README.Rmd

```{r, echo = FALSE, warning = TRUE}

knitr::opts_knit$set(base.url = "/")
knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  echo = FALSE,
  fig.width = 9,
  fig.height = 6
)
options(scipen = 999, digits = 2, tibble.width = Inf, tibble.print_max = Inf)

knitr::knit_hooks$set(inline = function(x) {
  prettyNum(x, big.mark = ",")
})
```

```{r}
require(tidyverse)
require(stringr)
require(ggplot2)
```

```{r, echo=FALSE, cache = FALSE}
my_apc <- readr::read_csv("data/apc_de.csv")
my_bpc <- readr::read_csv("data/bpc.csv")
my_apc_ac <- readr::read_csv("data/apc_de_additional_costs.csv")
```

## About

The aim of this repository is:

- to release data sets on fees paid for Open Access journal articles and monographs by Universities and Research Society Funds under an Open Database License
- to demonstrate how reporting on fee-based Open Access publishing can be made more transparent and reproducible across institutions.

At the moment this project provides the following cost data:

| Publication Type | Count           | Aggregated Sum (€)      | Contributing Institutions              |
|------------------|-----------------|-------------------------|----------------------------------------|
| Articles         |`r nrow(my_apc)` | `r sum(my_apc$euro)`    | `r length(unique(my_apc$institution))` |
| Monographs       |`r nrow(my_bpc)` | `r sum(my_bpc$euro)`    | `r length(unique(my_bpc$institution))` |

## How to access the data?

There are several options. You may simply download the the raw data sets in CSV format, query our [OLAP server](https://github.com/OpenAPC/openapc-olap/blob/master/HOWTO.md) or use our [Treemap site](https://treemaps.openapc.net/) for visual data exploration.

| Dataset         | CSV File                                                                                                                                                                                                                         | OLAP Cube                                                                     | Treemap                                                                       |
|-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------|-------------------------------------------------------------------------------|
| articles        | [APC file](https://github.com/OpenAPC/openapc-de/blob/master/data/apc_de.csv), [data schema](https://github.com/OpenAPC/openapc-de/wiki/schema#openapc-data-set)                                                                 | [APC cube](https://olap.openapc.net/cube/openapc/aggregate)                   | [APC treemap](https://treemaps.openapc.net/apcdata/openapc/)                  |
| TA articles     | [TA file](https://github.com/OpenAPC/openapc-de/blob/master/data/transformative_agreements/transformative_agreements.csv), [data schema](https://github.com/OpenAPC/openapc-de/wiki/schema#transformative-agreements-data-set)   | [TA cube](https://olap.openapc.net/cube/transformative_agreements/aggregate)  | [TA treemap](https://treemaps.openapc.net/apcdata/transformative-agreements/) |
| monographs      | [BPC file](https://github.com/OpenAPC/openapc-de/blob/master/data/bpc.csv), [data schema](https://github.com/OpenAPC/openapc-de/wiki/schema#bpc-data-set)                                                                        | [BPC cube](https://olap.openapc.net/cube/bpc/aggregate)                       | [BPC treemap](https://treemaps.openapc.net/apcdata/bpc/)                      |

Our latest data release can always be accessed via the following DOI:

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6883472.svg)](https://doi.org/10.5281/zenodo.6883472)

## Additional Costs

In 2024 OpenAPC started to aggregate additional costs like page charges or submission fees, which may occur in the context of OA publishing aside from APCs. These cost types are considered optional and thus collected in a separate data file, they are linked to a main publication entry using the DOI as primary key. At the moment additional costs are only collected for journal articles.

| Dataset         | Main CSV File                                                                 | Additional Costs File                                                                                       | OLAP Cube                                                             | Treemap                                                                           |
|-----------------|-------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------|-----------------------------------------------------------------------------------|
| articles        | [APC file](https://github.com/OpenAPC/openapc-de/blob/master/data/apc_de.csv) | [APC additional costs](https://github.com/OpenAPC/openapc-de/blob/master/data/apc_de_additional_costs.csv)  | [APC_AC cube](https://olap.openapc.net/cube/openapc_ac/aggregate)     | [APC_AC Treemap](https://treemaps.openapc.net/apcdata/openapc/additional_costs)   |

## How to contribute?

Any academic institution or research funder paying for Article Process Charges (APCs) or Book Processing Charges (BPCs) can contribute to OpenAPC, no formal registration is required.
This [page](https://github.com/OpenAPC/openapc-de/wiki/Data-Submission-Handout) ([German version](https://github.com/OpenAPC/openapc-de/wiki/Handreichung-Dateneingabe)) explains the details.

