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Palaeoecological records from BJM2 sediment core (Sebkha Boujmel, Southern Tunisia. 33°18’30.96” N, 11°5’0.68” E (Latitude Y 33.3086, Longitude X 11.083522). 1. Conventional AMS radiocarbon dates and reservoir-corrected and 2σ range calibrated ages from Sebkha Boujmel (BJM2 core). 2. Output of the age-depth model for BJM2 core indicating depth and associated mean date for each cm (cal yr BP). The age model was obtained by third-degree polynomial regression with 10k model iteration using the package Clam 2.2. 3. Pollen percentage for the three ecological groups (Mediterranean, steppe and desert taxa). The percentages are calculated with respect to a basic sum that only includes these three groups. Pollen taxa and types from the same genus or family and with the same ecology are grouped; including Boraginaceae (Moltkiopsis ciliata, Onosma and Echium), Ephedra sp. (Ephedra fragilis-t. and Ephedra distachia-t.) and Zygophyllaceae (Fagonia, Nitraria and Zygophyllum). Percentage of aquatics pollen are calculated based on the total sum of pollen grains identified in each pollen spectrum. 4. Pollen and clay mineralogy data from Sebkha Boujmel. Percentages of (1) fresh water (Cyperaceae, Glyceria, Juncus, Lemna, Potamogeton, Rumex aquaticus-t., Typha/Sparganium-t.) and (2) Mediterranean tree and shrub (Buxus, Ceratonia, Cistus, Juniperus, Lamiaceae, Myrtus, Nerium, Olea, Papaveraceae, Pinus, Pistacia, Quercus ilex-t., Quercus deciduous-t., Rhus tripartita-t.) pollen taxa. (3) Wet / dry (W / D) pollen ratio (Poaceae + Cyperaceae/Asteraceae Cichorioideae + Asteraceae Asteroideae + Amaranthaceae Cornulaca/Traganum-t.). (4) Percentages of desert pollen taxa (Apiaceae, Asphodelus, Asteraceae Asteroideae, Asteraceae Cichorioideae, Calligonum, Capparis, Cistanche, Cleome, Cornulaca/Traganum-t., Crassulaceae, Cucurbitaceae, Echium, Ephedra distachia-t., Ephedra fragilis-t., Fagonia, Helianthemum, Malvaceae, Moltkiopsis ciliata, Neurada, Nitraria, Onosma, Reaumuria, Tamarix and Zygophyllum). (5) Illite [%] (Ill) / Kaolinite [%] (Kln) ratio and (6) Palygorskite percentages [%] (Plg). 5. Pollen percentage of Artemisia and selected anthropogenic pollen indicators (APIs) including cultivated (Cerealia-t., Corchorus, Ficus, Olea, Phoenix, Vitis), nitrophilous (Aizoaceae, Emex, Peganum, Polygonum) and introduced (Acacia cyanophylla-t., Casuarina, Eucalyptus) plant taxa. Percentage are calculated based on the total sum of pollen grains identified in each pollen spectrum. 6. Pollen counts for BJM2 core (pollen grain count for each taxon by sample). + Lycopodium (added), Lycopodium (counted) and Sample weight [gr]. 7. Clay Mineralogy of BJM2 sediment core. Smectite [%] (Sme), METHOD/DEVICE: X-ray diffraction, clay fraction Illite [%] (Ill), METHOD/DEVICE: X-ray diffraction, clay fraction Palygorskite [%] (Plg), METHOD/DEVICE: X-ray diffraction, clay fraction Kaolinite [%] (Kln), METHOD/DEVICE: X-ray diffraction, clay fraction Chlorite [%] (Chl), METHOD/DEVICE: X-ray diffraction, clay fraction {"references": ["Jaouadi S, Lebreton V, Bout-Roumazeilles V, Siani G, Lakhdar R, Boussoffara R, Dezileau L, Kallel N, Mannai-Tayech B, Combourieu-Nebout N. 2016. Environmental changes, climate and anthropogenic impact in south-east Tunisia during the last 8\u202fkyr. Clim Past. 12(6):1339-1359. https://doi.org/10.5194/cp-12-1339-2016", "Jaouadi, S., and Lebreton, V.: Pollen-Based Landscape Reconstruction and Land-Use History Since 6000 BC along the Margins of the Southern Tunisian Desert, in: Plants and People in the African Past: Progress in African Archaeobotany, edited by: Mercuri, A. M., D'Andrea, A. C., Fornaciari, R., and H\u00f6hn, A., Springer International Publishing, Cham, 548-572, 2018."]}

