD; Continent; COOTES; Cootes Paradise Marsh; Core; CORE; COTJ; COTTONWD; Cottonwood Lake; COWDEN; Cowden Lake; COWLES; Cowles Bog; CRANBER; Cranberry Glades; Cranberry Lake; CRANGLDS; CRATES; Crates Lake; CRAWFDC; Crawford Lake; CREELBAY; CRIDERS; Criders Pond; CRISTAL; Cristal Lake; Crooked Lake; CROOKEDN; CRYSTAL; CRYSTALIL; Crystal Lake; CTNECKB; CUB2; Cub Lake; CUMMINSA; Cummins Pond; CUPOLA; CUPOLA1; CUPOLA4; Cupola Pond; DAU; Daumont; DBATHTUB; DEADFROG; Dead Frog Pond; DECOY; Decoy Lake; DEEPFAL; Deep-Falmouth Pond; DEEPLAKE; Deep Lake; DEEPPOND; Deep Pond; DEEPTAU; Deep-Taunton Pond; DEER; Deer Lake Bog; DEL1; DEL2; Delorme 1; Delorme 2; Demont Lake; DEMONTLK; DENTONC1; Denton Lake; Description; Devils Bathtub; Devils Lake; Devils Lake, Creel Bay; DEVILSWI; DIA375; DIAB; Diana 375; DISMAL; Dismal Swamp (91); Disterhaft Farm Bog; DISTRHFT; DIVERS; Divers Lake; DONARD; Donard Lake; Douglas, Wisconsin, USA; DUCKPOND; Duck Pond; DUFRESNE; DUGASBAY; Dugas Bay Bog; DUNNIC1; Dunnigan Lake; Dye Lower Water Lake (Dyer Lake); DYER; EAGLE; EAGLEL; Eagle Lake; Eagle Lake Bog; EAGLELK; EAGLEPND; Eagle Pond; East Baltic Bog; Eastern North America; East Soldier Lake; East Twin Lake; East Whitewater Bay; EBALTIC; EDWARD; Edward Lake; E Lake; ELAKE68; ELEVATION; ELEVENS; Elevenses Lake; ELKGR-89; Elk Lake; EMILY_NE; EMRICK; Emrick Lake; ENNADA72; ENNADAI; Ennadai Lake; Ennadai Lake, 1972 Site; ERIE; ESKALAKE; Eska Lake; ESOLDIER; ETWINOH; Event label; EWHTWTR; FAKIII98; FALLISON; FALLISON_neotoma; FAR; FARN1; FAWN; Fawn Lake (CA:Ontario); FERNDAL1; FERNDALE; Ferndale Bog; FERRY01; Ferry Lake; FIRELAKE; Fire Lake; FISHL; Fish Lake; Fish Lake (Sc); FISHS; FLINFLON; Floater; FOGLAKE; Fog Lake; Fort Bragg (core PAW2); fossil pollen; Found Lake; FOUNDLK; Fox Lake; FOXMN; Frains Lake; FRAINSLK; FRBLAKE; FRENCH; French Lake; FRESH; FRESHPND; Fresh Pond; FULLER; Fuller Lake; FURNIVAL; Furnival Lake; GABRIEL; GASS; Gass Lake; GDF141; GER; Giles; GILES; GLENBORO; Glimmerglass Lake; GLIMML1; GOOSEBAY; Goose Bay Marsh; GOSHEN; Goshen Springs; GOULD; Gould Pond; Grab; GRAB; GRAHAM; Graham Lake; GRANDRAP; Grand Rapids; GRAS; GRAS_neotoma; GRAVEL; Gravel Ridge; Great Swamp (Highbush Blueberry); Green Lake; GREENLK; GREENMA; GREENPND; Green Pond; GRENADIE; Grenadier Pond; GREYIS; Grey Islands; GRINNELL; GUILDER; Guilder Pond; GUMLIMFT; GUMLIMM; GUMLIMNT; HACK; Hack Pond; Halls Cave; HALLSTX; HAMSLAKE; Hams Lake; HARRIE; Harrie Lake; HAUTNOR; HAUTSOU; HAWKE; Hawke Hills Kettle; Hayes Lake; Heart Lake; HEARTNY; HEBRON; HEBRON_neotoma; HELLHOLE; Hell Hole Lake; HEMLOCK; Hemlock Lake; HEN; HERSHOP; Hershop Bog; Hicks Lake; HICKSMI; HIGHBSH; HIGHLAKE; High Lake; HISCOCKP; Hiscock Site; HORM12; HORSESH8; HORSESHA; Horseshoe Lake; HOSTAGE; Hostage Lake; HOWES; Howes Prairie Marsh; Hoya Rincon de Parangueo; Hoya San Nicolás; Hudson Lake; HUDSONLK; HUMBER3; HUMBER5; HUMBER7; Humber Pond 3; Humber Pond 5; Humber Pond 7; HUSTLER; Hustler Lake; HYDEPARK; Hyde Park; HYNES; Hynes Brook Salt Marsh; Icehouse Pond; ICEHSMA; Identification; Iglutalik Lake; IGLUTALK; INDIAN; Indian Lake; INGLESBY; Inglesby Lake; IRVIN; Irvin Lake; Irwin Smith Bog; IRWINSMT; Island Lake; Isle au Haut North Hollow; Isle au Haut South Hollow; Itasca Bison Kill Site; ITASCAMB; Iztapa; IZTAPA; JACKLAKE; Jack Lake; JACKSN07; JACKSON; JACKSON_neotoma; Jackson Pond; JACOBSN2; Jacobson Lake; James Bay; Janes Cove; JANESCV; JAYLAKE; Jay Lake; JBL004; JEAN; JEMIMA; Jemima Pond; JEWELL; Jewell Site; JOES3; Joes Pond; JON; JONES; Jones Lake; Jones Lake [Glenboro site]; JOS; KAN; Kanaaupscow; Kearney McKean-2; KELLHOL1; KELLHOL2; KELLNERS; Kellners Lake; KELLYDUD; Kelly-Dudley Lake; Kellys Hollow; KENNYS; Kennys Pond; KENOGAMI; KESV; KETTLE; Kettle Lake; KIMBLE; Kimble Pond; Kirchner Marsh; KIRCHNR1; KITCHNER; Kitchner Lake; KNOBHIL; Knob Hill Pond; Kylen Lake; KYLENLK; Lac à lAnge; Lac à la Tortue; Lac à Léonard; Lac à Magie; Lac à Robin; Lac à Sam; Lac à St-Germain; Lac au Sable; Lac aux Quenouilles; Lacawac; LACAWAC; Lac Bastien; Lac Boucané; Lac Brule; Lac Caribou; LACCOLIN; Lac Colin; Lac des Atocas; Lac des Roches Moutonnées; Lac du Diable; Lac Dufresne; Lac Faribault; Lac Kénogami; LACLOUIS; Lac Louis; LACMAGIE; Lac Manitou; Lac Marcotte; Lac Martyne; Lac Mimi; Lac Noir; Lac Ouellet; Lac Patricia; Lac Petel; Lac Romer; Lac Turcotte; Lac Yelle; LADDLAKE; Ladd Lake; LAGARTO2; LAGARTOS; Laguna Biajaca; Laguna de la Leche; Laguna Pompal; LAKE16; LAKE27; Lake 27; LAKE31; Lake 31; Lake Ann; LAKEANNF; Lake Annie; Lake Arthur; Lake AT01; LAKEBI2; Lake BI2; LAKEBN7; Lake BN7; LAKECH2; Lake CH2; LAKEEC1; Lake EC1; LAKEEC2; Lake EC2; Lake Emily, Northeast Basin; Lake Erie; LAKEGB1; Lake GB1; LAKEGB2; Lake GB2; Lake Grinnell; Lake Hope Simpson; LAKEJAKE; Lake Jake; LAKELB1; Lake LB1; Lake Louise; LAKELR1; Lake LR1; LAKELR3; Lake LR3;
    Type: Dataset
    Format: text/tab-separated-values, 9665 data points
    Location Call Number Expected Availability
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  • 10
    Publication Date: 2023-08-29
    Keywords: 02DIOGO; 02GUIERS; Africa; Age, maximum/old; Age, minimum/young; ALB3PC; ALBITR1; ALBITR2; Albitrunca Cliff; Algeria; ANDOLO; AWI_Envi; BADDA; Badda bog; BAMBILI1; Bambili 1; BAMBILI2; Bambili 2; Bois de Bilanko; BOURDIM; Bourdim, Algeria; COMPCORE; Composite Core; Continent; CORAF; CORAF2; CORAFT; Core; CORE; CoreS2; CoreS6; CoreS7; CoreS8; CRAIG; Craigrossie; DARFATMA; Dar Fatma, Tunisia; Description; Deva-Deva; DEVA-DEVA; Diogo; Djebel El Ghorra, Tunisia; Driehoek; E96-1P; E96-5M; ELEVATION; ELIMSEQ; Elim Swamp; ELKHALED; EV13; Event label; fossil pollen; Garaat El-Ouez, Algeria; GARATOUE; GDV4; GHORRA01; Ghorra I; GROOTDRIFT; Grootdrift - Verlorenvlei; HARUB2; Harubes; Identification; KA1; KA3; Kashiru Swamp; Kitumbako; KITUMBAKO; KLAARFONT; Klaarfontein Springs; Lake Albert WHOI core 3PC; Lake Andolonomby; Lake Edward; Lake Eilandvlei; Lake Guiers; Lake Malawi MAL05-2; Lake Tritrivakely; LATITUDE; LH1; LH3; Livingstone piston sampler; Location type; LONGITUDE; LPS; MAHOMA; Mahoma Lake; Majen Ben Hmida, Tunisia; Majen El Orbi, Tunisia; MAJENHMI; MAJORBOG; MAJORMAR; MAL05-2A; MALAHLAPAN; Malahlapanga; Mare aux Songes; Mare dOursi; MAS1TR4C3; Mt Shengena; MULT; Multiple investigations; Neotoma; Nile Delta S2, Egypt; Nile Delta S6, Egypt; Nile Delta S7, Egypt; Nile Delta S8, Egypt; NILEDS2; NILEDS6; NILEDS7; NILEDS8; OBLONG; Oblong Tarn, Mt Kenya, Kenya; Oumm el-Khaled; OURSI; paleoecology; Polar Terrestrial Environmental Systems @ AWI; RCD; Reference/source; Rietvlei-Still Bay; Rotary core drilling; RUMUIKU; RUMUIKU_neotoma; RUSAKA; Rusaka Swamp; RUSC; Russian corer; RVSB; Sample amount; SHENGENA; Site; Small Momela Lake; SMOM; Sneeuberg; Sneeuberg Vlei; Songolo; SONGOLO; South Africa; South Atlantic Ocean; taxonomically harmonized; TIGAL; Tigalmamine, Morocco; TRIVA; Type; Uniform resource locator/link to reference
    Type: Dataset
    Format: text/tab-separated-values, 812 data points
    Location Call Number Expected Availability
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