Please note that OpenAPC uses internal identifiers for institutions in all its data sets to maintain backwards compatibility. Additional metadata on all participants (including ROR IDs as persistent identifier) is maintained in a separate [lookup table](https://github.com/OpenAPC/openapc-de/blob/master/data/institutions.csv).

The following institutions have contributed to OpenAPC so far:

```{r, echo=FALSE, results = 'asis'}

ins <- readr::read_csv("data/institutions.csv")
countries <- readr::read_csv("data/translation_countries.csv")



ins_types <- readr::read_csv("data/translation_institution_types.csv")
ins_groups <- readr::read_csv("data/translation_institution_groups.csv")

for (i in seq_along(countries$country)) {
  ctry_full_name <- countries[[i, "country_full_name"]]
  ctry <- countries[[i, "country"]]
  cat(paste("## Institutions from", ctry_full_name, "\n\n"))
  for (j in seq_along(ins_types$institution_type)) {
    ins_type <- ins_types[[j, "institution_type"]]
    if (!is.na(ins_type)) {
      institutions_with_type <- filter(ins, country == ctry, institution_type == ins_type)
    }
    else {
      institutions_with_type <- filter(ins, country == ctry, is.na(institution_type))
    }
    if (nrow(institutions_with_type) > 0) {
      if (!is.na(ins_type)) {
        cat(paste("### ", ins_types[[j, "institution_type_full_name"]], "\n\n"))
      }
      for (k in seq_along(ins_groups$institution_group)) {
        ins_group <- ins_groups[[k, "institution_group"]]
        if (!is.na(ins_group)) {
          institutions_with_group <- filter(institutions_with_type, institution_group == ins_group)
        }
        else {
          institutions_with_group <- filter(institutions_with_type, is.na(institution_group))
        }
        institutions_with_group = arrange(institutions_with_group, institution_full_name)
        if (nrow(institutions_with_group) > 0) {
          if (!is.na(ins_group)) {
            cat(paste("#### ", ins_groups[[k, "institution_group_full_name"]], "\n\n"))
          }
          for (l in seq_along(institutions_with_group$institution)) {
            comment <- ""
            if (!is.na(institutions_with_group[[l, "comment"]])) {
              comment <- paste(" (", institutions_with_group[[l, "comment"]], ")", sep="")
            }
            name <- institutions_with_group[[l, "institution_full_name"]]
            if (!is.na(institutions_with_group[[l, "info_url"]])) {
              name <- paste("[", name, "](", institutions_with_group[[l, "info_url"]], ")", sep="")
            }
            cat(paste("- ", name, comment, "\n", sep=""))
          }
          cat("\n")
        }
      }
    }
  }
}

```

## Data sets

*Note: The following numbers and plots are always based on the [latest revision](https://github.com/OpenAPC/openapc-de/releases/latest) of the OpenAPC data set. The underlying code can be found in the associated [R Markdown template](README.Rmd).*

### Articles (APCs)

```{r, echo=FALSE}

fully_oa <- my_apc %>% filter(is_hybrid == FALSE)
hybrid <- my_apc %>% filter(is_hybrid == TRUE)

```

The article data set contains information on `r nrow(my_apc)` open access journal articles being published in fully and hybrid open access journal. Publication fees for these articles were supported financially by `r length(unique(my_apc$institution))` research performing institutions and research funders. 

In total, article publication fee spending covered by the OpenAPC initiative amounted to € `r sum(my_apc$euro)`. The average payment was € `r mean(my_apc$euro)` and the median was € `r median(my_apc$euro)`.

`r nrow(fully_oa)` articles in the data set were published in fully open access journals. Total spending on publication fees for these articles amounts to € `r sum(fully_oa$euro)`, including value-added tax; the average payment was € `r mean(fully_oa$euro)` (median =  € `r median(fully_oa$euro)`, SD = € `r sd(fully_oa$euro)`).

Hybrid open access journals rely on both publication fees and subscriptions as revenue source. `r nrow(hybrid)` articles in the data set were published in hybrid journals. Total expenditure amounts to `r sum(hybrid$euro)` €; the average fee was € `r mean(hybrid$euro)` (median =  € `r median(hybrid$euro)`, SD = € `r sd(hybrid$euro)`).