These are the models and CAR scores presented in reported in R. Kyle Bocinsky, Johnathan Rush, Keith W. Kintigh, and Timothy A. Kohler. Exploration and exploitation in the macrohistory of the prehispanic Pueblo Southwest. Science Advances, 2:e1501532. These files are R data sets. See the paleocar package for information on how to extract model uncertainty and other data from these data files.

Dataset analysed in Pintossi, N.; Ikiz Kaya, D.; Pereira Roders, A. Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability 2021, 13, 5547. https://doi.org/10.3390/su13105547 Access: dataset embargoed until the doctoral defense of P.N., it can be requested at n.pintossi@tue.nl Date of data collection: 31/05/2018 Geographic location of data collection: Amsterdam, The Netherlands. The venue of the data collection is Pakhuis de Zwijger, Piet Heinkade 179, 1019 HC, Amsterdam, The Netherlands Activity of data collection: Historic Urban Landscape workshop 1 - Amsterdam. Held in Amsterdam, the Nethelands, on 30-31/05/2018 Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions Methods for collection/generation of data: see the methodology section and supporting information of Pintossi, N.; Ikiz Kaya, D.; Pereira Roders, A. Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability 2021, 13, 5547. https://doi.org/10.3390/su13105547 Researchers facilitating roundtable discussion and writing down paper version of data: Gamze Dane, Antonia Gravagnuolo, Paloma Guzman Molina, Ana Pereira Roders, Nadia Pintossi, and Julia Rey-Perez Original language of the data: English {"references": ["Pintossi, N., Ikiz Kaya, D. , & Pereira Roders, A. (2020). Identifying challenges and solutions in cultural heritage adaptive reuse through the Historic Urban Landscape approach in Amsterdam. XXX. DoiXXX"]}