#### Spending distribution over fully and hybrid open access journals

```{r, echo=FALSE}
p <- my_apc %>%
  mutate(is_hybrid = ifelse(.$is_hybrid, "Hybrid OA journals", "Fully OA journals")) %>% 
  mutate(short_period = str_sub(period, 3 ,4)) %>%
  ggplot(aes(factor(short_period), euro)) +
  geom_boxplot(outlier.size = 0.5) +
  xlab("Year") +
  ylab("Publication fee paid in Euro") +
  scale_y_continuous(limits = c(0,8000)) + 
  facet_grid(~is_hybrid) +
  theme_bw()
  ggsave(p, path = "figure/", filename = "boxplot_oa.png", width=9, height=4.5, units="in", device = "png")
```

![](figure/boxplot_oa.png)

#### Spending distribution details

```{r, echo=FALSE, results='asis', message = FALSE}

oa_grouped <- my_apc %>%
    filter(is_hybrid == FALSE) %>%
    select(period, euro) %>%
    group_by(period) %>%
    summarise(
        n = n(),
        mean = mean(euro),
        median = median(euro),
        min = min(euro),
        max = max(euro)
    ) %>%
    mutate(n = format(n, big.mark=","), 
           mean = format(mean, big.mark=","),
           median = format(median, big.mark=","),
           min = format(min, big.mark=","),
           max = format(max, big.mark=","),
           minmax = str_c(min, " - ", max)
    ) %>%
    select(period, oa_n = n, oa_mean = mean, oa_median = median, oa_min_max = 'minmax')

hyb_grouped <- my_apc %>%
    filter(is_hybrid == TRUE) %>%
    select(period, euro) %>%
    group_by(period) %>%
    summarise(
        n = n(),
        mean = mean(euro),
        median = median(euro),
        min = min(euro),
        max = max(euro)
    ) %>%
    mutate(n = format(n, big.mark=","), 
           mean = format(mean, big.mark=","),
           median = format(median, big.mark=","),
           min = format(min, big.mark=","),
           max = format(max, big.mark=","),
           minmax = str_c(min, " - ", max)
    ) %>%
    select(period, hyb_n = n, hyb_mean = mean, hyb_median = median, hyb_min_max = 'minmax')

full_join(oa_grouped, hyb_grouped) %>%
knitr::kable(col.names = c("period", "OA articles", "OA mean", "OA median", "OA min - max", "Hybrid Articles", "Hybrid mean", "Hybrid median", "Hybrid min - max"), align = c("l","r", "r", "r", "r", "r", "r", "r", "r"))

```

#### Additional Costs for articles

```{r, echo=FALSE, results='asis', message = FALSE}

apc_de_euro_only <- my_apc %>% mutate(apc = euro) %>% select(doi, apc)

apc_ac_long <- my_apc_ac %>%
  left_join(apc_de_euro_only) %>%
  pivot_longer(!doi, names_to = "cost_type", values_to = "value", values_drop_na = TRUE)

apc_ac_grouped <- apc_ac_long %>%
  left_join(my_apc) %>%
  mutate(cost_type = factor(cost_type), publisher = factor(publisher)) %>%
  group_by(cost_type, publisher) %>%
  summarize(value = sum(value), articles = n())

apc_ac_biggest <- apc_ac_grouped %>%
  group_by(publisher) %>%
  summarize(value=sum(value)) %>%
  arrange(desc(value))

apc_ac_big_publishers <- apc_ac_biggest[1:20,] %>%
  select(publisher)

apc_ac_grouped_biggest <- apc_ac_big_publishers %>%
  left_join(apc_ac_grouped)

apc_ac_long_table <- apc_ac_long %>%
  filter(cost_type != "apc") %>%
  group_by(cost_type) %>%
  summarize(count = n(), value = sum(value))

p <- apc_ac %>%
  ggplot(data = apc_ac_grouped_biggest, mapping = aes(x = factor(publisher, as.vector(apc_ac_big_publishers$publisher)), y = value, fill=cost_type)) +
  geom_col(position = position_stack(reverse = TRUE)) + 
  xlab('Publisher') + 
  ylab("Summarized Costs (€)") +
  guides(fill = guide_legend(title = "Cost Type")) + 
  scale_fill_manual(
    values = c("darkolivegreen3", "orange", "red", "magenta2", "darkslategray", "blue", "darkgreen", "yellow", "cyan"),
  #  labels = c("APC", "Colour Charges", "Cover Charge", "Other", "Page Charge", "Payment Fee", "Permission", "Reprint", "Submission Fee")
  ) + 
  coord_flip()
  ggsave(p, path = "figure/", filename = "additional_costs.png", width=9, height=4.5, units="in", device = "png")