Col Broken stickCol Pond plot of broken stick model resultsCol ConissCol Pond plot of CONISS model resultsCol N2Col Pond Hill's N2 and chlorophyll a outputCol PondCol Pond diatom raw count data and relative abundances including basionyms and modern taxonomic designationsElison broken stickElison Lake plot of broken stick model resultsHigh broken stickHigh Lake plot of broken stick model resultsMoraine broken stickMoraine Pond plot of broken stick model resultsParadise Broken stickParadise Pond plot of broken stick model resultsPlateau Pond 2 broken stickPlateau Pond 2 plot of broken stick model resultsProteus broken stickProteus Lake plot of broken stick model resultsSV5 broken stickSV Pond 5 plot of broken stick model resultsSV8 broken stickSV Pond 8 plot of broken stick model resultsWest broken stickWest Lake plot of broken stick model resultsElison conissElison Lake plot of CONISS model resultsHigh conissHigh Lake plot of CONISS model resultsMoraine ConissMoraine Pond plot of CONISS model resultsParadise ConissParadise Pond plot of CONISS model resultsPlateau pond 2 conissPlateau Pond 2 plot of CONISS model resultsProteus conissProteus Lake plot of CONISS model resultsSV5 conissSV Pond 5 plot of CONISS model resultsSV8 conissSV Pond 8 plot of CONISS model resultsWest conissWest Lake plot of CONISS model resultsElison N2Elison Lake Hill's N2 and Chlorophyll a valuesHigh N2High Lake Hill's N2 and Chlorophyll a valuesMoraine N2Moraine Pond Hill's N2 and Chlorophyll a valuesParadise N2Paradise Pond Hill's N2 and Chlorophyll a valuesPP2 N2Plateau Pond 2 Hill's N2 and Chlorophyll a valuesProteus N2Proteus Lake Hill's N2 and Chlorophyll a valuesSV5 N2SV Pond 5 Hill's N2 and Chlorophyll a valuesSV8 N2SV Pond 8 Hill's N2 and Chlorophyll a valuesWest N2West Lake Hill's N2 and Chlorophyll a valuesElison LakeElison Lake diatom raw count data and relative abundances including basionyms and modern taxonomic designationsHigh LakeHigh Lake diatom raw count data and relative abundances including basionyms and modern taxonomic designationsMoraine PondMoraine Pond diatom raw count data and relative abundances including basionyms and modern taxonomic designationsParadise PondParadise Pond diatom raw count data and relative abundances including basionyms and modern taxonomic designationsPlateau Pond 2Plateau Pond 2 diatom raw count data and relative abundances including basionyms and modern taxonomic designationsProteus lakeProteus Lake diatom raw count data and relative abundances including basionyms and modern taxonomic designationsSverdrup Pass PondsSverdrup Pond 5 and 8 diatom raw count data and relative abundances including basionyms and modern taxonomic designationsWest LakeWest Lake diatom raw count data and relative abundances including basionyms and modern taxonomic designationsElison_Lake_July_17_2007-July 16 2011 thermistorElison Lake thermistor data from July 17 2007 to July 16 2011Grouping rationaleAdditional summary of field notes providing support for grouping rationale.High Lake activityHigh Lake 210Pb activity from gamma-datingMoraine activityMoraine Pond 210Pb activity from gamma-datingParadise activityParadise Pond 210Pb activity from gamma-datingPlateau Pond2 activityPlateau Pond 2 210Pb activity from gamma-datingProteus activityProtease Lake 210Pb activity from gamma-datingSV Pond 5 activitySV Pond 5 210Pb activity from gamma-datingSV Pond 8 activitySV Pond 8 210Pb activity from gamma-datingWater chem additional parameters all sitesAdditional water chemistry parameters for all sitesWest activityWest Lake 210Pb activity from gamma-datingHigh Lake datesHigh Lake 210Pb dates using CRS dating methodMoraine Pond DatesMoraine Pond 210Pb dates using CRS dating methodMoraine datesParadise datesParadise Pond 210Pb dates using CRS dating methodPlateau Pond2 datesPlateau Pond 2 210Pb dates using CRS dating methodProteus datesProteus Lake 210Pb dates using CRS dating methodSV Pond 5 datesSV Pond 5 210Pb dates using CRS dating methodSV Pond 8 datesSV Pond 8 210Pb dates using CRS dating methodWest Lake datesWest Lake 210Pb dates using CRS dating methodSV Ponds 14C datingSV Pond 5 and 8 14C dates and types of microfossils datedHills N2 scriptHill's N2 R script used to calculate Hill's N2 and rarify species count data (using pre-made packages) Recent climate change has been especially pronounced in the High Arctic, however, the responses of aquatic biota, such as diatoms, can be modified by site-specific environmental characteristics. To assess if climate-mediated ice cover changes affect the diatom response to climate, we used paleolimnological techniques to examine shifts in diatom assemblages from ten High Arctic lakes and ponds from Ellesmere Island and nearby Pim Island (Nunavut, Canada). The sites were divided a priori into four groups (“warm”, “cool”, “cold”, and “oasis”) based on local elevation and microclimatic differences that result in differing lengths of the ice-free season, as well as about three decades of personal observations. We characterized the species changes as a shift from Condition 1 (i.e. a generally low diversity, predominantly epipelic and epilithic diatom assemblage) to Condition 2 (i.e. a typically more diverse and ecologically complex assemblage with an increasing proportion of epiphytic species). This shift from Condition 1 to Condition 2 was a consistent pattern recorded across the sites that experienced a change in ice cover with warming. The “warm” sites are amongst the first to lose their ice covers in summer and recorded the earliest and highest magnitude changes. The “cool” sites also exhibited a shift from Condition 1 to Condition 2, but, as predicted, the timing of the response lagged the “warm” sites. Meanwhile some of the “cold” sites, which until recently still retained an ice raft in summer, only exhibited this shift in the upper-most sediments. The warmer “oasis” ponds likely supported aquatic vegetation throughout their records. Consequently, the diatoms of the “oasis” sites were characterized as high-diversity, Condition 2 assemblages throughout the record. Our results support the hypothesis that the length of the ice-free season is the principal driver of diatom assemblage responses to climate in the High Arctic, largely driven by the establishment of new aquatic habitats, resulting in increased diversity and the emergence of novel growth forms and epiphytic species.

This record contains raw data of a 3-month monitoring period of the HeLLo project. The datafiles titled MeasLog_YYYY-MM-DD.dat correspond to the raw data used for the data analysis presented in “Hygrothermal analysis at critical points of an internally insulated historic wall without vapour barrier: in situ measurements and dynamic simulation”, submitted for publication in journal energies. Each file, format MeasLog_YYYY-MM-DD.dat, corresponds to daily registered data monitored every minute. Each file, format MeasLog_YYYY-MM-DD.dat, contains temperature (T) and relative humidity (RH) values, monitored through T-RH sensors (Telaire T9602; Amphenol). The general architecture of the acquisition system is based on a Master Slave configuratio, as described in “Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls” (doi:10.3390/electronics8060643). Each file, format MeasLog_YYYY-MM-DD.dat is a text-based DAT file and can be opened with a standard text editor.

The datasets present collected data aimed at measuring the state of cultural tourism and economic development and resilience of a select set of potential cultural tourism destinations in Europe, as part of the Horizon 2020 funded project SmartCulTour (www.smartcultour.eu). The data is collected on the level of Local Administrative Units (LAUs) for the following municipalities/cities: Spain: Ainsa, Barbastro, Benasque, Graus, Huesca, Jaca, Sariñena the Netherlands: Rotterdam, Delft, Dordrecht, Molenlanden, Barendrecht, Ridderkerk, Zwijndrecht Finland: Utsjoki Italy: Vicenza, Caldogno, Pojana Maggiore, Grumolo delle Abbadesse, Lonigo, Montagna Croatia: Split, Trogir, Kaštela, Solin, Sinj, Dugopolje, Klis Belgium: Dendermonde, Puurs-Sint-Amands, Bornem, Berlare, Aalst, Denderleeuw, Willebroek The data is presented as panel data and available for the following years: 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019. Please consult the metadata on each dataset for an overview of collected indicators and units of measurement.

1. This paper addresses the origin and development of the oldest prehistoric pasture in the timberline ecotone known so far in the Alps and its relation to anthropogenic pressure and natural climate change. 2. Paleoecological and geochemical techniques were applied on the Crotte Basse mire stratigraphy (2365 m asl, western Italy) to describe changes in vegetation composition, forest biomass, land use and fertilization between ca. 6400 - 1800 yrs cal BP. 3. Subalpine forests dominated by Pinus cembra occurred at very high-altitude up to ca. 5600 yrs cal BP, when a sharp contraction of woody vegetation took place. This major vegetation shift is matched by increasing charcoal input and markers of pastoral/grazing activities (pollen, dung spores, and forms of phosphorus) in the sediment sequence in this small basin. 4. Major phases of landscape change detected in our multiproxy record chronologically match intervals of cumulative probability density of 14C ages from nearby archaeological sites, suggesting that human activity was the factor leading to massive landscape change from the onset of the Copper Age (ca. 5600 yrs cal BP). The change may have been reinforced by climate variability in the period 5700 - 5300 years cal BP. 5. Sensitivity of woody species to fires was statistically explored (Appendix S3 in Supporting Information), revealing negative reactions of Pinus cembra and Betula to frequent fire episodes and positive reactions of Alnus viridis and Juniperus. Fire episodes do not affect Larix dynamics. 6. Synthesis: Mt. Fallère provides some of the oldest and consistent evidence so far available in the Alps for major anthropogenic pressure at the upper forest limit. As far back as 5600 cal years BP, high-elevation forest ecosystems were permanently disrupted and the alpine pastures were created. Palaeoecological data enable a clear distinction between a random and sporadic use of the alpine space, typical for Mesolithic and Neolithic societies, and an organized seasonal exploitation of natural resources, starting from the Copper Age onwards. The chronological comparison of independent climate proxies, paleoecological information and pollen-based temperature reconstructions sheds light on the relationships between climate and humans since prehistoric times. Pini et al_JEcol_2016_0491_R1_Dryad archived dataThis file contains % pollen and LOI data, P forms concentrations used for elaborations presented in the paper.