```

In addition to APCs, additional costs have been reported for `r sum(apc_ac_long_table$count)` articles, totalling € `r sum(apc_ac_long_table$value)`. The following table shows an overview of the different cost types:

```{r, echo=FALSE, results='asis', message = FALSE}
apc_ac_long_table %>%
  mutate(count = format(count, big.mark=","), 
         value = format(value, big.mark=",")
        ) %>%
  knitr::kable(col.names = c("Cost Type", "Number of Articles", "Total Sum (€)"), align = c("l","r", "r"))
```

This plot shows the cost distribution grouped by publishers. It includes only articles where additional reports have been reported and summarizes both additional costs and APCs for all of them:

![](figure/additional_costs.png)

### Books (BPCs)

The book data set contains information on `r nrow(my_bpc)` open access books. Publication fees were supported financially by `r length(unique(my_bpc$institution))` research performing institutions and funders. 

In total, book processing charges covered by the OpenAPC initiative amounted to € `r sum(my_bpc$euro)`. The average payment was € `r mean(my_bpc$euro)` and the median was € `r median(my_bpc$euro)`.

Books can be made Open Access right from the beginning ("frontlist") or only retroactively after having been published traditionally in the first place ("backlist"), which can have a big influence on the paid BPCs.

#### Spending distribution over frontlist and backlist OA books

```{r, echo=FALSE}
p <- my_bpc %>%
  mutate(backlist_oa = ifelse(.$backlist_oa, "Backlist OA", "Frontlist OA")) %>% 
  mutate(short_period = str_sub(period, 3 ,4)) %>%
  ggplot(aes(factor(short_period), euro)) +
  geom_boxplot(outlier.size = 0.5) +
  xlab("Year") +
  ylab("Publication fee paid in Euro") +
  scale_y_continuous(limits = c(0,12000)) + 
  facet_grid(~backlist_oa) +
  theme_bw()
  ggsave(p, path = "figure/", filename = "boxplot_bpcs.png", width=9, height=4.5, units="in", device = "png")
```

![](figure/boxplot_bpcs.png)

#### Spending distribution details

```{r, echo=FALSE, results='asis', message = FALSE}

frontlist_grouped <- my_bpc %>%
    filter(backlist_oa == FALSE) %>%
    select(period, euro) %>%
    group_by(period) %>%
    summarise(
        n = n(),
        mean = mean(euro),
        median = median(euro),
        min = min(euro),
        max = max(euro)
    ) %>%
    mutate(n = format(n, big.mark=","), 
           mean = format(mean, big.mark=","),
           median = format(median, big.mark=","),
           min = format(min, big.mark=","),
           max = format(max, big.mark=","),
           minmax = str_c(min, " - ", max)
    ) %>%
    select(period,frontlist_n = n,frontlist_mean = mean,frontlist_median = median,frontlist_min_max = minmax)

backlist_grouped <- my_bpc %>%
    filter(backlist_oa == TRUE) %>%
    select(period, euro) %>%
    group_by(period) %>%
   summarise(
        n = n(),
        mean = mean(euro),
        median = median(euro),
        min = min(euro),
        max = max(euro)
    ) %>%
    mutate(n = format(n, big.mark=","), 
           mean = format(mean, big.mark=","),
           median = format(median, big.mark=","),
           min = format(min, big.mark=","),
           max = format(max, big.mark=","),
           minmax = str_c(min, " - ", max)
    ) %>%
    select(period, backlist_n = n, backlist_mean = mean, backlist_median = median, backlist_min_max = minmax)

full_join(frontlist_grouped, backlist_grouped) %>%
knitr::kable(col.names = c("period", "Frontlist books", "mean BPC", "median BPC", "BPC min - max", "Backlist books", "mean BPC", "median BPC", "BPC min - max"), align = c("l","r", "r", "r", "r", "r", "r", "r", "r"))

```


## Use of external sources

Metadata representing publication titles or publisher names is obtained from Crossref in order to avoid extensive validation of records. Cases where we don't re-use information from Crossref to disambiguate the spending metadata are documented [here](python/test/test_apc_csv.py). Moreover, indexing coverage in Europe PMC and the Web of science is automatically checked.

### Articles 

|Source     |Variable  |Description                     |
|:--------------|:---------|:-----------------------------------------------|
|CrossRef   |`publisher` |Title of Publisher             |
|CrossRef   |`journal_full_title` |Full title of the journal  |
|CrossRef   |`issn` |International Standard Serial Numbers (collapsed) |
|CrossRef   |`issn_print` |ISSN print |
|CrossRef   |`issn_electronic`  |ISSN electronic        |
|CrossRef   |`license_ref`  |License of the article     |
|CrossRef   |`indexed_in_crossref`  |Is the article metadata registered with CrossRef? (logical)    |
|EuropePMC    |`pmid`  |PubMed ID                 |
|EuropePMC    |`pmcid` |PubMed Central ID         |
|Web of Science |`ut` |Web of Science record ID             |
|DOAJ           |`doaj` |Is the journal indexed in the DOAJ? (logical)    |

### Books

|Source     |Variable  |Description                     |
|:--------------|:---------|:-----------------------------------------------|
|CrossRef   |`publisher` |Title of Publisher             |
|CrossRef   |`book_title` |Full Title of a Book  |
|CrossRef   |`isbn` |International Standard Book Number |
|CrossRef   |`isbn_print` |ISBN print |
|CrossRef   |`isbn_electronic`  |ISBN electronic        |
|CrossRef   |`license_ref`  |License of the article     |
|CrossRef   |`indexed_in_crossref`  |Is the article metadata registered with CrossRef? (logical)    |
|DOAB           |`doab` |Is the book indexed in the DOAB? (logical)    |


```{r, echo=FALSE, cache = FALSE}

my.apc <- readr::read_csv("data/apc_de.csv")

my.apc_doi <- my.apc[!is.na(my.apc$doi), ]
my.apc_pmid <- my.apc[!is.na(my.apc$pmid), ]
my.apc_pmcid <- my.apc[!is.na(my.apc$pmcid), ]
my.apc_ut <- my.apc[!is.na(my.apc$ut), ]

my.bpc <- readr::read_csv("data/bpc.csv")

my.bpc_doi <- my.bpc[!is.na(my.bpc$doi), ]

```

### Indexing coverage

|Identifier                 | Coverage (articles)                                               | Coverage (Books)                                            |
|:--------------------------|:------------------------------------------------------------------|-------------------------------------------------------------|
|DOI                        |  `r format(nrow(my.apc_doi)*100/nrow(my.apc), digits = 4)`%       |`r format(nrow(my.bpc_doi)*100/nrow(my.bpc), digits = 4)`%   |
|PubMed ID                  |  `r format(nrow(my.apc_pmid)*100/nrow(my.apc), digits = 4)`%      | NA                                                          |
|PubMed Central ID          |  `r format(nrow(my.apc_pmcid)*100/nrow(my.apc), digits = 4)`%     | NA                                                          |
|Web of Science record ID   | `r format(nrow(my.apc_ut)*100/nrow(my.apc), digits = 4)`%         | NA                                                          |





## License

The data sets are made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/

This work is licensed under the Creative Commons Attribution 4.0 Unported License.

## How to cite?

When citing this data set, please indicate the [release](https://github.com/OpenAPC/openapc-de/releases/) you are referring to. The releases also contain information on contributors relating to the respective release.

Please do not cite the master branch of the Github repository (https://github.com/OpenAPC/openapc-de/tree/master/), but use the release numbers/tags.

Bielefeld University Library archives a copy (including commit history). To cite:

{Contributors:} *Datasets on fee-based Open Access publishing across German Institutions*. Bielefeld University. [10.4119/UNIBI/UB.2014.18](http://dx.doi.org/10.4119/UNIBI/UB.2014.18)

## Acknowledgement

This project was set up in collaboration with the [DINI working group Electronic Publishing](http://dini.de/ag/e-pub1/). It follows [Wellcome Trust example to share data on paid APCs](http://blog.wellcome.ac.uk/2014/03/28/the-cost-of-open-access-publishing-a-progress-report/) and recognises efforts from [JISC](https://www.jisc-collections.ac.uk/News/Releasing-open-data-about-Total-Cost-of-Ownership/) and the [ESAC initative](http://esac-initiative.org/) to standardise APC reporting.