Selection of individual members of the CCC and CCS groups: Using publicly available records, we created two lists of names comprising the CCC and CCS groups – 386 prominent climate change contrarians and 386 highly-cited climate change scientists, respectively. Media Cloud (MC) data: We then used the Media Cloud API to download media article records associated with each individual by querying the MC database for articles including that individual's name and the word "climate". All together, we analyzed roughly ~100,000 individual media articles. Web of Science (WOS) data: We downloaded a large dataset of ~ 200,000 articles from the WOS "Core Collection" using a database query that identified articles associated with the topic "Climate Change". Within this set of CC research articles, we then searched for the research articles associated with each individual using string matching of coauthor names. Research objective: In the accompanying research article, we juxtapose these two groups and compare quantitative measures of scientific authority with quantitative measures of visibility in digital and print media – at both the group and individual levels. We also map out the associations between groups/individuals that are manifest when two individuals appear in the same media article or when one individual cites the research article of another individual. We aggregate these dyadic relations and visualize the resulting association networks within and between CCC and CCS groups, at both the group and individual levels. We document the media visibility and climate change research achievements of two groups of individuals representing some of the most prominent figures in their respective domains: 386 climate change contrarians (CCC) juxtaposed with 386 expert climate change scientists (CCS). These data were collected from the Media Cloud project (MC), an open data project hosted by the MIT Center for Civic Media and the Berkman Klein Center for Internet & Society at Harvard University. Enclosed are raw MC data and parsed media article data files obtained from two types of MC database queries: (i) ~105,000 media articles derived from the MC search query ''climate AND change AND global AND warming''; (ii) 772 individual data files for each member of the CCC and CCS groups, each derived from a single MC search query ''MemberFullName AND climate''. These data facilitate the objective comparison of authority in science with visibility in digital and print media, and are used to map individuals' association networks within and between CCC and CCS groups, at both the group and individual levels. ReadMe file: The enclosed PDF file ClimateChange-Contrarians-Scientists_DataDescription_ANONYMIZED.pdf describes the source and organization of the Article-level and Individual-level (CCC and CCS profiles) variabiles contained in each file. Source code: provided in a Mathematica (v11.1) notebook (MediaSource_Annotated_ALL_2256.nb using MediaSource_Annotated_ALL_2256_ANONYMIZED.txt) reproduces the subpanels for Fig. 5 in the following research article - if you use this data please cite: A. M. Petersen, E. M. Vincent, A. L. Westerling (2019) Discrepancy in scientific authority and media visibility of climate change scientists and contrarians. Nature Communications 10, 3502. DOI: 10.1038/s41467-019-09959-4

Dataset analyzed in Pintossi, N.; Ikiz Kaya, D.; Pereira Roders, A. Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability 2021, 13, 3603. https://doi.org/10.3390/su13073603 Access: dataset embargoed until the doctoral defense of P.N., it can be requested at n.pintossi@tue.nl Date of data collection: 28/03/2019 Geographic location of data collection: Rijeka, Croatia. The venue of the data collection is RiHub, Ul. Ivana Grohovca 1/a, 51000, Rijeka, Croatia. Activity of data collection: Historic Urban Landscape workshop 3 - Rijeka. Held in Rijeka, Croatia, on 28/03/2019 Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions to overcome these challenges. Methods for collection/generation of data: See the methodology section in Pintossi, N.; Ikiz Kaya, D.; Pereira Roders, A. Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability 2021, 13, 3603. https://doi.org/10.3390/su13073603. Researchers facilitating roundtable discussion and writing down paper version of data: Marco Acri, Martina Bosone, Deniz Ikiz Kaya, Silvia Iodice, Lu Lu, and Nadia Pintossi. Researcher translating to English, transcribing data in the digital tabular dataset, and cleaning the data: Nadia Pintossi. Language of the data: English. Original language of the data: English and Italian (refer to Original language.csv).

This dataset contains the underlying data (energy sector data for India) for the book chapter Song et al., 2022 published in Science in May 2022.