## Contributors

Jens Harald Aasheim, Sarah Abusaada, Benjamin Ahlborn, Chelsea Ambler, Magdalena Andrae, Jochen Apel, Karina Barros Ferradás, Myriam Bastin, Hans-Georg Becker, Roland Bertelmann, Daniel Beucke, Manuela Bielow, Jochen Bihn, Peter Blume, Ute Blumtritt, Sabine Boccalini, Stefanie Bollin, Katrin Bosselmann, Valentina Bozzato, Kim Braun, Christoph Broschinski, Paolo Buoso, Cliff Buschhart, Dorothea Busjahn, Pablo de Castro, Ann-Kathrin Christann, Roberto Cozatl, Micaela Crespo Quesada, Amanda Cullin, Patrick Danowski, Gernot Deinzer, Julia Dickel, Andrea Dorner, Stefan Drößler, Karin Eckert, Carsten Elsner, Clemens Engelhardt, Olli Eskola, Katrin Falkenstein-Feldhoff, Ashley Farley, Inken Feldsien-Sudhaus, Silke Frank, Fabian Franke, Claudia Frick, Marléne Friedrich, Paola Galimberti, Agnes Geißelmann, Kai Karin Geschuhn, Silvia Giannini, Marianna Gnoato, Larissa Gordon, Paul Gredler, Steffi Grimm, Ute Grimmel-Holzwarth, Evgenia Grishina, Christian Gutknecht, Birgit Hablizel, Florian Hagen, Uli Hahn, Kristina Hanig, Margit L. Hartung, Dominik Hell, Christina Hemme, Eike Hentschel, Ulrich Herb, Stephanie Herzog, Elfi Hesse, Silke Hillmann, Kathrin Höhner, Dana Horch, Conrad Hübler, Christie Hurrell, Arto Ikonen, Simon Inselmann, Doris Jaeger, Najko Jahn, Alexandra Jobmann, Daniela Jost, Tiina Jounio, Juho Jussila, Nadja Kalinna, Mirjam Kant, Kerstin Klein, Andreas Kennecke, Robert Kiley, Ilka Kleinod, Lydia Koglin, Nives Korrodi, Biljana Kosanovic, Stephanie Kroiß, Gerrit Kuehle, Stefanie Kutz, Marjo Kuusela, Anna Laakkonen, Ignasi Labastida i Juan, Gerald Langhanke, Inga Larres, Sarah Last, Stuart Lawson, Anne Lehto, Sari Leppänen, Camilla Lindelöw, Maria Löffler, Jutta Lotz, Kathrin Lucht-Roussel, Susanne Luger, Ute von Lüpke, Jan Lüth, Frank Lützenkirchen, Steffen Malo, Anna Marini, Manuel Moch, Vlatko Momirovski, Andrea Moritz, Max Mosterd, Marcel Nieme, Anja Oberländer, Martina Obst, Jere Odell, Linda Ohrtmann, Vitali Peil, Gabriele Pendorf, Mikko Pennanen, Dirk Pieper, Tobias Pohlmann, Thomas Porquet, Markus Putnings, Andrée Rathemacher, Rainer Rees-Mertins, Edith Reschke, Ulrike Richter, Katharina Rieck, Friedrich Riedel, Simone Rosenkranz, Florian Ruckelshausen, Steffen Rudolph, Ilka Rudolf, Pavla Rygelová, Lea Satzinger, Annette Scheiner, Isabo Schick, Michael Schlachter, Birgit Schlegel, Andreas Schmid, Barbara Schmidt, Katharina Schulz, Stefanie Seeh, Barbara Senkbeil-Stoffels, Adriana Sikora, Tereza Simandlová, Stefanie Söhnitz, Jana Sonnenstuhl, Lisa Spindler, Susanne Stemmler, Matti Stöhr, Eva Stopková, Marius Stricker, Andrea Stühn, Kálmán Szőke, Linda Thomas, Anne Timm, Laura Tobler, Johanna Tönsing, Marco Tullney,  Milan Vasiljevic, Astrid Vieler, Lena Vinnemann, Viola Voß, Christin Wacke, Roland Wagner, Agnieszka Wenninger, Kerstin Werth, Martin Wimmer, Marco Winkler, Sabine Witt, Michael Wohlgemuth, Verena Wohlleben, Qingbo Xu, Philip Young, Esther Zaugg, Miriam Zeunert, Philipp Zumstein

## Contact

For bugs, feature requests and other issues, please submit an issue via [Github](https://github.com/OpenAPC/openapc-de/issues/new).

For general comments, email openapc at uni-bielefeld.de

## Disclaimer

People, who are looking for "Open Advanced Process Control Software" for automation, visualization and process control tasks from home control up to industrial automation, please follow <http://www.openapc.com> (2015-09-30)